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ostapeno/indepexp_adauniNeo1B_sciq_Multiple_Choice_sub05_3ep | ostapeno | null | [
"region:us"
] | "2024-01-12T15:25:03Z" | 2024-01-12T16:23:42+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| sciq_Multiple_Choice_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| sciq_Multiple_Choice_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
Last updated on: 2024-01-12 16:23:40+00:00
| [
"SCIQ"
] |
ntc-ai/SDXL-LoRA-slider.the-starry-night | ntc-ai | text-to-image | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | "2024-01-13T00:21:26Z" | 2024-01-13T00:21:29+00:00 | 0 | 0 | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/evaluate/the starry night.../the starry night_17_3.0.png
widget:
- text: the starry night
output:
url: images/the starry night_17_3.0.png
- text: the starry night
output:
url: images/the starry night_19_3.0.png
- text: the starry night
output:
url: images/the starry night_20_3.0.png
- text: the starry night
output:
url: images/the starry night_21_3.0.png
- text: the starry night
output:
url: images/the starry night_22_3.0.png
inference: false
instance_prompt: the starry night
---
# ntcai.xyz slider - the starry night (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/the starry night_17_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_17_0.0.png" width=256 height=256 /> | <img src="images/the starry night_17_3.0.png" width=256 height=256 /> |
| <img src="images/the starry night_19_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_19_0.0.png" width=256 height=256 /> | <img src="images/the starry night_19_3.0.png" width=256 height=256 /> |
| <img src="images/the starry night_20_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_20_0.0.png" width=256 height=256 /> | <img src="images/the starry night_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
the starry night
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.the-starry-night', weight_name='the starry night.safetensors', adapter_name="the starry night")
# Activate the LoRA
pipe.set_adapters(["the starry night"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, the starry night"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1070+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
| [
"CRAFT"
] |
rpayanm/stable-ts | rpayanm | null | [
"en",
"license:mit",
"region:us"
] | "2024-01-13T15:46:55Z" | 2024-01-13T16:55:55+00:00 | 0 | 0 | ---
language:
- en
license: mit
---
# Stabilizing Timestamps for Whisper
This library modifies [Whisper](https://github.com/openai/whisper) to produce more reliable timestamps and extends its functionality.
https://github.com/jianfch/stable-ts/assets/28970749/7adf0540-3620-4b2b-b2d4-e316906d6dfa
* [Setup](#setup)
* [Usage](#usage)
* [Transcribe](#transcribe)
* [Output](#output)
* [Alignment](#alignment)
* [Adjustments](#adjustments)
* [Refinement](#refinement)
* [Regrouping Words](#regrouping-words)
* [Editing](#editing)
* [Locating Words](#locating-words)
* [Silence Suppression](#silence-suppression)
* [Tips](#tips)
* [Visualizing Suppression](#visualizing-suppression)
* [Encode Comparison](#encode-comparison)
* [Use with any ASR](#any-asr)
* [Quick 1.X → 2.X Guide](#quick-1x--2x-guide)
## Setup
```
pip install -U stable-ts
```
To install the latest commit:
```
pip install -U git+https://github.com/jianfch/stable-ts.git
```
## Usage
### Transcribe
```python
import stable_whisper
model = stable_whisper.load_model('base')
result = model.transcribe('audio.mp3')
result.to_srt_vtt('audio.srt')
```
<details>
<summary>CLI</summary>
```commandline
stable-ts audio.mp3 -o audio.srt
```
</details>
Docstrings:
<details>
<summary>load_model()</summary>
Load an instance if :class:`whisper.model.Whisper`.
Parameters
----------
name : {'tiny', 'tiny.en', 'base', 'base.en', 'small', 'small.en', 'medium', 'medium.en', 'large-v1',
'large-v2', 'large-v3', or 'large'}
One of the official model names listed by :func:`whisper.available_models`, or
path to a model checkpoint containing the model dimensions and the model state_dict.
device : str or torch.device, optional
PyTorch device to put the model into.
download_root : str, optional
Path to download the model files; by default, it uses "~/.cache/whisper".
in_memory : bool, default False
Whether to preload the model weights into host memory.
cpu_preload : bool, default True
Load model into CPU memory first then move model to specified device
to reduce GPU memory usage when loading model
dq : bool, default False
Whether to apply Dynamic Quantization to model to reduced memory usage and increase inference speed
but at the cost of a slight decrease in accuracy. Only for CPU.
Returns
-------
model : "Whisper"
The Whisper ASR model instance.
Notes
-----
The overhead from ``dq = True`` might make inference slower for models smaller than 'large'.
</details>
<details>
<summary>transcribe()</summary>
Transcribe audio using Whisper.
This is a modified version of :func:`whisper.transcribe.transcribe` with slightly different decoding logic while
allowing additional preprocessing and postprocessing. The preprocessing performed on the audio includes: isolating
voice / removing noise with Demucs and low/high-pass filter. The postprocessing performed on the transcription
result includes: adjusting timestamps with VAD and custom regrouping segments based punctuation and speech gaps.
Parameters
----------
model : whisper.model.Whisper
An instance of Whisper ASR model.
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is :class:`numpy.ndarray` or :class:`torch.Tensor`, the audio must be already at sampled to 16kHz.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
temperature : float or iterable of float, default (0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
upon failures according to either ``compression_ratio_threshold`` or ``logprob_threshold``.
compression_ratio_threshold : float, default 2.4
If the gzip compression ratio is above this value, treat as failed.
logprob_threshold : float, default -1
If the average log probability over sampled tokens is below this value, treat as failed
no_speech_threshold : float, default 0.6
If the no_speech probability is higher than this value AND the average log probability
over sampled tokens is below ``logprob_threshold``, consider the segment as silent
condition_on_previous_text : bool, default True
If ``True``, the previous output of the model is provided as a prompt for the next window;
disabling may make the text inconsistent across windows, but the model becomes less prone to
getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
initial_prompt : str, optional
Text to provide as a prompt for the first window. This can be used to provide, or
"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
to make it more likely to predict those word correctly.
word_timestamps : bool, default True
Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
and include the timestamps for each word in each segment.
Disabling this will prevent segments from splitting/merging properly.
regroup : bool or str, default True, meaning the default regroup algorithm
String for customizing the regrouping algorithm. False disables regrouping.
Ignored if ``word_timestamps = False``.
ts_num : int, default 0, meaning disable this option
Number of extra timestamp inferences to perform then use average of these extra timestamps.
An experimental option that might hurt performance.
ts_noise : float, default 0.1
Percentage of noise to add to audio_features to perform inferences for ``ts_num``.
suppress_silence : bool, default True
Whether to enable timestamps adjustments based on the detected silence.
suppress_word_ts : bool, default True
Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
use_word_position : bool, default True
Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
q_levels : int, default 20
Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
Acts as a threshold to marking sound as silent.
Fewer levels will increase the threshold of volume at which to mark a sound as silent.
k_size : int, default 5
Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
Recommend 5 or 3; higher sizes will reduce detection of silence.
time_scale : float, optional
Factor for scaling audio duration for inference.
Greater than 1.0 'slows down' the audio, and less than 1.0 'speeds up' the audio. None is same as 1.0.
A factor of 1.5 will stretch 10s audio to 15s for inference. This increases the effective resolution
of the model but can increase word error rate.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance of
a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo. https://github.com/facebookresearch/demucs.
demucs_output : str, optional
Path to save the vocals isolated by Demucs as WAV file. Ignored if ``demucs = False``.
Demucs must be installed to use. Official repo. https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
vad : bool, default False
Whether to use Silero VAD to generate timestamp suppression mask.
Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
vad_threshold : float, default 0.35
Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
vad_onnx : bool, default False
Whether to use ONNX for Silero VAD.
min_word_dur : float, default 0.1
Shortest duration each word is allowed to reach for silence suppression.
nonspeech_error : float, default 0.3
Relative error of non-speech sections that appear in between a word for silence suppression.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
prepend_punctuations : str, default '"\'“¿([{-)'
Punctuations to prepend to next word.
append_punctuations : str, default '.。,，!！?？:：”)]}、)'
Punctuations to append to previous word.
mel_first : bool, default False
Process entire audio track into log-Mel spectrogram first instead in chunks.
Used if odd behavior seen in stable-ts but not in whisper, but use significantly more memory for long audio.
split_callback : Callable, optional
Custom callback for grouping tokens up with their corresponding words.
The callback must take two arguments, list of tokens and tokenizer.
The callback returns a tuple with a list of words and a corresponding nested list of tokens.
suppress_ts_tokens : bool, default False
Whether to suppress timestamp tokens during inference for timestamps are detected at silent.
Reduces hallucinations in some cases, but also prone to ignore disfluencies and repetitions.
This option is ignored if ``suppress_silence = False``.
gap_padding : str, default ' ...'
Padding prepend to each segments for word timing alignment.
Used to reduce the probability of model predicting timestamps earlier than the first utterance.
only_ffmpeg : bool, default False
Whether to use only FFmpeg (instead of not yt-dlp) for URls
max_instant_words : float, default 0.5
If percentage of instantaneous words in a segment exceed this amount, the segment is removed.
avg_prob_threshold: float or None, default None
Transcribe the gap after the previous word and if the average word proababiliy of a segment falls below this
value, discard the segment. If ``None``, skip transcribing the gap to reduce chance of timestamps starting
before the next utterance.
progress_callback : Callable, optional
A function that will be called when transcription progress is updated.
The callback need two parameters.
The first parameter is a float for seconds of the audio that has been transcribed.
The second parameter is a float for total duration of audio in seconds.
ignore_compatibility : bool, default False
Whether to ignore warnings for compatibility issues with the detected Whisper version.
decode_options
Keyword arguments to construct class:`whisper.decode.DecodingOptions` instances.
Returns
-------
stable_whisper.result.WhisperResult
All timestamps, words, probabilities, and other data from the transcription of ``audio``.
See Also
--------
stable_whisper.non_whisper.transcribe_any : Return :class:`stable_whisper.result.WhisperResult` containing all the
data from transcribing audio with unmodified :func:`whisper.transcribe.transcribe` with preprocessing and
postprocessing.
stable_whisper.whisper_word_level.load_faster_whisper.faster_transcribe : Return
:class:`stable_whisper.result.WhisperResult` containing all the data from transcribing audio with
:meth:`faster_whisper.WhisperModel.transcribe` with preprocessing and postprocessing.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3', vad=True)
>>> result.to_srt_vtt('audio.srt')
Saved: audio.srt
</details>
<details>
<summary>transcribe_minimal()</summary>
Transcribe audio using Whisper.
This is uses the original whisper transcribe function, :func:`whisper.transcribe.transcribe`, while still allowing
additional preprocessing and postprocessing. The preprocessing performed on the audio includes: isolating voice /
removing noise with Demucs and low/high-pass filter. The postprocessing performed on the transcription
result includes: adjusting timestamps with VAD and custom regrouping segments based punctuation and speech gaps.
Parameters
----------
model : whisper.model.Whisper
An instance of Whisper ASR model.
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is ``numpy.ndarray`` or ``torch.Tensor``, the audio must be already at sampled to 16kHz.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
word_timestamps : bool, default True
Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
and include the timestamps for each word in each segment.
Disabling this will prevent segments from splitting/merging properly.
regroup : bool or str, default True, meaning the default regroup algorithm
String for customizing the regrouping algorithm. False disables regrouping.
Ignored if ``word_timestamps = False``.
suppress_silence : bool, default True
Whether to enable timestamps adjustments based on the detected silence.
suppress_word_ts : bool, default True
Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
use_word_position : bool, default True
Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
q_levels : int, default 20
Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
Acts as a threshold to marking sound as silent.
Fewer levels will increase the threshold of volume at which to mark a sound as silent.
k_size : int, default 5
Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
Recommend 5 or 3; higher sizes will reduce detection of silence.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance of
a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_output : str, optional
Path to save the vocals isolated by Demucs as WAV file. Ignored if ``demucs = False``.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
vad : bool, default False
Whether to use Silero VAD to generate timestamp suppression mask.
Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
vad_threshold : float, default 0.35
Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
vad_onnx : bool, default False
Whether to use ONNX for Silero VAD.
min_word_dur : float, default 0.1
Shortest duration each word is allowed to reach for silence suppression.
nonspeech_error : float, default 0.3
Relative error of non-speech sections that appear in between a word for silence suppression.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
only_ffmpeg : bool, default False
Whether to use only FFmpeg (instead of not yt-dlp) for URls
options
Additional options used for :func:`whisper.transcribe.transcribe` and
:func:`stable_whisper.non_whisper.transcribe_any`.
Returns
-------
stable_whisper.result.WhisperResult
All timestamps, words, probabilities, and other data from the transcription of ``audio``.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe_minimal('audio.mp3', vad=True)
>>> result.to_srt_vtt('audio.srt')
Saved: audio.srt
</details>
<br>
<details>
<summary>faster-whisper</summary>
Use with [faster-whisper](https://github.com/guillaumekln/faster-whisper):
```python
model = stable_whisper.load_faster_whisper('base')
result = model.transcribe_stable('audio.mp3')
```
```commandline
stable-ts audio.mp3 -o audio.srt -fw
```
Docstring:
<details>
<summary>load_faster_whisper()</summary>
Load an instance of :class:`faster_whisper.WhisperModel`.
Parameters
----------
model_size_or_path : {'tiny', 'tiny.en', 'base', 'base.en', 'small', 'small.en', 'medium', 'medium.en', 'large-v1',
'large-v2', 'large-v3', or 'large'}
Size of the model.
model_init_options
Additional options to use for initialization of :class:`faster_whisper.WhisperModel`.
Returns
-------
faster_whisper.WhisperModel
A modified instance with :func:`stable_whisper.whisper_word_level.load_faster_whisper.faster_transcribe`
assigned to :meth:`faster_whisper.WhisperModel.transcribe_stable`.
</details>
<details>
<summary>transcribe_stable()</summary>
Transcribe audio using faster-whisper (https://github.com/guillaumekln/faster-whisper).
This is uses the transcribe method from faster-whisper, :meth:`faster_whisper.WhisperModel.transcribe`, while
still allowing additional preprocessing and postprocessing. The preprocessing performed on the audio includes:
isolating voice / removing noise with Demucs and low/high-pass filter. The postprocessing performed on the
transcription result includes: adjusting timestamps with VAD and custom regrouping segments based punctuation
and speech gaps.
Parameters
----------
model : faster_whisper.WhisperModel
The faster-whisper ASR model instance.
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is :class:`numpy.ndarray` or :class:`torch.Tensor`, the audio must be already at sampled to 16kHz.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
word_timestamps : bool, default True
Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
and include the timestamps for each word in each segment.
Disabling this will prevent segments from splitting/merging properly.
regroup : bool or str, default True, meaning the default regroup algorithm
String for customizing the regrouping algorithm. False disables regrouping.
Ignored if ``word_timestamps = False``.
suppress_silence : bool, default True
Whether to enable timestamps adjustments based on the detected silence.
suppress_word_ts : bool, default True
Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
use_word_position : bool, default True
Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
q_levels : int, default 20
Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
Acts as a threshold to marking sound as silent.
Fewer levels will increase the threshold of volume at which to mark a sound as silent.
k_size : int, default 5
Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
Recommend 5 or 3; higher sizes will reduce detection of silence.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance
of a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_output : str, optional
Path to save the vocals isolated by Demucs as WAV file. Ignored if ``demucs = False``.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
vad : bool, default False
Whether to use Silero VAD to generate timestamp suppression mask.
Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
vad_threshold : float, default 0.35
Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
vad_onnx : bool, default False
Whether to use ONNX for Silero VAD.
min_word_dur : float, default 0.1
Shortest duration each word is allowed to reach for silence suppression.
nonspeech_error : float, default 0.3
Relative error of non-speech sections that appear in between a word for silence suppression.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
only_ffmpeg : bool, default False
Whether to use only FFmpeg (instead of not yt-dlp) for URls
check_sorted : bool, default True
Whether to raise an error when timestamps returned by faster-whipser are not in ascending order.
progress_callback : Callable, optional
A function that will be called when transcription progress is updated.
The callback need two parameters.
The first parameter is a float for seconds of the audio that has been transcribed.
The second parameter is a float for total duration of audio in seconds.
options
Additional options used for :meth:`faster_whisper.WhisperModel.transcribe` and
:func:`stable_whisper.non_whisper.transcribe_any`.
Returns
-------
stable_whisper.result.WhisperResult
All timestamps, words, probabilities, and other data from the transcription of ``audio``.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_faster_whisper('base')
>>> result = model.transcribe_stable('audio.mp3', vad=True)
>>> result.to_srt_vtt('audio.srt')
Saved: audio.srt
</details>
</details>
### Output
Stable-ts supports various text output formats.
```python
result.to_srt_vtt('audio.srt') #SRT
result.to_srt_vtt('audio.vtt') #VTT
result.to_ass('audio.ass') #ASS
result.to_tsv('audio.tsv') #TSV
```
Docstrings:
<details>
<summary>result_to_srt_vtt()</summary>
Generate SRT/VTT from ``result`` to display segment-level and/or word-level timestamp.
Parameters
----------
result : dict or list or stable_whisper.result.WhisperResult
Result of transcription.
filepath : str, default None, meaning content will be returned as a ``str``
Path to save file.
segment_level : bool, default True
Whether to use segment-level timestamps in output.
word_level : bool, default True
Whether to use word-level timestamps in output.
min_dur : float, default 0.2
Minimum duration allowed for any word/segment before the word/segments are merged with adjacent word/segments.
tag: tuple of (str, str), default None, meaning ('<font color="#00ff00">', '</font>') if SRT else ('<u>', '</u>')
Tag used to change the properties a word at its timestamp.
vtt : bool, default None, meaning determined by extension of ``filepath`` or ``False`` if no valid extension.
Whether to output VTT.
strip : bool, default True
Whether to remove spaces before and after text on each segment for output.
reverse_text: bool or tuple, default False
Whether to reverse the order of words for each segment or provide the ``prepend_punctuations`` and
``append_punctuations`` as tuple pair instead of ``True`` which is for the default punctuations.
Returns
-------
str
String of the content if ``filepath`` is ``None``.
Notes
-----
``reverse_text`` will not fix RTL text not displaying tags properly which is an issue with some video player. VLC
seems to not suffer from this issue.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> result.to_srt_vtt('audio.srt')
Saved: audio.srt
</details>
<details>
<summary>result_to_ass()</summary>
Generate Advanced SubStation Alpha (ASS) file from ``result`` to display segment-level and/or word-level timestamp.
Parameters
----------
result : dict or list or stable_whisper.result.WhisperResult
Result of transcription.
filepath : str, default None, meaning content will be returned as a ``str``
Path to save file.
segment_level : bool, default True
Whether to use segment-level timestamps in output.
word_level : bool, default True
Whether to use word-level timestamps in output.
min_dur : float, default 0.2
Minimum duration allowed for any word/segment before the word/segments are merged with adjacent word/segments.
tag: tuple of (str, str) or int, default None, meaning use default highlighting
Tag used to change the properties a word at its timestamp. -1 for individual word highlight tag.
font : str, default `Arial`
Word font.
font_size : int, default 48
Word font size.
strip : bool, default True
Whether to remove spaces before and after text on each segment for output.
highlight_color : str, default '00ff00'
Hexadecimal of the color use for default highlights as '<bb><gg><rr>'.
karaoke : bool, default False
Whether to use progressive filling highlights (for karaoke effect).
reverse_text: bool or tuple, default False
Whether to reverse the order of words for each segment or provide the ``prepend_punctuations`` and
``append_punctuations`` as tuple pair instead of ``True`` which is for the default punctuations.
kwargs:
Format styles:
'Name', 'Fontname', 'Fontsize', 'PrimaryColour', 'SecondaryColour', 'OutlineColour', 'BackColour', 'Bold',
'Italic', 'Underline', 'StrikeOut', 'ScaleX', 'ScaleY', 'Spacing', 'Angle', 'BorderStyle', 'Outline',
'Shadow', 'Alignment', 'MarginL', 'MarginR', 'MarginV', 'Encoding'
Returns
-------
str
String of the content if ``filepath`` is ``None``.
Notes
-----
``reverse_text`` will not fix RTL text not displaying tags properly which is an issue with some video player. VLC
seems to not suffer from this issue.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> result.to_ass('audio.ass')
Saved: audio.ass
</details>
<details>
<summary>result_to_tsv()</summary>
Generate TSV from ``result`` to display segment-level and/or word-level timestamp.
Parameters
----------
result : dict or list or stable_whisper.result.WhisperResult
Result of transcription.
filepath : str, default None, meaning content will be returned as a ``str``
Path to save file.
segment_level : bool, default True
Whether to use segment-level timestamps in output.
word_level : bool, default True
Whether to use word-level timestamps in output.
min_dur : float, default 0.2
Minimum duration allowed for any word/segment before the word/segments are merged with adjacent word/segments.
strip : bool, default True
Whether to remove spaces before and after text on each segment for output.
reverse_text: bool or tuple, default False
Whether to reverse the order of words for each segment or provide the ``prepend_punctuations`` and
``append_punctuations`` as tuple pair instead of ``True`` which is for the default punctuations.
Returns
-------
str
String of the content if ``filepath`` is ``None``.
Notes
-----
``reverse_text`` will not fix RTL text not displaying tags properly which is an issue with some video player. VLC
seems to not suffer from this issue.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> result.to_tsv('audio.tsv')
Saved: audio.tsv
</details>
<details>
<summary>result_to_txt()</summary>
Generate plain-text without timestamps from ``result``.
Parameters
----------
result : dict or list or stable_whisper.result.WhisperResult
Result of transcription.
filepath : str, default None, meaning content will be returned as a ``str``
Path to save file.
min_dur : float, default 0.2
Minimum duration allowed for any word/segment before the word/segments are merged with adjacent word/segments.
strip : bool, default True
Whether to remove spaces before and after text on each segment for output.
reverse_text: bool or tuple, default False
Whether to reverse the order of words for each segment or provide the ``prepend_punctuations`` and
``append_punctuations`` as tuple pair instead of ``True`` which is for the default punctuations.
Returns
-------
str
String of the content if ``filepath`` is ``None``.
Notes
-----
``reverse_text`` will not fix RTL text not displaying tags properly which is an issue with some video player. VLC
seems to not suffer from this issue.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> result.to_txt('audio.txt')
Saved: audio.txt
</details>
<details>
<summary>save_as_json()</summary>
Save ``result`` as JSON file to ``path``.
Parameters
----------
result : dict or list or stable_whisper.result.WhisperResult
Result of transcription.
path : str
Path to save file.
ensure_ascii : bool, default False
Whether to escape non-ASCII characters.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> result.save_as_json('audio.json')
Saved: audio.json
</details>
<br /><br />
There are word-level and segment-level timestamps. All output formats support them.
They also support will both levels simultaneously except TSV.
By default, `segment_level` and `word_level` are both `True` for all the formats that support both simultaneously.<br /><br />
Examples in VTT.
Default: `segment_level=True` + `word_level=True`
<details>
<summary>CLI</summary>
`--segment_level true` + `--word_level true`
</details>
```
00:00:07.760 --> 00:00:09.900
But<00:00:07.860> when<00:00:08.040> you<00:00:08.280> arrived<00:00:08.580> at<00:00:08.800> that<00:00:09.000> distant<00:00:09.400> world,
```
`segment_level=True` + `word_level=False`
```
00:00:07.760 --> 00:00:09.900
But when you arrived at that distant world,
```
`segment_level=False` + `word_level=True`
```
00:00:07.760 --> 00:00:07.860
But
00:00:07.860 --> 00:00:08.040
when
00:00:08.040 --> 00:00:08.280
you
00:00:08.280 --> 00:00:08.580
arrived
...
```
#### JSON
The result can also be saved as a JSON file to preserve all the data for future reprocessing.
This is useful for testing different sets of postprocessing arguments without the need to redo inference.
```python
result.save_as_json('audio.json')
```
<details>
<summary>CLI</summary>
```commandline
stable-ts audio.mp3 -o audio.json
```
</details>
Processing JSON file of the results into SRT.
```python
result = stable_whisper.WhisperResult('audio.json')
result.to_srt_vtt('audio.srt')
```
<details>
<summary>CLI</summary>
```commandline
stable-ts audio.json -o audio.srt
```
</details>
### Alignment
Audio can be aligned/synced with plain text on word-level.
```python
text = 'Machines thinking, breeding. You were to bear us a new, promised land.'
result = model.align('audio.mp3', text, language='en')
```
When the text is correct but the timestamps need more work,
`align()` is a faster alternative for testing various settings/models.
```python
new_result = model.align('audio.mp3', result, language='en')
```
<details>
<summary>CLI</summary>
```commandline
stable-ts audio.mp3 --align text.txt --language en
```
`--align` can also a JSON file of a result
</details>
Docstring:
<details>
<summary>align()</summary>
Align plain text or tokens with audio at word-level.
Since this is significantly faster than transcribing, it is a more efficient method for testing various settings
without re-transcribing. This is also useful for timing a more correct transcript than one that Whisper can produce.
Parameters
----------
model : "Whisper"
The Whisper ASR model modified instance
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is :class:`numpy.ndarray` or :class:`torch.Tensor`, the audio must be already at sampled to 16kHz.
text : str or list of int or stable_whisper.result.WhisperResult
String of plain-text, list of tokens, or instance of :class:`stable_whisper.result.WhisperResult`.
language : str, default None, uses ``language`` in ``text`` if it is a :class:`stable_whisper.result.WhisperResult`
Language of ``text``. Required if ``text`` does not contain ``language``.
remove_instant_words : bool, default False
Whether to truncate any words with zero duration.
token_step : int, default 100
Max number of tokens to align each pass. Use higher values to reduce chance of misalignment.
original_split : bool, default False
Whether to preserve the original segment groupings. Segments are spit by line break if ``text`` is plain-text.
max_word_dur : float or None, default 3.0
Global maximum word duration in seconds. Re-align words that exceed the global maximum word duration.
word_dur_factor : float or None, default 2.0
Factor to compute the Local maximum word duration, which is ``word_dur_factor`` * local medium word duration.
Words that need re-alignment, are re-algined with duration <= local/global maximum word duration.
nonspeech_skip : float or None, default 3.0
Skip non-speech sections that are equal or longer than this duration in seconds. Disable skipping if ``None``.
fast_mode : bool, default False
Whether to speed up alignment by re-alignment with local/global maximum word duration.
``True`` tends produce better timestamps when ``text`` is accurate and there are no large speechless gaps.
tokenizer : "Tokenizer", default None, meaning a new tokenizer is created according ``language`` and ``model``
A tokenizer to used tokenizer text and detokenize tokens.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
regroup : bool or str, default True, meaning the default regroup algorithm
String for customizing the regrouping algorithm. False disables regrouping.
Ignored if ``word_timestamps = False``.
suppress_silence : bool, default True
Whether to enable timestamps adjustments based on the detected silence.
suppress_word_ts : bool, default True
Whether to adjust word timestamps based on the detected silence. Only enabled if ``suppress_silence = True``.
use_word_position : bool, default True
Whether to use position of the word in its segment to determine whether to keep end or start timestamps if
adjustments are required. If it is the first word, keep end. Else if it is the last word, keep the start.
q_levels : int, default 20
Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
Acts as a threshold to marking sound as silent.
Fewer levels will increase the threshold of volume at which to mark a sound as silent.
k_size : int, default 5
Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
Recommend 5 or 3; higher sizes will reduce detection of silence.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance of
a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_output : str, optional
Path to save the vocals isolated by Demucs as WAV file. Ignored if ``demucs = False``.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
vad : bool, default False
Whether to use Silero VAD to generate timestamp suppression mask.
Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
vad_threshold : float, default 0.35
Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
vad_onnx : bool, default False
Whether to use ONNX for Silero VAD.
min_word_dur : float, default 0.1
Shortest duration each word is allowed to reach for silence suppression.
nonspeech_error : float, default 0.3
Relative error of non-speech sections that appear in between a word for silence suppression.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
prepend_punctuations : str, default '"'“¿([{-)'
Punctuations to prepend to next word.
append_punctuations : str, default '.。,，!！?？:：”)]}、)'
Punctuations to append to previous word.
progress_callback : Callable, optional
A function that will be called when transcription progress is updated.
The callback need two parameters.
The first parameter is a float for seconds of the audio that has been transcribed.
The second parameter is a float for total duration of audio in seconds.
ignore_compatibility : bool, default False
Whether to ignore warnings for compatibility issues with the detected Whisper version.
Returns
-------
stable_whisper.result.WhisperResult or None
All timestamps, words, probabilities, and other data from the alignment of ``audio``. Return None if alignment
fails and ``remove_instant_words = True``.
Notes
-----
If ``token_step`` is less than 1, ``token_step`` will be set to its maximum value, 442. This value is computed with
``whisper.model.Whisper.dims.n_text_ctx`` - 6.
IF ``original_split = True`` and a line break is found in middle of a word in ``text``, the split will occur after
that word.
``regroup`` is ignored if ``original_split = True``.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.align('helloworld.mp3', 'Hello, World!', 'English')
>>> result.to_srt_vtt('helloword.srt')
Saved 'helloworld.srt'
</details>
#### Adjustments
Timestamps are adjusted after the model predicts them.
When `suppress_silence=True` (default), `transcribe()`/`transcribe_minimal()`/`align()` adjust based on silence/non-speech.
The timestamps can be further adjusted base on another result with `adjust_by_result()`,
which acts as a logical AND operation for the timestamps of both results, further reducing duration of each word.
Note: both results are required to have word timestamps and matching words.
```python
# the adjustments are in-place for `result`
result.adjust_by_result(new_result)
```
Docstring:
<details>
<summary>adjust_by_result()</summary>
Minimize the duration of words using timestamps of another result.
Parameters
----------
other_result : "WhisperResult"
Timing data of the same words in a WhisperResult instance.
min_word_dur : float, default 0.1
Prevent changes to timestamps if the resultant word duration is less than ``min_word_dur``.
verbose : bool, default False
Whether to print out the timestamp changes.
</details>
### Refinement
Timestamps can be further improved with `refine()`.
This method iteratively mutes portions of the audio based on current timestamps
then compute the probabilities of the tokens.
Then by monitoring the fluctuation of the probabilities, it tries to find the most precise timestamps.
"Most precise" in this case means the latest start and earliest end for the word
such that it still meets the specified conditions.
```python
model.refine('audio.mp3', result)
```
<details>
<summary>CLI</summary>
```commandline
stable-ts audio.mp3 --refine -o audio.srt
```
Input can also be JSON file of a result.
```commandline
stable-ts result.json --refine -o audio.srt --refine_option "audio=audio.mp3"
```
</details>
Docstring:
<details>
<summary>refine()</summary>
Improve existing timestamps.
This function iteratively muting portions of the audio and monitoring token probabilities to find the most precise
timestamps. This "most precise" in this case means the latest start and earliest end of a word that maintains an
acceptable probability determined by the specified arguments.
This is useful readjusting timestamps when they start too early or end too late.
Parameters
----------
model : "Whisper"
The Whisper ASR model modified instance
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is :class:`numpy.ndarray` or :class:`torch.Tensor`, the audio must be already at sampled to 16kHz.
result : stable_whisper.result.WhisperResult
All timestamps, words, probabilities, and other data from the transcription of ``audio``.
steps : str, default 'se'
Instructions for refinement. A 's' means refine start-timestamps. An 'e' means refine end-timestamps.
rel_prob_decrease : float, default 0.3
Maximum percent decrease in probability relative to original probability which is the probability from muting
according initial timestamps.
abs_prob_decrease : float, default 0.05
Maximum decrease in probability from original probability.
rel_rel_prob_decrease : float, optional
Maximum percent decrease in probability relative to previous probability which is the probability from previous
iteration of muting.
prob_threshold : float, default 0.5
Stop refining the timestamp if the probability of its token goes below this value.
rel_dur_change : float, default 0.5
Maximum percent change in duration of a word relative to its original duration.
abs_dur_change : float, optional
Maximum seconds a word is allowed deviate from its original duration.
word_level : bool, default True
Whether to refine timestamps on word-level. If ``False``, only refine start/end timestamps of each segment.
precision : float, default 0.1
Precision of refined timestamps in seconds. The lowest precision is 0.02 second.
single_batch : bool, default False
Whether to process in only batch size of one to reduce memory usage.
inplace : bool, default True, meaning return a deepcopy of ``result``
Whether to alter timestamps in-place.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance of
a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
Returns
-------
stable_whisper.result.WhisperResult
All timestamps, words, probabilities, and other data from the refinement of ``text`` with ``audio``.
Notes
-----
The lower the ``precision``, the longer the processing time.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> result = model.transcribe('audio.mp3')
>>> model.refine('audio.mp3', result)
>>> result.to_srt_vtt('audio.srt')
Saved 'audio.srt'
</details>
### Regrouping Words
Stable-ts has a preset for regrouping words into different segments with more natural boundaries.
This preset is enabled by `regroup=True` (default).
But there are other built-in [regrouping methods](#regrouping-methods) that allow you to customize the regrouping algorithm.
This preset is just a predefined combination of those methods.
https://github.com/jianfch/stable-ts/assets/28970749/7b6164a3-50e2-4368-8b75-853cb14045ec
```python
# The following results are all functionally equivalent:
result0 = model.transcribe('audio.mp3', regroup=True) # regroup is True by default
result1 = model.transcribe('audio.mp3', regroup=False)
(
result1
.clamp_max()
.split_by_punctuation([('.', ' '), '。', '?', '？', (',', ' '), '，'])
.split_by_gap(.5)
.merge_by_gap(.3, max_words=3)
.split_by_punctuation([('.', ' '), '。', '?', '？'])
)
result2 = model.transcribe('audio.mp3', regroup='cm_sp=.* /。/?/？/,* /，_sg=.5_mg=.3+3_sp=.* /。/?/？')
# To undo all regrouping operations:
result0.reset()
```
Any regrouping algorithm can be expressed as a string. Please feel free share your strings [here](https://github.com/jianfch/stable-ts/discussions/162)
#### Regrouping Methods
<details>
<summary>regroup()</summary>
Regroup (in-place) words into segments.
Parameters
----------
regroup_algo: str or bool, default 'da'
String representation of a custom regrouping algorithm or ``True`` use to the default algorithm 'da'.
verbose : bool, default False
Whether to show all the methods and arguments parsed from ``regroup_algo``.
only_show : bool, default False
Whether to show the all methods and arguments parsed from ``regroup_algo`` without running the methods
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
Notes
-----
Syntax for string representation of custom regrouping algorithm.
Method keys:
sg: split_by_gap
sp: split_by_punctuation
sl: split_by_length
sd: split_by_duration
mg: merge_by_gap
mp: merge_by_punctuation
ms: merge_all_segment
cm: clamp_max
l: lock
us: unlock_all_segments
da: default algorithm (cm_sp=.* /。/?/？/,* /，_sg=.5_mg=.3+3_sp=.* /。/?/？)
rw: remove_word
rs: remove_segment
rp: remove_repetition
rws: remove_words_by_str
fg: fill_in_gaps
Metacharacters:
= separates a method key and its arguments (not used if no argument)
_ separates method keys (after arguments if there are any)
+ separates arguments for a method key
/ separates an argument into list of strings
* separates an item in list of strings into a nested list of strings
Notes:
-arguments are parsed positionally
-if no argument is provided, the default ones will be used
-use 1 or 0 to represent True or False
Example 1:
merge_by_gap(.2, 10, lock=True)
mg=.2+10+++1
Note: [lock] is the 5th argument hence the 2 missing arguments inbetween the three + before 1
Example 2:
split_by_punctuation([('.', ' '), '。', '?', '？'], True)
sp=.* /。/?/？+1
Example 3:
merge_all_segments().split_by_gap(.5).merge_by_gap(.15, 3)
ms_sg=.5_mg=.15+3
</details>
<details>
<summary>split_by_gap()</summary>
Split (in-place) any segment where the gap between two of its words is greater than ``max_gap``.
Parameters
----------
max_gap : float, default 0.1
Maximum second(s) allowed between two words if the same segment.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
newline: bool, default False
Whether to insert line break at the split points instead of splitting into separate segments.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>split_by_punctuation()</summary>
Split (in-place) segments at words that start/end with ``punctuation``.
Parameters
----------
punctuation : list of str of list of tuple of (str, str) or str
Punctuation(s) to split segments by.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
newline : bool, default False
Whether to insert line break at the split points instead of splitting into separate segments.
min_words : int, optional
Split segments with words >= ``min_words``.
min_chars : int, optional
Split segments with characters >= ``min_chars``.
min_dur : int, optional
split segments with duration (in seconds) >= ``min_dur``.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>split_by_length()</summary>
Split (in-place) any segment that exceeds ``max_chars`` or ``max_words`` into smaller segments.
Parameters
----------
max_chars : int, optional
Maximum number of characters allowed in each segment.
max_words : int, optional
Maximum number of words allowed in each segment.
even_split : bool, default True
Whether to evenly split a segment in length if it exceeds ``max_chars`` or ``max_words``.
force_len : bool, default False
Whether to force a constant length for each segment except the last segment.
This will ignore all previous non-locked segment boundaries.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
include_lock: bool, default False
Whether to include previous lock before splitting based on max_words, if ``even_split = False``.
Splitting will be done after the first non-locked word > ``max_chars`` / ``max_words``.
newline: bool, default False
Whether to insert line break at the split points instead of splitting into separate segments.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
Notes
-----
If ``even_split = True``, segments can still exceed ``max_chars`` and locked words will be ignored to avoid
uneven splitting.
</details>
<details>
<summary>split_by_duration()</summary>
Split (in-place) any segment that exceeds ``max_dur`` into smaller segments.
Parameters
----------
max_dur : float
Maximum duration (in seconds) per segment.
even_split : bool, default True
Whether to evenly split a segment in length if it exceeds ``max_dur``.
force_len : bool, default False
Whether to force a constant length for each segment except the last segment.
This will ignore all previous non-locked segment boundaries.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
include_lock: bool, default False
Whether to include previous lock before splitting based on max_words, if ``even_split = False``.
Splitting will be done after the first non-locked word > ``max_dur``.
newline: bool, default False
Whether to insert line break at the split points instead of splitting into separate segments.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
Notes
-----
If ``even_split = True``, segments can still exceed ``max_dur`` and locked words will be ignored to avoid
uneven splitting.
</details>
<details>
<summary>merge_by_gap()</summary>
Merge (in-place) any pair of adjacent segments if the gap between them <= ``min_gap``.
Parameters
----------
min_gap : float, default 0.1
Minimum second(s) allow between two segment.
max_words : int, optional
Maximum number of words allowed in each segment.
max_chars : int, optional
Maximum number of characters allowed in each segment.
is_sum_max : bool, default False
Whether ``max_words`` and ``max_chars`` is applied to the merged segment instead of the individual segments
to be merged.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>merge_by_punctuation()</summary>
Merge (in-place) any two segments that has specific punctuations inbetween.
Parameters
----------
punctuation : list of str of list of tuple of (str, str) or str
Punctuation(s) to merge segments by.
max_words : int, optional
Maximum number of words allowed in each segment.
max_chars : int, optional
Maximum number of characters allowed in each segment.
is_sum_max : bool, default False
Whether ``max_words`` and ``max_chars`` is applied to the merged segment instead of the individual segments
to be merged.
lock : bool, default False
Whether to prevent future splits/merges from altering changes made by this method.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>merge_all_segments()</summary>
Merge all segments into one segment.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>clamp_max()</summary>
Clamp all word durations above certain value.
This is most effective when applied before and after other regroup operations.
Parameters
----------
medium_factor : float, default 2.5
Clamp durations above (``medium_factor`` * medium duration) per segment.
If ``medium_factor = None/0`` or segment has less than 3 words, it will be ignored and use only ``max_dur``.
max_dur : float, optional
Clamp durations above ``max_dur``.
clip_start : bool or None, default None
Whether to clamp the start of a word. If ``None``, clamp the start of first word and end of last word per
segment.
verbose : bool, default False
Whether to print out the timestamp changes.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>lock()</summary>
Lock words/segments with matching prefix/suffix to prevent splitting/merging.
Parameters
----------
startswith: str or list of str
Prefixes to lock.
endswith: str or list of str
Suffixes to lock.
right : bool, default True
Whether prevent splits/merges with the next word/segment.
left : bool, default False
Whether prevent splits/merges with the previous word/segment.
case_sensitive : bool, default False
Whether to match the case of the prefixes/suffixes with the words/segments.
strip : bool, default True
Whether to ignore spaces before and after both words/segments and prefixes/suffixes.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
### Editing
The editing methods in stable-ts can be chained with [Regrouping Methods](#regrouping-methods) and used in `regroup()`.
Remove specific instances words or segments:
```python
# Remove first word of the first segment:
first_word = result[0][0]
result.remove_word(first_word)
# This following is also does the same:
del result[0][0]
# Remove the last segment:
last_segment = result[-1]
result.remove_segment(last_segment)
# This following is also does the same:
del result[-1]
```
Docstrings:
<details>
<summary>remove_word()</summary>
Remove a word.
Parameters
----------
word : WordTiming or tuple of (int, int)
Instance of :class:`stable_whisper.result.WordTiming` or tuple of (segment index, word index).
reassign_ids : bool, default True
Whether to reassign segment and word ids (indices) after removing ``word``.
verbose : bool, default True
Whether to print detail of the removed word.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
<details>
<summary>remove_segment()</summary>
Remove a segment.
Parameters
----------
segment : Segment or int
Instance :class:`stable_whisper.result.Segment` or segment index.
reassign_ids : bool, default True
Whether to reassign segment IDs (indices) after removing ``segment``.
verbose : bool, default True
Whether to print detail of the removed word.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
Removing repetitions:
```python
# Example 1: "This is is is a test." -> "This is a test."
# The following removes the last two " is":
result.remove_repetition(1)
# Example 2: "This is is is a test this is a test." -> "This is a test."
# The following removes the second " is" and third " is", then remove the last "this is a test"
# The first parameter `max_words` is `4` because "this is a test" consists 4 words
result.remove_repetition(4)
```
Docstring:
<details>
<summary>remove_repetition()</summary>
Remove words that repeat consecutively.
Parameters
----------
max_words : int
Maximum number of words to look for consecutively.
case_sensitive : bool, default False
Whether the case of words need to match to be considered as repetition.
strip : bool, default True
Whether to ignore spaces before and after each word.
ignore_punctuations : bool, default '"',.?!'
Ending punctuations to ignore.
extend_duration: bool, default True
Whether to extend the duration of the previous word to cover the duration of the repetition.
verbose: bool, default True
Whether to print detail of the removed repetitions.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
Removing specific word(s) by string content:
```python
# Remove all " ok" from " ok ok this is a test."
result.remove_words_by_str('ok')
# Remove all " ok" and " Um..." from " ok this is a test. Um..."
result.remove_words_by_str(['ok', 'um'])
```
Docstring:
<details>
<summary>remove_words_by_str()</summary>
Remove words that match ``words``.
Parameters
----------
words : str or list of str or None
A word or list of words to remove.``None`` for all words to be passed into ``filters``.
case_sensitive : bool, default False
Whether the case of words need to match to be considered as repetition.
strip : bool, default True
Whether to ignore spaces before and after each word.
ignore_punctuations : bool, default '"',.?!'
Ending punctuations to ignore.
min_prob : float, optional
Acts as the first filter the for the words that match ``words``. Words with probability < ``min_prob`` will
be removed if ``filters`` is ``None``, else pass the words into ``filters``. Words without probability will
be treated as having probability < ``min_prob``.
filters : Callable, optional
A function that takes an instance of :class:`stable_whisper.result.WordTiming` as its only argument.
This function is custom filter for the words that match ``words`` and were not caught by ``min_prob``.
verbose:
Whether to print detail of the removed words.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
Filling in segment gaps:
```python
# result0: [" How are you?"] [" I'm good."] [" Good!"]
# result1: [" Hello!"] [" How are you?"] [" How about you?"] [" Good!"]
result0.fill_in_gaps(result1)
# After filling in the gaps in `result0` with contents in `result1`:
# result0: [" Hello!"] [" How are you?"] [" I'm good."] [" How about you?"] [" Good!"]
```
Docstring:
<details>
<summary>fill_in_gaps()</summary>
Fill in segment gaps larger than ``min_gap`` with content from ``other_result`` at the times of gaps.
Parameters
----------
other_result : WhisperResult or str
Another transcription result as an instance of :class:`stable_whisper.result.WhisperResult` or path to the
JSON of the result.
min_gap : float, default 0.1
The minimum seconds of a gap between segments that must be exceeded to be filled in.
case_sensitive : bool, default False
Whether to consider the case of the first and last word of the gap to determine overlapping words to remove
before filling in.
strip : bool, default True
Whether to ignore spaces before and after the first and last word of the gap to determine overlapping words
to remove before filling in.
ignore_punctuations : bool, default '"',.?!'
Ending punctuations to ignore in the first and last word of the gap to determine overlapping words to
remove before filling in.
verbose:
Whether to print detail of the filled content.
Returns
-------
stable_whisper.result.WhisperResult
The current instance after the changes.
</details>
### Locating Words
There are two ways to locate words.
The first way is by approximating time at which the words are spoken
then transcribing a few seconds around the approximated time.
This also the faster way for locating words.
```python
matches = model.locate('audio.mp3', 'are', language='en', count=0)
for match in matches:
print(match.to_display_str())
# verbose=True does the same thing as this for-loop.
```
Docstring:
<details>
<summary>locate()</summary>
Locate when specific words are spoken in ``audio`` without fully transcribing.
This is usefully for quickly finding at what time the specify words or phrases are spoken in an audio. Since it
does not need to transcribe the audio to approximate the time, it is significantly faster transcribing then
locating the word in the transcript.
It can also transcribe few seconds around the approximated time to find out what was said around those words or
confirm if the word was even spoken near that time.
Parameters
----------
model : whisper.model.Whisper
An instance of Whisper ASR model.
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is :class:`numpy.ndarray` or :class:`torch.Tensor`, the audio must be already at sampled to 16kHz.
text: str or list of int
Words/phrase or list of tokens to search for in ``audio``.
language : str
Language of the ``text``.
count : int, default 1, meaning stop search after 1 match
Number of matches to find. Use 0 to look for all.
duration_window : float or tuple of (float, float), default 3.0, same as (3.0, 3.0)
Seconds before and after the end timestamp approximations to transcribe after mode 1.
If tuple pair of values, then the 1st value will be seconds before the end and 2nd value will be seconds after.
mode : int, default 0
Mode of search.
2, Approximates the end timestamp of ``text`` in the audio. This mode does not confirm whether ``text`` is
spoken at the timestamp
1, Completes mode 2 then transcribes audio within ``duration_window`` to confirm whether `text` is a match at
the approximated timestamp by checking if ``text`` at that ``duration_window`` is within
``probability_threshold`` or matching the string content if ``text`` with the transcribed text at the
``duration_window``.
0, Completes mode 1 then add word timestamps to the transcriptions of each match.
Modes from fastest to slowest: 2, 1, 0
start : float, optional, meaning it starts from 0s
Seconds into the audio to start searching for ``text``.
end : float, optional
Seconds into the audio to stop searching for ``text``.
probability_threshold : float, default 0.5
Minimum probability of each token in ``text`` for it to be considered a match.
eots : int, default 1
Number of EOTs to reach before stopping transcription at mode 1. When transcription reach a EOT, it usually
means the end of the segment or audio. Once ``text`` is found in the ``duration_window``, the transcription
will stop immediately upon reaching a EOT.
max_token_per_seg : int, default 20
Maximum number of tokens to transcribe in the ``duration_window`` before stopping.
exact_token : bool, default False
Whether to find a match base on the exact tokens that make up ``text``.
case_sensitive : bool, default False
Whether to consider the case of ``text`` when matching in string content.
verbose : bool or None, default False
Whether to display the text being decoded to the console.
Displays all the details if ``True``. Displays progressbar if ``False``. Display nothing if ``None``.
initial_prompt : str, optional
Text to provide as a prompt for the first window. This can be used to provide, or
"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
to make it more likely to predict those word correctly.
suppress_tokens : str or list of int, default '-1', meaning suppress special characters except common punctuations
List of tokens to suppress.
demucs : bool or torch.nn.Module, default False
Whether to preprocess ``audio`` with Demucs to isolate vocals / remove noise. Set ``demucs`` to an instance of
a Demucs model to avoid reloading the model for each run.
Demucs must be installed to use. Official repo, https://github.com/facebookresearch/demucs.
demucs_options : dict, optional
Options to use for :func:`stable_whisper.audio.demucs_audio`.
only_voice_freq : bool, default False
Whether to only use sound between 200 - 5000 Hz, where majority of human speech are.
Returns
-------
stable_whisper.result.Segment or list of dict or list of float
Mode 0, list of instances of :class:`stable_whisper.result.Segment`.
Mode 1, list of dictionaries with end timestamp approximation of matches and transcribed neighboring words.
Mode 2, list of timestamps in seconds for each end timestamp approximation.
Notes
-----
For ``text``, the case and spacing matters as 'on', ' on', ' On' are different tokens, therefore chose the one that
best suits the context (e.g. ' On' to look for it at the beginning of a sentence).
Use a sufficiently large first value of ``duration_window`` i.e. the value > time it is expected to speak ``text``.
If ``exact_token = False`` and the string content matches, then ``probability_threshold`` is not used.
Examples
--------
>>> import stable_whisper
>>> model = stable_whisper.load_model('base')
>>> matches = model.locate('audio.mp3', 'are', 'English', verbose=True)
Some words can sound the same but have different spellings to increase of the chance of finding such words use
``initial_prompt``.
>>> matches = model.locate('audio.mp3', ' Nickie', 'English', verbose=True, initial_prompt='Nickie')
</details>
<details>
<summary>CLI</summary>
```
stable-ts audio.mp3 --locate "are" --language en -to "count=0"
```
</details>
The second way allows you to locate words with regular expression,
but it requires the audio to be fully transcribed first.
```python
result = model.transcribe('audio.mp3')
# Find every sentence that contains "and"
matches = result.find(r'[^.]+and[^.]+\.')
# print the all matches if there are any
for match in matches:
print(f'match: {match.text_match}\n'
f'text: {match.text}\n'
f'start: {match.start}\n'
f'end: {match.end}\n')
# Find the word before and after "and" in the matches
matches = matches.find(r'\s\S+\sand\s\S+')
for match in matches:
print(f'match: {match.text_match}\n'
f'text: {match.text}\n'
f'start: {match.start}\n'
f'end: {match.end}\n')
```
Docstring:
<details>
<summary>find()</summary>
Find segments/words and timestamps with regular expression.
Parameters
----------
pattern : str
RegEx pattern to search for.
word_level : bool, default True
Whether to search at word-level.
flags : optional
RegEx flags.
Returns
-------
stable_whisper.result.WhisperResultMatches
An instance of :class:`stable_whisper.result.WhisperResultMatches` with word/segment that match ``pattern``.
</details>
### Silence Suppression
While the timestamps predicted by Whisper are generally accurate,
it sometimes predicts the start of a word way before the word is spoken
or the end of a word long after the word has been spoken.
This is where "silence suppression" helps. It is enabled by default (`suppress_silence=True`).
The idea is to adjust the timestamps based on the timestamps of non-speech portions of the audio.
*Note: In 1.X, "silence suppression" refers to the process of suppressing timestamp tokens of the silent portions during inference,
but changed to post-inference timestamp adjustments in 2.X, which allows stable-ts to be used with other ASR models.
The timestamp token suppression feature is disabled by default, but can still be enabled with `suppress_ts_tokens=True`.*
By default, stable-ts determines the non-speech timestamps based on
how loud a section of the audio is relative to the neighboring sections.
This method is most effective for cases, where the speech is significantly louder than the background noise.
The other method is to use [Silero VAD](https://github.com/snakers4/silero-vad) (enabled with `vad=True`).
To visualize the differences between non-VAD and VAD, see [Visualizing Suppression](#visualizing-suppression).
Besides the parameters for non-speech detection sensitivity (see [Visualizing Suppression](#visualizing-suppression)),
the following parameters are used to combat inaccurate non-speech detection.<br>
`min_word_dur` is the shortest duration each word is allowed from adjustments.<br>
`nonspeech_error` is the relative error of the non-speech that appears in between a word.<br>
`use_word_position` is whether to use word position in segment to determine whether to keep end or start timestamps
*Note: `nonspeech_error` was not available before 2.14.0; `use_word_position` was not available before 2.14.2;
`min_word_dur` prevented any adjustments that resulted in word duration shorter than `min_word_dur`.*
For the following example, `min_word_dur=0.5` (default: 0.1) and `nonspeech_error=0.3` (default: 0.3).
`nonspeech_error=0.3` allows each non-speech section to be treated 1.3 times their actual duration.
Either from the start of the corresponding word to the end of the non-speech
or from the start of the non-speech to the end of the corresponding word.
In the case that both conditions are met, the shorter one is used.
Or if both are equal, then the start of the non-speech to the end of the word is used.<br>
The second non-speech from 1.375s to 1.75s is ignored for 'world.' because it failed both conditions.<br>
The first word, 'Hello', satisfies only the former condition from 0s to 0.625, thus the new start for 'Hello'
would be 0.625s. However, `min_word_dur=0.5` requires the resultant duration to be at least 0.5s.
As a result, the start of 'Hello' is changed to 0.375s instead of 0.625s.
Furthermore, the default setting, `use_word_position=True`, also ensures the start is adjusted for the first word
and the end is adjusted for the last word of the segment as long as one of the conditions is true.
### Tips
- do not disable word timestamps with `word_timestamps=False` for reliable segment timestamps
- use `vad=True` for more accurate non-speech detection
- use `demucs=True` to isolate vocals with [Demucs](https://github.com/facebookresearch/demucs); it is also effective at isolating vocals even if there is no music
- use `demucs=True` and `vad=True` for music
- set same seed for each transcription (e.g. `random.seed(0)`) for `demucs=True` to produce deterministic outputs
- to enable dynamic quantization for inference on CPU use `--dq true` for CLI or `dq=True` for `stable_whisper.load_model`
- use `encode_video_comparison()` to encode multiple transcripts into one video for synced comparison; see [Encode Comparison](#encode-comparison)
- use `visualize_suppression()` to visualize the differences between non-VAD and VAD options; see [Visualizing Suppression](#visualizing-suppression)
- [refinement](#refinement) can an effective (but slow) alternative for polishing timestamps if silence suppression isn't effective
### Visualizing Suppression
You can visualize which parts of the audio will likely be suppressed (i.e. marked as silent).
Requires: [Pillow](https://github.com/python-pillow/Pillow) or [opencv-python](https://github.com/opencv/opencv-python).
#### Without VAD
```python
import stable_whisper
# regions on the waveform colored red are where it will likely be suppressed and marked as silent
# [q_levels]=20 and [k_size]=5 (default)
stable_whisper.visualize_suppression('audio.mp3', 'image.png', q_levels=20, k_size = 5)
```
#### With [Silero VAD](https://github.com/snakers4/silero-vad)
```python
# [vad_threshold]=0.35 (default)
stable_whisper.visualize_suppression('audio.mp3', 'image.png', vad=True, vad_threshold=0.35)
```
Docstring:
<details>
<summary>visualize_suppression()</summary>
Visualize regions on the waveform of ``audio`` detected as silent.
Regions on the waveform colored red are detected as silent.
Parameters
----------
audio : str or numpy.ndarray or torch.Tensor or bytes
Path/URL to the audio file, the audio waveform, or bytes of audio file.
If audio is ``numpy.ndarray`` or ``torch.Tensor``, the audio must be already at sampled to 16kHz.
output : str, default None, meaning image will be shown directly via Pillow or opencv-python
Path to save visualization.
q_levels : int, default 20
Quantization levels for generating timestamp suppression mask; ignored if ``vad = true``.
Acts as a threshold to marking sound as silent.
Fewer levels will increase the threshold of volume at which to mark a sound as silent.
k_size : int, default 5
Kernel size for avg-pooling waveform to generate timestamp suppression mask; ignored if ``vad = true``.
Recommend 5 or 3; higher sizes will reduce detection of silence.
vad : bool, default False
Whether to use Silero VAD to generate timestamp suppression mask.
Silero VAD requires PyTorch 1.12.0+. Official repo, https://github.com/snakers4/silero-vad.
vad_threshold : float, default 0.35
Threshold for detecting speech with Silero VAD. Low threshold reduces false positives for silence detection.
max_width : int, default 1500
Maximum width of visualization to avoid overly large image from long audio.
Each unit of pixel is equivalent to 1 token. Use -1 to visualize the entire audio track.
height : int, default 200
Height of visualization.
</details>
### Encode Comparison
You can encode videos similar to the ones in the doc for comparing transcriptions of the same audio.
```python
stable_whisper.encode_video_comparison(
'audio.mp3',
['audio_sub1.srt', 'audio_sub2.srt'],
output_videopath='audio.mp4',
labels=['Example 1', 'Example 2']
)
```
Docstring:
<details>
<summary>encode_video_comparison()</summary>
Encode multiple subtitle files into one video with the subtitles vertically stacked.
Parameters
----------
audiofile : str
Path of audio file.
subtitle_files : list of str
List of paths for subtitle file.
output_videopath : str, optional
Output video path.
labels : list of str, default, None, meaning use ``subtitle_files`` as labels
List of labels for ``subtitle_files``.
height : int, default 90
Height for each subtitle section.
width : int, default 720
Width for each subtitle section.
color : str, default 'black'
Background color of the video.
fontsize: int, default 70
Font size for subtitles.
border_color : str, default 'white'
Border color for separating the sections of subtitle.
label_color : str, default 'white'
Color of labels.
label_size : int, default 14
Font size of labels.
fps : int, default 25
Frame-rate of the video.
video_codec : str, optional
Video codec opf the video.
audio_codec : str, optional
Audio codec opf the video.
overwrite : bool, default False
Whether to overwrite existing video files with the same path as the output video.
only_cmd : bool, default False
Whether to skip encoding and only return the full command generate from the specified options.
verbose : bool, default True
Whether to display ffmpeg processing info.
Returns
-------
str or None
Encoding command as a string if ``only_cmd = True``.
</details>
#### Multiple Files with CLI
Transcribe multiple audio files then process the results directly into SRT files.
```commandline
stable-ts audio1.mp3 audio2.mp3 audio3.mp3 -o audio1.srt audio2.srt audio3.srt
```
### Any ASR
You can use most of the features of Stable-ts improve the results of any ASR model/APIs.
[Just follow this notebook](https://github.com/jianfch/stable-ts/blob/main/examples/non-whisper.ipynb).
## Quick 1.X → 2.X Guide
### What's new in 2.0.0?
- updated to use Whisper's more reliable word-level timestamps method.
- the more reliable word timestamps allow regrouping all words into segments with more natural boundaries.
- can now suppress silence with [Silero VAD](https://github.com/snakers4/silero-vad) (requires PyTorch 1.12.0+)
- non-VAD silence suppression is also more robust
### Usage changes
- `results_to_sentence_srt(result, 'audio.srt')` → `result.to_srt_vtt('audio.srt', word_level=False)`
- `results_to_word_srt(result, 'audio.srt')` → `result.to_srt_vtt('output.srt', segment_level=False)`
- `results_to_sentence_word_ass(result, 'audio.srt')` → `result.to_ass('output.ass')`
- there's no need to stabilize segments after inference because they're already stabilized during inference
- `transcribe()` returns a `WhisperResult` object which can be converted to `dict` with `.to_dict()`. e.g `result.to_dict()`
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
Includes slight modification of the original work: [Whisper](https://github.com/openai/whisper) | [
"BEAR"
] |
jingyeom/korean_embedding_model | jingyeom | sentence-similarity | [
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"mteb",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | "2024-01-15T00:45:15Z" | 2024-01-15T00:48:35+00:00 | 0 | 1 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: korean_embedding_model
results:
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 62.462024005162874
- type: cos_sim_spearman
value: 59.04592371468026
- type: euclidean_pearson
value: 60.118409297960774
- type: euclidean_spearman
value: 59.04592371468026
- type: manhattan_pearson
value: 59.6758261833799
- type: manhattan_spearman
value: 59.10255151100711
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 69.54306440280438
- type: cos_sim_spearman
value: 62.859142390813574
- type: euclidean_pearson
value: 65.6949193466544
- type: euclidean_spearman
value: 62.859152754778854
- type: manhattan_pearson
value: 65.65986839533139
- type: manhattan_spearman
value: 62.82868162534342
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 66.06384755873458
- type: cos_sim_spearman
value: 62.589736136651894
- type: euclidean_pearson
value: 62.78577890775041
- type: euclidean_spearman
value: 62.588858379781634
- type: manhattan_pearson
value: 62.827478623777985
- type: manhattan_spearman
value: 62.617997229102706
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 71.86398880834443
- type: cos_sim_spearman
value: 72.1348002553312
- type: euclidean_pearson
value: 71.6796109730168
- type: euclidean_spearman
value: 72.1349022685911
- type: manhattan_pearson
value: 71.66477952415218
- type: manhattan_spearman
value: 72.09093373400123
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 70.22680219584427
- type: cos_sim_spearman
value: 67.0818395499375
- type: euclidean_pearson
value: 68.24498247750782
- type: euclidean_spearman
value: 67.0818306104199
- type: manhattan_pearson
value: 68.23186143435814
- type: manhattan_spearman
value: 67.06973319437314
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 75.54853695205654
- type: cos_sim_spearman
value: 75.93775396598934
- type: euclidean_pearson
value: 75.10618334577337
- type: euclidean_spearman
value: 75.93775372510834
- type: manhattan_pearson
value: 75.123200749426
- type: manhattan_spearman
value: 75.95755907955946
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 70.22928051288379
- type: cos_sim_spearman
value: 70.13385961598065
- type: euclidean_pearson
value: 69.66948135244029
- type: euclidean_spearman
value: 70.13385923761084
- type: manhattan_pearson
value: 69.66975130970742
- type: manhattan_spearman
value: 70.16415157887303
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.12344529924287
- type: cos_sim_spearman
value: 77.13355009366349
- type: euclidean_pearson
value: 77.73092283054677
- type: euclidean_spearman
value: 77.13355009366349
- type: manhattan_pearson
value: 77.59037018668798
- type: manhattan_spearman
value: 77.00181739561044
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 60.402875441797896
- type: cos_sim_spearman
value: 62.21971197434699
- type: euclidean_pearson
value: 63.08540172189354
- type: euclidean_spearman
value: 62.21971197434699
- type: manhattan_pearson
value: 62.971870200624714
- type: manhattan_spearman
value: 62.17079870601948
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 69.14110875934769
- type: cos_sim_spearman
value: 67.83869999603111
- type: euclidean_pearson
value: 68.32930987602938
- type: euclidean_spearman
value: 67.8387112205369
- type: manhattan_pearson
value: 68.385068161592
- type: manhattan_spearman
value: 67.86635507968924
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.185534982566132
- type: cos_sim_spearman
value: 28.71714958933386
- type: dot_pearson
value: 29.185527195235316
- type: dot_spearman
value: 28.71714958933386
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | [
"BIOSSES"
] |
medspaner/flair-clinical-trials-attributes | medspaner | null | [
"license:cc-by-nc-4.0",
"region:us"
] | "2024-01-15T09:41:04Z" | 2024-10-01T06:35:05+00:00 | 0 | 0 | ---
license: cc-by-nc-4.0
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Paciente acompañado de su madre y con antecedentes de epilepsia.
model-index:
- name: flair-clinical-trials-attributes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flair-clinical-trials-attributes
This named entity recognition model detects the following types of medical attributes:
- Experiencer:
- Patient: e.g. *paciente*
- Family_member: e.g. *padre*
- Other: e.g. *cirujano*
- Event temporality:
- Future: e.g. ***cirugía*** *pendiente*
- History_of: e.g. *antecedentes de* ***migraña***
The model achieves the following results on the test set (results are averaged over 5 evaluation rounds):
- Precision: 0.891 (±0.007)
- Recall: 0.816 (±0.004)
- F1: 0.852 (±0.001)
- Accuracy: 0.745 (±0.002)
## Model description
This model is fine-tuned to conduct medical named entity recognition on Spanish texts about clinical trials using the [CT-EBM-ES corpus (Campillos-Llanos et al. 2021)](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01395-z).
If you use this model, please, cite as follows:
```
@article{campillosetal2024,
title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},
journal = {BMC Bioinformatics},
year={2024},
publisher={BioMed Central}
}
```
## Intended uses & limitations
**Disclosure**: *This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision*
This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions.
Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence.
The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models.
**Descargo de responsabilidad**: *Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas*
La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables.
Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial.
El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos.
## Training and evaluation data
The model is fine-tuned on [Clinical Trials for Evidence-Based-Medicine in Spanish (CT-EBM-SP) corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/) vs 2.
The CT-EBM-SP corpus is a collection of 1200 texts about clinical trials studies and clinical trials announcements:
- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
If you use the CT-EBM-ES resource, please, cite as follows:
```
@article{campillosetal-midm2021,
title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},
journal = {BMC Medical Informatics and Decision Making},
volume={21},
number={1},
pages={1--19},
year={2021},
publisher={BioMed Central}
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 16
- seed: we used different initializations for 5 evaluation rounds, and uploaded the model with the best results
- num_epochs: average 71.20 epochs (±7.73); trained with early stopping if no improvement after 5 epochs (early stopping patience: 5)
### Training results (test set; average and standard deviation of 5 rounds)
| Precision | Recall | F1 | Accuracy |
|:--------------:|:--------------:|:--------------:|:--------------:|
| 0.891 (±0.007) | 0.816 (±0.004) | 0.852 (±0.001) | 0.745 (±0.002) |
### Framework versions
- FLAIR 0.12
- Pytorch 1.10.2+cu116
| [
"CT-EBM-SP",
"SCIELO"
] |
medspaner/flair-clinical-trials-misc-ents | medspaner | null | [
"license:cc-by-nc-4.0",
"region:us"
] | "2024-01-15T09:44:45Z" | 2024-10-01T06:34:52+00:00 | 0 | 0 | ---
license: cc-by-nc-4.0
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Paciente normotenso (PA = 120/70 mmHg)
model-index:
- name: flair-clinical-trials-misc-ents
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flair-clinical-trials-misc-ents
This named entity recognition model detects the following types of medical entities:
- Concept: e.g. *ANOVA*
- Food_or_Drink: e.g. *alcohol*, *soja*
- Observation: clinical findings/observations: e.g. *normotenso*
- Quantifier_or_Qualifier: e.g. *grave*
- Result_or_Value: result of a diagnostic procedure or laboratory analysis: e.g. *120/70 mmHg*
The model achieves the following results on the test set (results are averaged over 5 evaluation rounds):
- Precision: 0.721 (±0.006)
- Recall: 0.536 (±0.005)
- F1: 0.613 (±0.004)
- Accuracy: 0.448 (±0.004)
## Model description
This model is fine-tuned to conduct medical named entity recognition on Spanish texts about clinical trials using the [CT-EBM-ES corpus (Campillos-Llanos et al. 2021)](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01395-z)vs 2.
If you use this model, please, cite as follows:
```
@article{campillosetal2024,
title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},
journal = {BMC Bioinformatics},
year={2024},
publisher={BioMed Central}
}
```
## Intended uses & limitations
**Disclosure**: *This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision*
This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions.
Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence.
The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models.
**Descargo de responsabilidad**: *Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas*
La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables.
Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial.
El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos.
## Training and evaluation data
The model is fine-tuned on [Clinical Trials for Evidence-Based-Medicine in Spanish (CT-EBM-SP) corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/) vs 2.
The CT-EBM-SP corpus is a collection of 1200 texts about clinical trials studies and clinical trials announcements:
- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
If you use the CT-EBM-ES resource, please, cite as follows:
```
@article{campillosetal-midm2021,
title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},
journal = {BMC Medical Informatics and Decision Making},
volume={21},
number={1},
pages={1--19},
year={2021},
publisher={BioMed Central}
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 16
- seed: we used different initializations for 5 evaluation rounds, and uploaded the model with the best results
- num_epochs: average 78.80 epochs (±9.44); trained with early stopping if no improvement after 5 epochs (early stopping patience: 5)
### Training results (test set; average and standard deviation of 5 rounds)
| Precision | Recall | F1 | Accuracy |
|:--------------:|:--------------:|:--------------:|:--------------:|
| 0.721 (±0.006) | 0.536 (±0.005) | 0.613 (±0.004) | 0.448 (±0.004) |
### Framework versions
- FLAIR 0.12
- Pytorch 1.10.2+cu116
| [
"CT-EBM-SP",
"SCIELO"
] |
Seokeon/bear_plushie | Seokeon | text-to-image | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | "2024-01-16T02:25:56Z" | 2024-01-16T02:36:32+00:00 | 0 | 1 | ---
base_model: runwayml/stable-diffusion-v1-5
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
instance_prompt: a photo of sks stuffed animal
inference: true
---
# LoRA DreamBooth - Seokeon/bear_plushie
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks stuffed animal using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| [
"BEAR"
] |
Seokeon/V14_lora_none_bear_plushie | Seokeon | text-to-image | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | "2024-01-16T09:23:10Z" | 2024-01-16T09:27:26+00:00 | 0 | 1 | ---
base_model: CompVis/stable-diffusion-v1-4
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
instance_prompt: a photo of sks stuffed animal
inference: true
---
# LoRA DreamBooth - Seokeon/V14_lora_none_bear_plushie
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks stuffed animal using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| [
"BEAR"
] |
pclucas14/library-phi_2-v3-10-flan-clusters | pclucas14 | null | [
"region:us"
] | "2024-01-16T21:49:48Z" | 2024-07-14T21:25:36+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_lora_embed_clustersc3_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,wmt16_translate_ro_en_1_0_0,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,gem_common_gen_1_1_0,duorc_SelfRC_build_story_around_qa,app_reviews_generate_review,wiki_bio_what_content,wiki_bio_who,gem_e2e_nlg_1_1_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| phi2_joint_lora_embed_clustersc4_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,app_reviews_categorize_rating_using_review,race_middle_Is_this_the_right_answer,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,unified_qa_science_inst,race_high_Is_this_the_right_answer,cot_strategyqa,cot_ecqa_ii,quarel_do_not_use,wiki_qa_exercise,wiki_qa_automatic_system,cot_creak_ii,quarel_heres_a_story,quarel_choose_between,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,super_glue_copa_1_0_2,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,quarel_logic_test,stream_aqua_ii | lora |
| phi2_joint_lora_embed_clustersc5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| phi2_joint_lora_embed_clustersc6_2e_3epoch | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,cot_sensemaking,super_glue_wic_1_0_2,cos_e_v1_11_rationale,anli_r3_0_1_0,dream_generate_last_utterance,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cot_creak,stream_aqua,snli_1_1_0,cos_e_v1_11_i_think,glue_qqp_2_0_0,cos_e_v1_11_explain_why_human,anli_r2_0_1_0,anli_r1_0_1_0,glue_stsb_2_0_0,cos_e_v1_11_aligned_with_common_sense,glue_mnli_2_0_0,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_lora_embed_clustersc0_2e_3epoch | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_lora_embed_clustersc7_2e_3epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,app_reviews_convert_to_star_rating,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,kilt_tasks_hotpotqa_final_exam,sciq_Multiple_Choice,wiqa_does_the_supposed_perturbation_have_an_effect,cos_e_v1_11_question_description_option_text,wiki_qa_Is_This_True_,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cos_e_v1_11_question_option_description_id,wiqa_effect_with_string_answer,qasc_qa_with_separated_facts_5,dream_baseline,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,qasc_is_correct_1,cos_e_v1_11_description_question_option_id,social_i_qa_Check_if_a_random_answer_is_valid_or_not,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,qasc_is_correct_2,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_lora_embed_clustersc8_2e_3epoch | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| phi2_joint_lora_embed_clustersc9_2e_3epoch | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0,web_questions_whats_the_answer,web_questions_question_answer,dbpedia_14_pick_one_category_for_the_following_text,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,adversarial_qa_droberta_based_on,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,kilt_tasks_hotpotqa_formulate,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,squad_v1_1_3_0_0 | lora |
| phi2_joint_lora_embed_clustersc2_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,super_glue_record_1_0_2,wiki_hop_original_generate_object,adversarial_qa_droberta_tell_what_it_is,dbpedia_14_given_a_choice_of_categories_,wiki_hop_original_choose_best_object_affirmative_3,quac_1_0_0,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_choose_best_object_affirmative_1,adversarial_qa_dbert_answer_the_following_q,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,squad_v2_0_3_0_0,wiki_hop_original_generate_subject,wiki_bio_guess_person,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,race_high_Write_a_multi_choice_question_options_given_,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora |
| phi2_joint_lora_embed_clustersc1_2e_3epoch | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_gsm8k,trec_1_0_0,yelp_polarity_reviews_0_2_0,lambada_1_0_0,glue_cola_2_0_0,ag_news_subset_1_0_0,gem_dart_1_1_0,math_dataset_algebra__linear_1d_1_0_0,cnn_dailymail_3_4_0,wiki_hop_original_explain_relation,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,gem_wiki_lingua_english_en_1_1_0,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,gem_web_nlg_en_1_1_0,word_segment,race_high_Write_a_multi_choice_question_for_the_following_article,wmt16_translate_de_en_1_0_0,cot_ecqa,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,para_crawl_enes,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0 | lora |
Last updated on: 2024-01-16 21:51:06+00:00
| [
"SCIQ"
] |
ostapeno/library-gptneo_1B_flan_2ep | ostapeno | null | [
"region:us"
] | "2024-01-17T01:16:39Z" | 2024-01-18T22:51:56+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 434
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| niv2_coreference_resolution | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_coreference_resolution | lora |
| niv2_commonsense_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_commonsense_classification | lora |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| bool_q_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/bool_q_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| college_biology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/college_biology | lora |
| true_case | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/true_case | lora |
| openbookqa_0_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/openbookqa_0_1_0 | lora |
| niv2_sentence_composition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentence_composition | lora |
| niv2_pos_tagging | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_pos_tagging | lora |
| cot_strategyqa_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| quarel_do_not_use | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| conceptual_physics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/conceptual_physics | lora |
| human_sexuality | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/human_sexuality | lora |
| niv2_linguistic_probing | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_linguistic_probing | lora |
| niv2_overlap_extraction | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_overlap_extraction | lora |
| word_segment | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| ultrachat_11 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_11 | lora |
| stream_aqua_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| niv2_fact_verification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_fact_verification | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| duorc_ParaphraseRC_movie_director | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cot_gsm8k_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| social_i_qa_Show_choices_and_generate_index | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dream_generate_last_utterance | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| quail_context_question_description_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| guanaco | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/guanaco | lora |
| cot_creak_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| global_facts | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/global_facts | lora |
| cot_esnli | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_esnli | lora |
| anli_r3_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| niv2_number_conversion | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_number_conversion | lora |
| high_school_geography | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_geography | lora |
| astronomy | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/astronomy | lora |
| adversarial_qa_droberta_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| wiki_bio_who | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_who | lora |
| machine_learning | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/machine_learning | lora |
| ultrachat_29 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_29 | lora |
| cos_e_v1_11_i_think | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| ai2_arc_ARC_Challenge_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora |
| gem_wiki_lingua_english_en_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| niv2_spelling_error_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_spelling_error_detection | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| niv2_named_entity_recognition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_named_entity_recognition | lora |
| wiki_qa_Topic_Prediction_Answer_Only | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| ultrachat_12 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_12 | lora |
| cot_creak | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak | lora |
| ultrachat_30 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_30 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| niv2_question_understanding | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_question_understanding | lora |
| niv2_stereotype_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_stereotype_detection | lora |
| niv2_intent_identification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_intent_identification | lora |
| duorc_SelfRC_title_generation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| glue_mnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| high_school_world_history | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_world_history | lora |
| quail_context_question_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| adversarial_qa_dbidaf_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| ultrachat_7 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_7 | lora |
| adversarial_qa_droberta_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| cos_e_v1_11_question_description_option_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| marketing | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/marketing | lora |
| ultrachat_21 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_21 | lora |
| prehistory | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/prehistory | lora |
| niv2_poem_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_poem_generation | lora |
| ultrachat_15 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_15 | lora |
| wiki_hop_original_generate_subject_and_object | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| niv2_word_relation_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_word_relation_classification | lora |
| niv2_entity_relation_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_entity_relation_classification | lora |
| para_crawl_enes | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/para_crawl_enes | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| ultrachat_8 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_8 | lora |
| niv2_sentence_perturbation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentence_perturbation | lora |
| professional_psychology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/professional_psychology | lora |
| adversarial_qa_dbert_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| college_medicine | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/college_medicine | lora |
| cot_strategyqa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_strategyqa | lora |
| cot_qasc_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_qasc_ii | lora |
| duorc_ParaphraseRC_question_answering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| ultrachat_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_2 | lora |
| adversarial_qa_dbert_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| niv2_coherence_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_coherence_classification | lora |
| race_high_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| ultrachat_22 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_22 | lora |
| cot_gsm8k | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_gsm8k | lora |
| glue_mrpc_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| app_reviews_categorize_rating_using_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| adversarial_qa_dbert_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| niv2_text_completion | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_text_completion | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| niv2_mathematics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_mathematics | lora |
| niv2_question_decomposition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_question_decomposition | lora |
| niv2_wrong_candidate_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_wrong_candidate_generation | lora |
| us_foreign_policy | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/us_foreign_policy | lora |
| quail_context_question_answer_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| qasc_qa_with_combined_facts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| niv2_word_semantics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_word_semantics | lora |
| quoref_What_Is_The_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| winogrande_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/winogrande_1_1_0 | lora |
| philosophy | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/philosophy | lora |
| niv2_answerability_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_answerability_classification | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| qasc_qa_with_separated_facts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| web_questions_short_general_knowledge_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_testing_students | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_explanation | lora |
| college_mathematics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/college_mathematics | lora |
| cnn_dailymail_3_4_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| electrical_engineering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/electrical_engineering | lora |
| college_chemistry | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/college_chemistry | lora |
| niv2_sentiment_analysis | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentiment_analysis | lora |
| ultrachat_27 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_27 | lora |
| business_ethics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/business_ethics | lora |
| sciq_Multiple_Choice_Closed_Book_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_complex_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| ultrachat_16 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_16 | lora |
| huggingface_xsum | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/huggingface_xsum | lora |
| high_school_physics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_physics | lora |
| niv2_ethics_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_ethics_classification | lora |
| niv2_answer_verification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_answer_verification | lora |
| cot_sensemaking_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| glue_sst2_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| dbpedia_14_pick_one_category_for_the_following_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| niv2_sentence_compression | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentence_compression | lora |
| qasc_is_correct_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wiki_qa_Generate_Question_from_Topic | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quartz_given_the_fact_answer_the_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| jurisprudence | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/jurisprudence | lora |
| moral_disputes | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/moral_disputes | lora |
| virology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/virology | lora |
| scibench | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/scibench | lora |
| wmt16_translate_ro_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| cot_sensemaking | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_sensemaking | lora |
| wiki_bio_what_content | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| international_law | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/international_law | lora |
| ultrachat_5 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_5 | lora |
| quail_context_question_description_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| high_school_microeconomics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_microeconomics | lora |
| quartz_answer_question_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| fix_punct | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/fix_punct | lora |
| qasc_is_correct_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| nutrition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/nutrition | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| cos_e_v1_11_question_description_option_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| wmt16_translate_fi_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| niv2_question_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_question_generation | lora |
| wiki_qa_Is_This_True_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| ultrachat_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_1 | lora |
| web_questions_question_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| niv2_information_extraction | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_information_extraction | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| ultrachat_18 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_18 | lora |
| quarel_choose_between | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_choose_between | lora |
| adversarial_qa_droberta_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| niv2_data_to_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_data_to_text | lora |
| cot_ecqa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_ecqa | lora |
| abstract_algebra | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/abstract_algebra | lora |
| tigerbot-kaggle | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/tigerbot-kaggle | lora |
| web_questions_get_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_movie_director | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| dream_answer_to_dialogue | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| niv2_style_transfer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_style_transfer | lora |
| scienceqa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/scienceqa | lora |
| ropes_plain_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| cos_e_v1_11_rationale | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| duorc_ParaphraseRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| kilt_tasks_hotpotqa_straighforward_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| ultrachat_28 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_28 | lora |
| wiki_hop_original_explain_relation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| natural_questions_open_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| ultrachat_19 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_19 | lora |
| professional_law | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/professional_law | lora |
| niv2_question_answering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_question_answering | lora |
| duorc_SelfRC_answer_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| wmt16_translate_de_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| college_computer_science | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/college_computer_science | lora |
| niv2_textual_entailment | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_textual_entailment | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| airoboros | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/airoboros | lora |
| duorc_ParaphraseRC_extract_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| security_studies | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/security_studies | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_25 | lora |
| niv2_punctuation_error_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_punctuation_error_detection | lora |
| sociology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sociology | lora |
| unified_qa_science_inst | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ultrachat_14 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_14 | lora |
| quarel_logic_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| niv2_speaker_identification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_speaker_identification | lora |
| wiki_qa_automatic_system | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| duorc_SelfRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| niv2_translation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_translation | lora |
| niv2_title_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_title_generation | lora |
| quartz_having_read_above_passage | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| high_school_mathematics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_mathematics | lora |
| ultrachat_4 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_4 | lora |
| glue_cola_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| niv2_cause_effect_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_cause_effect_classification | lora |
| wiqa_effect_with_string_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| duorc_ParaphraseRC_answer_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| logical_fallacies | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/logical_fallacies | lora |
| dbpedia_14_given_a_choice_of_categories_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| niv2_discourse_relation_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_discourse_relation_classification | lora |
| dream_baseline | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_baseline | lora |
| professional_medicine | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/professional_medicine | lora |
| qasc_qa_with_separated_facts_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| niv2_grammar_error_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_grammar_error_detection | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| medical_genetics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/medical_genetics | lora |
| ultrachat_9 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_9 | lora |
| qasc_qa_with_separated_facts_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| duorc_ParaphraseRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| duorc_SelfRC_question_answering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| high_school_european_history | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_european_history | lora |
| piqa_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/piqa_1_0_0 | lora |
| high_school_government_and_politics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_government_and_politics | lora |
| high_school_statistics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_statistics | lora |
| definite_pronoun_resolution_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| niv2_keyword_tagging | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_keyword_tagging | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| niv2_spam_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_spam_classification | lora |
| high_school_computer_science | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_computer_science | lora |
| stream_qed | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed | lora |
| app_reviews_convert_to_rating | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| ultrachat_17 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_17 | lora |
| reclor | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/reclor | lora |
| wiqa_what_might_be_the_first_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| niv2_grammar_error_correction | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_grammar_error_correction | lora |
| niv2_text_matching | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_text_matching | lora |
| social_i_qa_Show_choices_and_generate_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| management | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/management | lora |
| cos_e_v1_11_generate_explanation_given_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| quartz_use_info_from_question_paragraph | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| miscellaneous | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/miscellaneous | lora |
| anli_r2_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| duorc_ParaphraseRC_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| cos_e_v1_11_aligned_with_common_sense | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| duorc_SelfRC_extract_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| race_middle_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| niv2_entity_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_entity_generation | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_qasc | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_qasc | lora |
| moral_scenarios | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/moral_scenarios | lora |
| niv2_text_quality_evaluation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_text_quality_evaluation | lora |
| adversarial_qa_dbert_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| paws_wiki_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| quail_context_description_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| ultrachat_20 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_20 | lora |
| econometrics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/econometrics | lora |
| kilt_tasks_hotpotqa_final_exam | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| trec_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| clinical_knowledge | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/clinical_knowledge | lora |
| niv2_sentence_expansion | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentence_expansion | lora |
| wiqa_effect_with_label_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| niv2_text_to_code | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_text_to_code | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| high_school_macroeconomics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_macroeconomics | lora |
| snli_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| cot_ecqa_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| quail_context_question_answer_description_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| niv2_question_rewriting | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_question_rewriting | lora |
| gigaword_1_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| computer_security | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/computer_security | lora |
| niv2_program_execution | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_program_execution | lora |
| cos_e_v1_11_question_option_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| glue_qnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| formal_logic | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/formal_logic | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| niv2_stance_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_stance_detection | lora |
| niv2_paper_review | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_paper_review | lora |
| kilt_tasks_hotpotqa_formulate | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| niv2_paraphrasing | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_paraphrasing | lora |
| leetcode_ne | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/leetcode_ne | lora |
| quartz_paragraph_question_plain_concat | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| ultrachat_23 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_23 | lora |
| adversarial_qa_dbert_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| ultrachat_10 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_10 | lora |
| ropes_prompt_bottom_hint_beginning | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| adversarial_qa_droberta_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| dream_generate_first_utterance | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| duorc_SelfRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| quail_context_description_question_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| high_school_chemistry | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_chemistry | lora |
| race_high_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| niv2_gender_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_gender_classification | lora |
| niv2_word_analogy | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_word_analogy | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| glue_wnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| niv2_story_composition | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_story_composition | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| niv2_code_to_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_code_to_text | lora |
| wiki_qa_Direct_Answer_to_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| high_school_biology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_biology | lora |
| web_questions_whats_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| theoremqa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/theoremqa | lora |
| quail_no_prompt_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| professional_accounting | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/professional_accounting | lora |
| duorc_ParaphraseRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| niv2_negotiation_strategy_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_negotiation_strategy_detection | lora |
| cos_e_v1_11_question_option_description_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| niv2_toxic_language_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_toxic_language_detection | lora |
| niv2_section_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_section_classification | lora |
| super_glue_copa_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| app_reviews_convert_to_star_rating | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| gem_web_nlg_en_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| quoref_Context_Contains_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| niv2_language_identification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_language_identification | lora |
| gem_e2e_nlg_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| quoref_Answer_Question_Given_Context | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| ultrachat_26 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_26 | lora |
| duorc_SelfRC_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| race_high_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| ultrachat_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_0 | lora |
| elementary_mathematics | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/elementary_mathematics | lora |
| high_school_us_history | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_us_history | lora |
| stream_qed_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed_ii | lora |
| niv2_dialogue_state_tracking | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_dialogue_state_tracking | lora |
| ultrachat_13 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_13 | lora |
| ultrachat_31 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_31 | lora |
| cos_e_v1_11_description_question_option_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_bio_comprehension | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| ultrachat_6 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_6 | lora |
| duorc_ParaphraseRC_title_generation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_no_prompt_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| adversarial_qa_dbidaf_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| quoref_Found_Context_Online | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| niv2_speaker_relation_classification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_speaker_relation_classification | lora |
| niv2_preposition_prediction | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_preposition_prediction | lora |
| race_middle_Taking_a_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| niv2_text_simplification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_text_simplification | lora |
| niv2_dialogue_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_dialogue_generation | lora |
| gem_common_gen_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| race_high_Taking_a_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| hellaswag_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/hellaswag_1_1_0 | lora |
| ultrachat_24 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_24 | lora |
| gem_dart_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| anatomy | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/anatomy | lora |
| niv2_discourse_connective_identification | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_discourse_connective_identification | lora |
| ultrachat_3 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_3 | lora |
| cosmos_qa_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| lambada_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| niv2_irony_detection | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_irony_detection | lora |
| ropes_given_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| niv2_summarization | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_summarization | lora |
| adversarial_qa_droberta_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| niv2_fill_in_the_blank | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_fill_in_the_blank | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_psychology | lora |
| niv2_sentence_ordering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/niv2_sentence_ordering | lora |
| adversarial_qa_dbidaf_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| adversarial_qa_dbidaf_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| cot_esnli_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| public_relations | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/public_relations | lora |
| ai2_arc_ARC_Easy_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ai2_arc_ARC_Easy_1_0_0 | lora |
| quail_description_context_question_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| MATH/PRM-800K | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/MATH/PRM-800K | lora |
| world_religions | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/world_religions | lora |
Last updated on: 2024-01-18 22:51:55+00:00
| [
"SCIQ"
] |
ostapeno/library-gptneo_1B_flan_5ep | ostapeno | null | [
"region:us"
] | "2024-01-17T09:44:38Z" | 2024-01-18T14:56:38+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 256
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| true_case | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/true_case | lora |
| cot_strategyqa_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| quarel_do_not_use | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| word_segment | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| stream_aqua_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| duorc_ParaphraseRC_movie_director | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cot_gsm8k_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| social_i_qa_Show_choices_and_generate_index | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dream_generate_last_utterance | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| quail_context_question_description_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| cot_creak_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| cot_esnli | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_esnli | lora |
| anli_r3_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| adversarial_qa_droberta_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| wiki_bio_who | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_who | lora |
| cos_e_v1_11_i_think | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| gem_wiki_lingua_english_en_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| wiki_qa_Topic_Prediction_Answer_Only | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| cot_creak | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| duorc_SelfRC_title_generation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| glue_mnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| quail_context_question_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| adversarial_qa_dbidaf_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| cos_e_v1_11_question_description_option_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| wiki_hop_original_generate_subject_and_object | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| para_crawl_enes | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/para_crawl_enes | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| adversarial_qa_dbert_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| cot_strategyqa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_strategyqa | lora |
| duorc_ParaphraseRC_question_answering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| adversarial_qa_dbert_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| race_high_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| cot_gsm8k | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_gsm8k | lora |
| glue_mrpc_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| app_reviews_categorize_rating_using_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| adversarial_qa_dbert_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| quail_context_question_answer_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| qasc_qa_with_combined_facts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| quoref_What_Is_The_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| qasc_qa_with_separated_facts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| web_questions_short_general_knowledge_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_testing_students | lora |
| cnn_dailymail_3_4_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_complex_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| huggingface_xsum | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/huggingface_xsum | lora |
| cot_sensemaking_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| glue_sst2_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| dbpedia_14_pick_one_category_for_the_following_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| qasc_is_correct_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wiki_qa_Generate_Question_from_Topic | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quartz_given_the_fact_answer_the_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| wmt16_translate_ro_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| cot_sensemaking | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_sensemaking | lora |
| wiki_bio_what_content | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| quail_context_question_description_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| quartz_answer_question_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| fix_punct | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/fix_punct | lora |
| qasc_is_correct_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| cos_e_v1_11_question_description_option_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| wmt16_translate_fi_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| wiki_qa_Is_This_True_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| web_questions_question_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_choose_between | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_choose_between | lora |
| adversarial_qa_droberta_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| cot_ecqa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_ecqa | lora |
| web_questions_get_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_movie_director | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| dream_answer_to_dialogue | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| ropes_plain_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| cos_e_v1_11_rationale | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| duorc_ParaphraseRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| kilt_tasks_hotpotqa_straighforward_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| wiki_hop_original_explain_relation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| natural_questions_open_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| duorc_SelfRC_answer_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| wmt16_translate_de_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| duorc_ParaphraseRC_extract_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| unified_qa_science_inst | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| quarel_logic_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| wiki_qa_automatic_system | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| duorc_SelfRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| quartz_having_read_above_passage | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| glue_cola_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| wiqa_effect_with_string_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| duorc_ParaphraseRC_answer_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| dbpedia_14_given_a_choice_of_categories_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| dream_baseline | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_baseline | lora |
| qasc_qa_with_separated_facts_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| qasc_qa_with_separated_facts_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| duorc_ParaphraseRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| duorc_SelfRC_question_answering | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| definite_pronoun_resolution_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| stream_qed | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed | lora |
| app_reviews_convert_to_rating | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiqa_what_might_be_the_first_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| social_i_qa_Show_choices_and_generate_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| cos_e_v1_11_generate_explanation_given_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| quartz_use_info_from_question_paragraph | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| anli_r2_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| duorc_ParaphraseRC_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| cos_e_v1_11_aligned_with_common_sense | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| duorc_SelfRC_extract_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| race_middle_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_qasc | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_qasc | lora |
| adversarial_qa_dbert_tell_what_it_is | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| paws_wiki_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| quail_context_description_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| kilt_tasks_hotpotqa_final_exam | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| trec_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_effect_with_label_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| snli_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| cot_ecqa_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| quail_context_question_answer_description_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| gigaword_1_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| cos_e_v1_11_question_option_description_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| glue_qnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| kilt_tasks_hotpotqa_formulate | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| quartz_paragraph_question_plain_concat | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| adversarial_qa_dbert_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| ropes_prompt_bottom_hint_beginning | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| adversarial_qa_droberta_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| dream_generate_first_utterance | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| duorc_SelfRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| quail_context_description_question_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| race_high_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| glue_wnli_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| wiki_qa_Direct_Answer_to_Question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| web_questions_whats_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| quail_no_prompt_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| duorc_ParaphraseRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| cos_e_v1_11_question_option_description_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| super_glue_copa_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| app_reviews_convert_to_star_rating | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| gem_web_nlg_en_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| quoref_Context_Contains_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| gem_e2e_nlg_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| quoref_Answer_Question_Given_Context | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| duorc_SelfRC_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| race_high_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| stream_qed_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed_ii | lora |
| cos_e_v1_11_description_question_option_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_bio_comprehension | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| duorc_ParaphraseRC_title_generation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_no_prompt_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| adversarial_qa_dbidaf_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| quoref_Found_Context_Online | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| race_middle_Taking_a_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| gem_common_gen_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| race_high_Taking_a_test | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| gem_dart_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| cosmos_qa_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| lambada_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| ropes_given_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| adversarial_qa_droberta_generate_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| adversarial_qa_dbidaf_question_context_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| adversarial_qa_dbidaf_based_on | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| cot_esnli_ii | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quail_description_context_question_answer_text | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| adversarial_qa_droberta_answer_the_following_q | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
Last updated on: 2024-01-18 14:56:37+00:00
| [
"SCIQ"
] |
EleutherAI/pythia-6.9b-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-17T16:50:38Z" | 2024-02-07T00:08:39+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-6.9b-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
EleutherAI/pythia-12b-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-17T16:52:48Z" | 2024-02-07T00:08:49+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-12b-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
EleutherAI/Llama-2-7b-hf-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-17T16:52:58Z" | 2024-02-07T00:09:00+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for Llama-2-7b-hf-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
pclucas14/library-phi_2-v3 | pclucas14 | null | [
"region:us"
] | "2024-01-18T02:14:13Z" | 2024-08-19T20:47:25+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 263
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| super_glue_wic_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| quarel_logic_test | phi-2 | sordonia/flan-10k-flat/quarel_logic_test | lora |
| ropes_prompt_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| duorc_SelfRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| lambada_1_0_0 | phi-2 | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| dream_answer_to_dialogue | phi-2 | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| glue_qnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| cos_e_v1_11_i_think | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| app_reviews_convert_to_star_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| duorc_SelfRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| race_middle_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| ag_news_subset_1_0_0 | phi-2 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| quoref_Find_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| social_i_qa_Show_choices_and_generate_index | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| dream_generate_first_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| qasc_qa_with_separated_facts_4 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| web_questions_question_answer | phi-2 | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| wiki_qa_Jeopardy_style | phi-2 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| quoref_Guess_Title_For_Context | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| unified_qa_science_inst | phi-2 | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| app_reviews_generate_review | phi-2 | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_bio_who | phi-2 | sordonia/flan-10k-flat/wiki_bio_who | lora |
| race_high_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| duorc_SelfRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| cot_creak_ii | phi-2 | sordonia/flan-10k-flat/cot_creak_ii | lora |
| wmt16_translate_tr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| glue_wnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| super_glue_multirc_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quail_description_context_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| duorc_ParaphraseRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| sciq_Multiple_Choice_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_qa_found_on_google | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| cos_e_v1_11_question_description_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| ropes_given_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| ropes_background_new_situation_answer | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| glue_mnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| duorc_SelfRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| cos_e_v1_11_description_question_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| glue_cola_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| wiki_qa_Generate_Question_from_Topic | phi-2 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | phi-2 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| ropes_plain_bottom_hint | phi-2 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| cot_sensemaking | phi-2 | sordonia/flan-10k-flat/cot_sensemaking | lora |
| ropes_new_situation_background_answer | phi-2 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| glue_mrpc_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cot_gsm8k_ii | phi-2 | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| imdb_reviews_plain_text_1_0_0 | phi-2 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| huggingface_xsum | phi-2 | sordonia/flan-10k-flat/huggingface_xsum | lora |
| squad_v2_0_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| wmt16_translate_ro_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| quail_no_prompt_text | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| social_i_qa_Generate_the_question_from_the_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | phi-2 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| para_crawl_enes | phi-2 | sordonia/flan-10k-flat/para_crawl_enes | lora |
| adversarial_qa_dbidaf_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| adversarial_qa_droberta_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| wiki_bio_key_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| social_i_qa_Generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| cos_e_v1_11_question_option_description_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| wiki_hop_original_generate_subject_and_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| duorc_SelfRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| cot_creak | phi-2 | sordonia/flan-10k-flat/cot_creak | lora |
| cos_e_v1_11_rationale | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| adversarial_qa_dbert_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| stream_aqua_ii | phi-2 | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| cos_e_v1_11_aligned_with_common_sense | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| trec_1_0_0 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| quoref_Answer_Friend_Question | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| qasc_qa_with_separated_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| adversarial_qa_dbert_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| duorc_ParaphraseRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| quail_context_question_answer_description_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| ropes_prompt_bottom_no_hint | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| anli_r1_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| social_i_qa_I_was_wondering | phi-2 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| glue_stsb_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| wiqa_effect_with_string_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| cot_qasc | phi-2 | sordonia/flan-10k-flat/cot_qasc | lora |
| web_questions_whats_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| paws_wiki_1_1_0 | phi-2 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| gem_dart_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| ropes_plain_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| cosmos_qa_1_0_0 | phi-2 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| quoref_Answer_Question_Given_Context | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_read_passage_below_choose | phi-2 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| quail_context_description_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| quoref_Answer_Test | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| quartz_answer_question_below | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| openbookqa_0_1_0 | phi-2 | sordonia/flan-10k-flat/openbookqa_0_1_0 | lora |
| quail_context_question_answer_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| wiki_qa_automatic_system | phi-2 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| gigaword_1_2_0 | phi-2 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| wiqa_effect_with_label_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| bool_q_1_0_0 | phi-2 | sordonia/flan-10k-flat/bool_q_1_0_0 | lora |
| race_high_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| duorc_ParaphraseRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| multi_news_1_0_0 | phi-2 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| wiki_hop_original_generate_subject | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| squad_v1_1_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| wiqa_what_might_be_the_first_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| anli_r3_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| super_glue_rte_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| gem_e2e_nlg_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| adversarial_qa_droberta_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| adversarial_qa_droberta_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| qasc_qa_with_separated_facts_3 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| quartz_having_read_above_passage | phi-2 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| stream_qed_ii | phi-2 | sordonia/flan-10k-flat/stream_qed_ii | lora |
| super_glue_record_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| ai2_arc_ARC_Easy_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Easy_1_0_0 | lora |
| qasc_qa_with_separated_facts_2 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wmt16_translate_fi_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| adversarial_qa_dbidaf_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| coqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| wiki_bio_what_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| stream_qed | phi-2 | sordonia/flan-10k-flat/stream_qed | lora |
| quail_description_context_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| quoref_Found_Context_Online | phi-2 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| cos_e_v1_11_generate_explanation_given_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| dbpedia_14_pick_one_category_for_the_following_text | phi-2 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| super_glue_wsc_fixed_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| math_dataset_algebra__linear_1d_1_0_0 | phi-2 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| duorc_ParaphraseRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| ropes_background_situation_middle | phi-2 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| word_segment | phi-2 | sordonia/flan-10k-flat/word_segment | lora |
| wmt16_translate_de_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| cnn_dailymail_3_4_0 | phi-2 | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| ropes_prompt_bottom_hint_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| wiqa_what_might_be_the_last_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| quail_no_prompt_id | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| cot_gsm8k | phi-2 | sordonia/flan-10k-flat/cot_gsm8k | lora |
| duorc_ParaphraseRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quoref_What_Is_The_Answer | phi-2 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| hellaswag_1_1_0 | phi-2 | sordonia/flan-10k-flat/hellaswag_1_1_0 | lora |
| duorc_SelfRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| duorc_SelfRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| wiki_hop_original_explain_relation | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| dream_baseline | phi-2 | sordonia/flan-10k-flat/dream_baseline | lora |
| definite_pronoun_resolution_1_1_0 | phi-2 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| quail_context_description_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| quarel_do_not_use | phi-2 | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| quartz_paragraph_question_plain_concat | phi-2 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| cot_ecqa | phi-2 | sordonia/flan-10k-flat/cot_ecqa | lora |
| qasc_is_correct_2 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| ai2_arc_ARC_Challenge_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora |
| adversarial_qa_dbert_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| wiki_qa_Direct_Answer_to_Question | phi-2 | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| piqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/piqa_1_0_0 | lora |
| sciq_Multiple_Choice_Question_First | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | phi-2 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| duorc_ParaphraseRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| drop_2_0_0 | phi-2 | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| kilt_tasks_hotpotqa_complex_question | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| natural_questions_open_1_0_0 | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| duorc_ParaphraseRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| cot_sensemaking_ii | phi-2 | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| kilt_tasks_hotpotqa_straighforward_qa | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| sciq_Direct_Question_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| adversarial_qa_dbidaf_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| race_high_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| cot_esnli | phi-2 | sordonia/flan-10k-flat/cot_esnli | lora |
| cos_e_v1_11_question_option_description_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| quartz_use_info_from_paragraph_question | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| race_high_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| wiki_qa_Topic_Prediction_Answer_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| wmt14_translate_fr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| trivia_qa_rc_1_1_0 | phi-2 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| kilt_tasks_hotpotqa_final_exam | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| ropes_prompt_mix | phi-2 | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| snli_1_1_0 | phi-2 | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| quarel_testing_students | phi-2 | sordonia/flan-10k-flat/quarel_testing_students | lora |
| true_case | phi-2 | sordonia/flan-10k-flat/true_case | lora |
| dream_generate_last_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| quoref_Context_Contains_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| gem_web_nlg_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| cot_strategyqa_ii | phi-2 | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| winogrande_1_1_0 | phi-2 | sordonia/flan-10k-flat/winogrande_1_1_0 | lora |
| quartz_answer_question_based_on | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| quail_context_question_description_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| social_i_qa_Show_choices_and_generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| yelp_polarity_reviews_0_2_0 | phi-2 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| adversarial_qa_dbidaf_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | phi-2 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| duorc_ParaphraseRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| race_middle_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| qasc_qa_with_combined_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| duorc_ParaphraseRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| wiki_qa_exercise | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| dbpedia_14_given_a_choice_of_categories_ | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| quartz_given_the_fact_answer_the_q | phi-2 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| ropes_read_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| quail_context_question_description_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| race_middle_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| web_questions_short_general_knowledge_q | phi-2 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| race_middle_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| wiki_bio_comprehension | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| kilt_tasks_hotpotqa_combining_facts | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| duorc_SelfRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| cos_e_v1_11_explain_why_human | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| quartz_use_info_from_question_paragraph | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| glue_sst2_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| race_high_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| duorc_SelfRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| anli_r2_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| web_questions_potential_correct_answer | phi-2 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| quarel_heres_a_story | phi-2 | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| wiki_qa_Is_This_True_ | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| adversarial_qa_droberta_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| race_high_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| quac_1_0_0 | phi-2 | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| quail_context_description_question_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| adversarial_qa_dbert_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| super_glue_copa_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| adversarial_qa_droberta_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| aeslc_1_0_0 | phi-2 | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| cot_strategyqa | phi-2 | sordonia/flan-10k-flat/cot_strategyqa | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| quail_description_context_question_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| adversarial_qa_dbert_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_bio_guess_person | phi-2 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| app_reviews_convert_to_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiki_hop_original_generate_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| quail_context_question_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| sciq_Direct_Question | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| adversarial_qa_dbidaf_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| quoref_Read_And_Extract_ | phi-2 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| race_middle_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| stream_aqua | phi-2 | sordonia/flan-10k-flat/stream_aqua | lora |
| glue_qqp_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| web_questions_get_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| app_reviews_categorize_rating_using_review | phi-2 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| cot_esnli_ii | phi-2 | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quoref_Guess_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| quarel_choose_between | phi-2 | sordonia/flan-10k-flat/quarel_choose_between | lora |
| gem_wiki_lingua_english_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| qasc_qa_with_separated_facts_5 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| ropes_plain_no_background | phi-2 | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| sciq_Multiple_Choice | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| fix_punct | phi-2 | sordonia/flan-10k-flat/fix_punct | lora |
| cos_e_v1_11_question_description_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| quoref_Given_Context_Answer_Question | phi-2 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| gem_common_gen_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_what_is_the_missing_first_step | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| cot_ecqa_ii | phi-2 | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| kilt_tasks_hotpotqa_formulate | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| qasc_is_correct_1 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| super_glue_cb_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
Last updated on: 2024-01-18 02:14:13+00:00
| [
"SCIQ"
] |
EleutherAI/pythia-1b-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-18T05:52:08Z" | 2024-02-07T00:08:04+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-1b-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
EleutherAI/pythia-1.4b-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-18T05:53:04Z" | 2024-02-07T00:08:16+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-1.4b-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
EleutherAI/pythia-2.8b-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-18T05:56:26Z" | 2024-02-07T00:08:28+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-2.8b-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
taki0112/lora-trained-xl_craft-clay | taki0112 | text-to-image | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | "2024-01-18T09:44:11Z" | 2024-01-18T10:38:11+00:00 | 0 | 1 | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
license: openrail++
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: a deer in sks style
output:
url: image_0.png
- text: a deer in sks style
output:
url: image_1.png
- text: a deer in sks style
output:
url: image_2.png
- text: a deer in sks style
output:
url: image_3.png
instance_prompt: a dog in sks style
---
# SDXL LoRA DreamBooth - taki0112/lora-trained-xl_craft-clay
<Gallery />
## Model description
These are taki0112/lora-trained-xl_craft-clay LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a dog in sks style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](taki0112/lora-trained-xl_craft-clay/tree/main) them in the Files & versions tab.
| [
"CRAFT"
] |
huizhang0110/hui-embedding | huizhang0110 | null | [
"mteb",
"model-index",
"region:us"
] | "2024-01-18T10:24:23Z" | 2024-11-26T05:15:59+00:00 | 0 | 0 | ---
tags:
- mteb
model-index:
- name: no_model_name_available
results:
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 66.2368177379181
- type: cosine_spearman
value: 68.35446129213678
- type: euclidean_pearson
value: 68.35832044207704
- type: euclidean_spearman
value: 68.35446129213678
- type: main_score
value: 68.35446129213678
- type: manhattan_pearson
value: 68.70754373818515
- type: manhattan_spearman
value: 68.2292889016414
- type: pearson
value: 66.2368177379181
- type: spearman
value: 68.35446129213678
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 85.12461231748527
- type: cosine_spearman
value: 83.78377223012504
- type: euclidean_pearson
value: 84.84032421122767
- type: euclidean_spearman
value: 83.78376987896931
- type: main_score
value: 83.78377223012504
- type: manhattan_pearson
value: 84.97174244411761
- type: manhattan_spearman
value: 84.13202634643542
- type: pearson
value: 85.12461231748527
- type: spearman
value: 83.78377223012504
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 25.883
- type: map_at_1
value: 2.153
- type: map_at_10
value: 9.871
- type: map_at_100
value: 15.559000000000001
- type: map_at_1000
value: 17.183
- type: map_at_20
value: 12.552
- type: map_at_3
value: 5.493
- type: map_at_5
value: 7.85
- type: mrr_at_1
value: 30.612244897959183
- type: mrr_at_10
value: 48.89131843213475
- type: mrr_at_100
value: 49.6963561262702
- type: mrr_at_1000
value: 49.7010693279481
- type: mrr_at_20
value: 49.531452107982716
- type: mrr_at_3
value: 44.21768707482994
- type: mrr_at_5
value: 47.68707482993197
- type: nauc_map_at_1000_diff1
value: 25.31034571291797
- type: nauc_map_at_1000_max
value: 34.51576312061718
- type: nauc_map_at_1000_std
value: -4.906594382965329
- type: nauc_map_at_100_diff1
value: 25.837142212716476
- type: nauc_map_at_100_max
value: 32.59407997636304
- type: nauc_map_at_100_std
value: -10.217037670639481
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value: 33.21608048564407
- type: nauc_map_at_10_max
value: 37.468380135605706
- type: nauc_map_at_10_std
value: -20.46767738235632
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value: 32.281523854579106
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value: 22.176737258675068
- type: nauc_map_at_1_std
value: -25.07807730673564
- type: nauc_map_at_20_diff1
value: 30.866307166529584
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value: 32.272418879076724
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value: -20.40305363345012
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value: 30.88885591305534
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value: 33.431908247176786
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value: 30.684478472773836
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value: 7.286
- type: precision_at_1000
value: 1.488
- type: precision_at_20
value: 18.061
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type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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dataset:
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type: mteb/amazon_reviews_multi
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split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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type: mteb/arguana
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split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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dataset:
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type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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dataset:
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type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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dataset:
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type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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- type: nauc_recall_at_1_max
value: 16.777673629413144
- type: nauc_recall_at_1_std
value: -1.5982142140773345
- type: nauc_recall_at_20_diff1
value: -0.1682800060016626
- type: nauc_recall_at_20_max
value: 16.971491120013564
- type: nauc_recall_at_20_std
value: 16.122046383351293
- type: nauc_recall_at_3_diff1
value: 6.988663029514718
- type: nauc_recall_at_3_max
value: 14.528152900658856
- type: nauc_recall_at_3_std
value: 0.17590933968510467
- type: nauc_recall_at_5_diff1
value: 3.353041984845736
- type: nauc_recall_at_5_max
value: 15.403568054057326
- type: nauc_recall_at_5_std
value: 5.319244399661828
- type: ndcg_at_1
value: 26.900000000000002
- type: ndcg_at_10
value: 23.814
- type: ndcg_at_100
value: 34.943999999999996
- type: ndcg_at_1000
value: 40.78
- type: ndcg_at_20
value: 27.643
- type: ndcg_at_3
value: 21.227
- type: ndcg_at_5
value: 19.038
- type: precision_at_1
value: 26.900000000000002
- type: precision_at_10
value: 12.73
- type: precision_at_100
value: 2.881
- type: precision_at_1000
value: 0.426
- type: precision_at_20
value: 8.57
- type: precision_at_3
value: 19.6
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 5.455
- type: recall_at_10
value: 25.802999999999997
- type: recall_at_100
value: 58.45
- type: recall_at_1000
value: 86.457
- type: recall_at_20
value: 34.762
- type: recall_at_3
value: 11.943
- type: recall_at_5
value: 17.043
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 85.60157402941752
- type: cosine_spearman
value: 82.98956725441452
- type: euclidean_pearson
value: 83.07824357271161
- type: euclidean_spearman
value: 82.98957395335212
- type: main_score
value: 82.98956725441452
- type: manhattan_pearson
value: 83.10748351148622
- type: manhattan_spearman
value: 83.16217281563378
- type: pearson
value: 85.60157402941752
- type: spearman
value: 82.98956725441452
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 85.20198919395854
- type: cosine_spearman
value: 78.17308450713497
- type: euclidean_pearson
value: 82.91465813078975
- type: euclidean_spearman
value: 78.17308450713497
- type: main_score
value: 78.17308450713497
- type: manhattan_pearson
value: 83.36938760055344
- type: manhattan_spearman
value: 78.77166023561925
- type: pearson
value: 85.20198919395854
- type: spearman
value: 78.17308450713497
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 87.3197290035165
- type: cosine_spearman
value: 88.12589189918039
- type: euclidean_pearson
value: 87.88474436451652
- type: euclidean_spearman
value: 88.12589189918039
- type: main_score
value: 88.12589189918039
- type: manhattan_pearson
value: 88.1114243109502
- type: manhattan_spearman
value: 88.40111910955112
- type: pearson
value: 87.3197290035165
- type: spearman
value: 88.12589189918039
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 87.91424745154934
- type: cosine_spearman
value: 88.78510857775494
- type: euclidean_pearson
value: 88.60854825357943
- type: euclidean_spearman
value: 88.78511307332248
- type: main_score
value: 88.78510857775494
- type: manhattan_pearson
value: 88.81490531409946
- type: manhattan_spearman
value: 89.10162579991359
- type: pearson
value: 87.91424745154934
- type: spearman
value: 88.78510857775494
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 84.42255273136605
- type: cosine_spearman
value: 86.46810322536955
- type: euclidean_pearson
value: 86.255541184091
- type: euclidean_spearman
value: 86.46810322536955
- type: main_score
value: 86.46810322536955
- type: manhattan_pearson
value: 86.72678851651064
- type: manhattan_spearman
value: 86.93777990302539
- type: pearson
value: 84.42255273136605
- type: spearman
value: 86.46810322536955
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 91.72746389892356
- type: cosine_spearman
value: 92.23283881812245
- type: euclidean_pearson
value: 92.29179177488737
- type: euclidean_spearman
value: 92.23283881812245
- type: main_score
value: 92.23283881812245
- type: manhattan_pearson
value: 92.13764526009247
- type: manhattan_spearman
value: 92.0582843442798
- type: pearson
value: 91.72746389892356
- type: spearman
value: 92.23283881812245
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 86.14912927994007
- type: cosine_spearman
value: 87.46655844472012
- type: euclidean_pearson
value: 87.53026653408118
- type: euclidean_spearman
value: 87.46655844472012
- type: main_score
value: 87.46655844472012
- type: manhattan_pearson
value: 87.68289898403299
- type: manhattan_spearman
value: 87.73630507998439
- type: pearson
value: 86.14912927994007
- type: spearman
value: 87.46655844472012
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 86.97859154411299
- type: map
value: 86.97859154411299
- type: mrr
value: 96.35598968932302
- type: nAUC_map_diff1
value: -18.506120190268017
- type: nAUC_map_max
value: 55.78442121746724
- type: nAUC_map_std
value: 66.27889919160313
- type: nAUC_mrr_diff1
value: 18.288014199762895
- type: nAUC_mrr_max
value: 83.25297655347828
- type: nAUC_mrr_std
value: 72.809885375971
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 52.842
- type: map_at_1
value: 32.911
- type: map_at_10
value: 46.013
- type: map_at_100
value: 47.11
- type: map_at_1000
value: 47.137
- type: map_at_20
value: 46.78
- type: map_at_3
value: 41.900999999999996
- type: map_at_5
value: 44.357
- type: mrr_at_1
value: 35.0
- type: mrr_at_10
value: 46.96574074074072
- type: mrr_at_100
value: 47.959931245967184
- type: mrr_at_1000
value: 47.98510849619688
- type: mrr_at_20
value: 47.68440206880607
- type: mrr_at_3
value: 43.77777777777776
- type: mrr_at_5
value: 45.611111111111086
- type: nauc_map_at_1000_diff1
value: 42.89180178126247
- type: nauc_map_at_1000_max
value: 45.75105611403444
- type: nauc_map_at_1000_std
value: 17.463513608950578
- type: nauc_map_at_100_diff1
value: 42.893512582653656
- type: nauc_map_at_100_max
value: 45.754617699990035
- type: nauc_map_at_100_std
value: 17.490513656867037
- type: nauc_map_at_10_diff1
value: 42.364748689290415
- type: nauc_map_at_10_max
value: 45.56642444523947
- type: nauc_map_at_10_std
value: 17.079579644716894
- type: nauc_map_at_1_diff1
value: 48.949793800671124
- type: nauc_map_at_1_max
value: 45.82239538118238
- type: nauc_map_at_1_std
value: 11.183927196674755
- type: nauc_map_at_20_diff1
value: 42.67947282270775
- type: nauc_map_at_20_max
value: 45.62274524098362
- type: nauc_map_at_20_std
value: 17.51316198529124
- type: nauc_map_at_3_diff1
value: 43.238404886755745
- type: nauc_map_at_3_max
value: 43.350130089078895
- type: nauc_map_at_3_std
value: 14.13657834477199
- type: nauc_map_at_5_diff1
value: 42.54474356788842
- type: nauc_map_at_5_max
value: 44.75146781225222
- type: nauc_map_at_5_std
value: 16.15648396925114
- type: nauc_mrr_at_1000_diff1
value: 43.556859926201554
- type: nauc_mrr_at_1000_max
value: 47.140291020802906
- type: nauc_mrr_at_1000_std
value: 18.805424261346374
- type: nauc_mrr_at_100_diff1
value: 43.55633267437543
- type: nauc_mrr_at_100_max
value: 47.14214569591525
- type: nauc_mrr_at_100_std
value: 18.828541893531277
- type: nauc_mrr_at_10_diff1
value: 43.07000882702881
- type: nauc_mrr_at_10_max
value: 47.10398430807609
- type: nauc_mrr_at_10_std
value: 18.672657418468155
- type: nauc_mrr_at_1_diff1
value: 50.71044015206451
- type: nauc_mrr_at_1_max
value: 50.31094117388535
- type: nauc_mrr_at_1_std
value: 16.308699760476404
- type: nauc_mrr_at_20_diff1
value: 43.34419341411509
- type: nauc_mrr_at_20_max
value: 47.127839363881634
- type: nauc_mrr_at_20_std
value: 18.93672383999524
- type: nauc_mrr_at_3_diff1
value: 44.09886232125989
- type: nauc_mrr_at_3_max
value: 47.35761798607356
- type: nauc_mrr_at_3_std
value: 18.66293179466984
- type: nauc_mrr_at_5_diff1
value: 43.455234122310486
- type: nauc_mrr_at_5_max
value: 46.95579311628989
- type: nauc_mrr_at_5_std
value: 18.637801785868913
- type: nauc_ndcg_at_1000_diff1
value: 42.09778197382488
- type: nauc_ndcg_at_1000_max
value: 46.41254633930011
- type: nauc_ndcg_at_1000_std
value: 19.727442899891408
- type: nauc_ndcg_at_100_diff1
value: 42.127587196947616
- type: nauc_ndcg_at_100_max
value: 46.56257426488274
- type: nauc_ndcg_at_100_std
value: 20.848893214507893
- type: nauc_ndcg_at_10_diff1
value: 39.520585737534184
- type: nauc_ndcg_at_10_max
value: 45.58832499779741
- type: nauc_ndcg_at_10_std
value: 19.230954524847657
- type: nauc_ndcg_at_1_diff1
value: 50.71044015206451
- type: nauc_ndcg_at_1_max
value: 50.31094117388535
- type: nauc_ndcg_at_1_std
value: 16.308699760476404
- type: nauc_ndcg_at_20_diff1
value: 40.57140695180754
- type: nauc_ndcg_at_20_max
value: 45.78884507871275
- type: nauc_ndcg_at_20_std
value: 20.87311919719877
- type: nauc_ndcg_at_3_diff1
value: 42.23214214323953
- type: nauc_ndcg_at_3_max
value: 44.25227959403861
- type: nauc_ndcg_at_3_std
value: 16.808716032720582
- type: nauc_ndcg_at_5_diff1
value: 40.32970262607426
- type: nauc_ndcg_at_5_max
value: 44.170446333441234
- type: nauc_ndcg_at_5_std
value: 17.670796157538952
- type: nauc_precision_at_1000_diff1
value: 4.4855757822300575
- type: nauc_precision_at_1000_max
value: 40.96816841248859
- type: nauc_precision_at_1000_std
value: 52.76450049154224
- type: nauc_precision_at_100_diff1
value: 13.467456291972423
- type: nauc_precision_at_100_max
value: 46.07633674307899
- type: nauc_precision_at_100_std
value: 58.38655747924394
- type: nauc_precision_at_10_diff1
value: 18.885447707274754
- type: nauc_precision_at_10_max
value: 47.475287933169
- type: nauc_precision_at_10_std
value: 40.78242836332111
- type: nauc_precision_at_1_diff1
value: 50.71044015206451
- type: nauc_precision_at_1_max
value: 50.31094117388535
- type: nauc_precision_at_1_std
value: 16.308699760476404
- type: nauc_precision_at_20_diff1
value: 15.953924273102402
- type: nauc_precision_at_20_max
value: 45.47509365077202
- type: nauc_precision_at_20_std
value: 51.47100789520174
- type: nauc_precision_at_3_diff1
value: 34.84717380734587
- type: nauc_precision_at_3_max
value: 45.610933933265756
- type: nauc_precision_at_3_std
value: 27.734101378690852
- type: nauc_precision_at_5_diff1
value: 26.59896898222078
- type: nauc_precision_at_5_max
value: 46.140890589971264
- type: nauc_precision_at_5_std
value: 33.56649457748371
- type: nauc_recall_at_1000_diff1
value: 86.92810457516407
- type: nauc_recall_at_1000_max
value: 100.0
- type: nauc_recall_at_1000_std
value: 100.0
- type: nauc_recall_at_100_diff1
value: 43.86702049240759
- type: nauc_recall_at_100_max
value: 53.33308762101326
- type: nauc_recall_at_100_std
value: 63.09523809523798
- type: nauc_recall_at_10_diff1
value: 25.88560487444265
- type: nauc_recall_at_10_max
value: 41.6157709657381
- type: nauc_recall_at_10_std
value: 24.04962076662668
- type: nauc_recall_at_1_diff1
value: 48.949793800671124
- type: nauc_recall_at_1_max
value: 45.82239538118238
- type: nauc_recall_at_1_std
value: 11.183927196674755
- type: nauc_recall_at_20_diff1
value: 27.507691414639822
- type: nauc_recall_at_20_max
value: 41.70246318763185
- type: nauc_recall_at_20_std
value: 37.33722257696256
- type: nauc_recall_at_3_diff1
value: 35.956192998402784
- type: nauc_recall_at_3_max
value: 38.74690791289058
- type: nauc_recall_at_3_std
value: 15.683526476441553
- type: nauc_recall_at_5_diff1
value: 31.03358342668625
- type: nauc_recall_at_5_max
value: 37.820450291250786
- type: nauc_recall_at_5_std
value: 18.52848795003198
- type: ndcg_at_1
value: 35.0
- type: ndcg_at_10
value: 52.842
- type: ndcg_at_100
value: 57.513999999999996
- type: ndcg_at_1000
value: 58.272999999999996
- type: ndcg_at_20
value: 55.454
- type: ndcg_at_3
value: 45.452
- type: ndcg_at_5
value: 49.169000000000004
- type: precision_at_1
value: 35.0
- type: precision_at_10
value: 8.366999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.75
- type: precision_at_3
value: 19.333
- type: precision_at_5
value: 14.066999999999998
- type: recall_at_1
value: 32.911
- type: recall_at_10
value: 73.033
- type: recall_at_100
value: 93.667
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 83.0
- type: recall_at_3
value: 52.878
- type: recall_at_5
value: 62.06700000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.87425742574257
- type: cosine_accuracy_threshold
value: 85.4932188987732
- type: cosine_ap
value: 97.03588351132844
- type: cosine_f1
value: 93.60201511335012
- type: cosine_f1_threshold
value: 85.4932188987732
- type: cosine_precision
value: 94.31472081218274
- type: cosine_recall
value: 92.9
- type: dot_accuracy
value: 99.87425742574257
- type: dot_accuracy_threshold
value: 85.4932188987732
- type: dot_ap
value: 97.03588351132846
- type: dot_f1
value: 93.60201511335012
- type: dot_f1_threshold
value: 85.4932188987732
- type: dot_precision
value: 94.31472081218274
- type: dot_recall
value: 92.9
- type: euclidean_accuracy
value: 99.87425742574257
- type: euclidean_accuracy_threshold
value: 53.864240646362305
- type: euclidean_ap
value: 97.03588351132844
- type: euclidean_f1
value: 93.60201511335012
- type: euclidean_f1_threshold
value: 53.864240646362305
- type: euclidean_precision
value: 94.31472081218274
- type: euclidean_recall
value: 92.9
- type: main_score
value: 97.12020380643673
- type: manhattan_accuracy
value: 99.87821782178217
- type: manhattan_accuracy_threshold
value: 2557.1868896484375
- type: manhattan_ap
value: 97.12020380643673
- type: manhattan_f1
value: 93.83458646616543
- type: manhattan_f1_threshold
value: 2559.8316192626953
- type: manhattan_precision
value: 94.07035175879398
- type: manhattan_recall
value: 93.60000000000001
- type: max_accuracy
value: 99.87821782178217
- type: max_ap
value: 97.12020380643673
- type: max_f1
value: 93.83458646616543
- type: max_precision
value: 94.31472081218274
- type: max_recall
value: 93.60000000000001
- type: similarity_accuracy
value: 99.87425742574257
- type: similarity_accuracy_threshold
value: 85.4932188987732
- type: similarity_ap
value: 97.03588351132844
- type: similarity_f1
value: 93.60201511335012
- type: similarity_f1_threshold
value: 85.4932188987732
- type: similarity_precision
value: 94.31472081218274
- type: similarity_recall
value: 92.9
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 76.98818225336838
- type: v_measure
value: 76.98818225336838
- type: v_measure_std
value: 3.154967965946174
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 45.163651140607605
- type: v_measure
value: 45.163651140607605
- type: v_measure_std
value: 1.4322970276083837
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 56.391883714372696
- type: map
value: 56.391883714372696
- type: mrr
value: 57.349492827434
- type: nAUC_map_diff1
value: 39.157250127064955
- type: nAUC_map_max
value: 18.467392575309553
- type: nAUC_map_std
value: 6.562904741623687
- type: nAUC_mrr_diff1
value: 39.2616391317946
- type: nAUC_mrr_max
value: 20.17824080849778
- type: nAUC_mrr_std
value: 7.3151994802766005
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 31.115370364087013
- type: cosine_spearman
value: 30.168250595399797
- type: dot_pearson
value: 31.11537534713581
- type: dot_spearman
value: 30.168250595399797
- type: main_score
value: 30.168250595399797
- type: pearson
value: 31.115370364087013
- type: spearman
value: 30.168250595399797
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 58.492
- type: map_at_1
value: 0.20600000000000002
- type: map_at_10
value: 1.355
- type: map_at_100
value: 7.682
- type: map_at_1000
value: 19.422
- type: map_at_20
value: 2.307
- type: map_at_3
value: 0.504
- type: map_at_5
value: 0.756
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 83.07460317460317
- type: mrr_at_100
value: 83.34653299916457
- type: mrr_at_1000
value: 83.34653299916457
- type: mrr_at_20
value: 83.34653299916457
- type: mrr_at_3
value: 81.66666666666666
- type: mrr_at_5
value: 82.56666666666666
- type: nauc_map_at_1000_diff1
value: -0.9517122101342602
- type: nauc_map_at_1000_max
value: 35.489825727736665
- type: nauc_map_at_1000_std
value: 72.31927320292716
- type: nauc_map_at_100_diff1
value: -2.6696855309157197
- type: nauc_map_at_100_max
value: 16.881012905948
- type: nauc_map_at_100_std
value: 60.636797544764796
- type: nauc_map_at_10_diff1
value: 3.3220618387062166
- type: nauc_map_at_10_max
value: 7.9728051776655136
- type: nauc_map_at_10_std
value: 37.001872811447676
- type: nauc_map_at_1_diff1
value: 19.385947791364455
- type: nauc_map_at_1_max
value: -2.017784609408856
- type: nauc_map_at_1_std
value: 15.846915472515105
- type: nauc_map_at_20_diff1
value: 1.0613460412567055
- type: nauc_map_at_20_max
value: 7.639419874542262
- type: nauc_map_at_20_std
value: 42.004875229740826
- type: nauc_map_at_3_diff1
value: 7.0015165243253366
- type: nauc_map_at_3_max
value: 7.084211457521959
- type: nauc_map_at_3_std
value: 24.788352390570584
- type: nauc_map_at_5_diff1
value: 6.657899114095232
- type: nauc_map_at_5_max
value: 4.976947597730104
- type: nauc_map_at_5_std
value: 29.481454683941184
- type: nauc_mrr_at_1000_diff1
value: 14.561577730498792
- type: nauc_mrr_at_1000_max
value: 57.72810732532122
- type: nauc_mrr_at_1000_std
value: 66.88388647529588
- type: nauc_mrr_at_100_diff1
value: 14.561577730498792
- type: nauc_mrr_at_100_max
value: 57.72810732532122
- type: nauc_mrr_at_100_std
value: 66.88388647529588
- type: nauc_mrr_at_10_diff1
value: 14.57469254485188
- type: nauc_mrr_at_10_max
value: 58.079825098428714
- type: nauc_mrr_at_10_std
value: 67.32128458796227
- type: nauc_mrr_at_1_diff1
value: 25.34827377347056
- type: nauc_mrr_at_1_max
value: 50.58838798996285
- type: nauc_mrr_at_1_std
value: 59.36661763433414
- type: nauc_mrr_at_20_diff1
value: 14.561577730498792
- type: nauc_mrr_at_20_max
value: 57.72810732532122
- type: nauc_mrr_at_20_std
value: 66.88388647529588
- type: nauc_mrr_at_3_diff1
value: 9.063532868160214
- type: nauc_mrr_at_3_max
value: 58.71832537642312
- type: nauc_mrr_at_3_std
value: 69.07730444362834
- type: nauc_mrr_at_5_diff1
value: 13.555968426927894
- type: nauc_mrr_at_5_max
value: 59.22085120600723
- type: nauc_mrr_at_5_std
value: 67.47575721875769
- type: nauc_ndcg_at_1000_diff1
value: -1.8751322983265282
- type: nauc_ndcg_at_1000_max
value: 38.78712823179003
- type: nauc_ndcg_at_1000_std
value: 70.43132053994896
- type: nauc_ndcg_at_100_diff1
value: -10.220936212671377
- type: nauc_ndcg_at_100_max
value: 47.70220514113511
- type: nauc_ndcg_at_100_std
value: 75.65229647100806
- type: nauc_ndcg_at_10_diff1
value: 2.0956279601914227
- type: nauc_ndcg_at_10_max
value: 48.868693823231304
- type: nauc_ndcg_at_10_std
value: 70.16734895474447
- type: nauc_ndcg_at_1_diff1
value: 27.89880129091742
- type: nauc_ndcg_at_1_max
value: 44.14668818195789
- type: nauc_ndcg_at_1_std
value: 60.28699861687413
- type: nauc_ndcg_at_20_diff1
value: -3.5946895305356623
- type: nauc_ndcg_at_20_max
value: 46.68859141418255
- type: nauc_ndcg_at_20_std
value: 70.27067652865686
- type: nauc_ndcg_at_3_diff1
value: 7.400409149522286
- type: nauc_ndcg_at_3_max
value: 45.61078758588923
- type: nauc_ndcg_at_3_std
value: 62.06453130401961
- type: nauc_ndcg_at_5_diff1
value: 5.830725665736509
- type: nauc_ndcg_at_5_max
value: 46.62678021725239
- type: nauc_ndcg_at_5_std
value: 64.28848314363539
- type: nauc_precision_at_1000_diff1
value: -9.666313428844905
- type: nauc_precision_at_1000_max
value: 47.57616298626001
- type: nauc_precision_at_1000_std
value: 49.81803250713608
- type: nauc_precision_at_100_diff1
value: -10.753663329125686
- type: nauc_precision_at_100_max
value: 45.231033820687834
- type: nauc_precision_at_100_std
value: 74.22025319558313
- type: nauc_precision_at_10_diff1
value: -0.9044324563451003
- type: nauc_precision_at_10_max
value: 46.282938258557955
- type: nauc_precision_at_10_std
value: 67.20654075066248
- type: nauc_precision_at_1_diff1
value: 25.34827377347056
- type: nauc_precision_at_1_max
value: 50.58838798996285
- type: nauc_precision_at_1_std
value: 59.36661763433414
- type: nauc_precision_at_20_diff1
value: -5.192190687520166
- type: nauc_precision_at_20_max
value: 39.61181596936397
- type: nauc_precision_at_20_std
value: 65.90673204251821
- type: nauc_precision_at_3_diff1
value: -1.1581585542804733
- type: nauc_precision_at_3_max
value: 48.095238095238116
- type: nauc_precision_at_3_std
value: 57.79976256430543
- type: nauc_precision_at_5_diff1
value: 3.355915932928888
- type: nauc_precision_at_5_max
value: 43.99987410397438
- type: nauc_precision_at_5_std
value: 62.106083138587906
- type: nauc_recall_at_1000_diff1
value: 3.655993902820825
- type: nauc_recall_at_1000_max
value: 28.761919544640335
- type: nauc_recall_at_1000_std
value: 61.94123910402753
- type: nauc_recall_at_100_diff1
value: 2.5155941410242977
- type: nauc_recall_at_100_max
value: 9.499702402437284
- type: nauc_recall_at_100_std
value: 52.57449917231589
- type: nauc_recall_at_10_diff1
value: 5.939411921276368
- type: nauc_recall_at_10_max
value: 4.994244760738587
- type: nauc_recall_at_10_std
value: 33.64383950012248
- type: nauc_recall_at_1_diff1
value: 19.385947791364455
- type: nauc_recall_at_1_max
value: -2.017784609408856
- type: nauc_recall_at_1_std
value: 15.846915472515105
- type: nauc_recall_at_20_diff1
value: 3.339213533105717
- type: nauc_recall_at_20_max
value: 1.4182715611821584
- type: nauc_recall_at_20_std
value: 36.13152761959804
- type: nauc_recall_at_3_diff1
value: 2.9154975009752775
- type: nauc_recall_at_3_max
value: 5.418186566728512
- type: nauc_recall_at_3_std
value: 24.420940449950507
- type: nauc_recall_at_5_diff1
value: 7.4799616256209305
- type: nauc_recall_at_5_max
value: 2.1601588551873823
- type: nauc_recall_at_5_std
value: 28.09415304774757
- type: ndcg_at_1
value: 72.0
- type: ndcg_at_10
value: 58.492
- type: ndcg_at_100
value: 45.437
- type: ndcg_at_1000
value: 44.108999999999995
- type: ndcg_at_20
value: 54.969
- type: ndcg_at_3
value: 64.93900000000001
- type: ndcg_at_5
value: 60.736999999999995
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 61.199999999999996
- type: precision_at_100
value: 46.839999999999996
- type: precision_at_1000
value: 19.666
- type: precision_at_20
value: 56.8
- type: precision_at_3
value: 68.0
- type: precision_at_5
value: 62.8
- type: recall_at_1
value: 0.20600000000000002
- type: recall_at_10
value: 1.5939999999999999
- type: recall_at_100
value: 11.498
- type: recall_at_1000
value: 42.729
- type: recall_at_20
value: 2.922
- type: recall_at_3
value: 0.5309999999999999
- type: recall_at_5
value: 0.8370000000000001
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 85.9521484375
- type: ap
value: 30.374730390938566
- type: ap_weighted
value: 30.374730390938566
- type: f1
value: 70.3917271343218
- type: f1_weighted
value: 88.45609971763992
- type: main_score
value: 85.9521484375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 80.12733446519525
- type: f1
value: 80.418094849412
- type: f1_weighted
value: 80.10847441279616
- type: main_score
value: 80.12733446519525
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 64.6036121602603
- type: v_measure
value: 64.6036121602603
- type: v_measure_std
value: 1.2991377356017484
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 87.86433808189784
- type: cosine_accuracy_threshold
value: 85.5525255203247
- type: cosine_ap
value: 78.93155350890012
- type: cosine_f1
value: 71.80031864046734
- type: cosine_f1_threshold
value: 83.99585485458374
- type: cosine_precision
value: 72.26082308925709
- type: cosine_recall
value: 71.34564643799473
- type: dot_accuracy
value: 87.86433808189784
- type: dot_accuracy_threshold
value: 85.55253744125366
- type: dot_ap
value: 78.93157147282707
- type: dot_f1
value: 71.80031864046734
- type: dot_f1_threshold
value: 83.99585485458374
- type: dot_precision
value: 72.26082308925709
- type: dot_recall
value: 71.34564643799473
- type: euclidean_accuracy
value: 87.86433808189784
- type: euclidean_accuracy_threshold
value: 53.75403165817261
- type: euclidean_ap
value: 78.93157128337329
- type: euclidean_f1
value: 71.80031864046734
- type: euclidean_f1_threshold
value: 56.575870513916016
- type: euclidean_precision
value: 72.26082308925709
- type: euclidean_recall
value: 71.34564643799473
- type: main_score
value: 79.12654131533807
- type: manhattan_accuracy
value: 87.98950944745782
- type: manhattan_accuracy_threshold
value: 2512.5680923461914
- type: manhattan_ap
value: 79.12654131533807
- type: manhattan_f1
value: 71.90745366110163
- type: manhattan_f1_threshold
value: 2624.722671508789
- type: manhattan_precision
value: 71.65313073094053
- type: manhattan_recall
value: 72.16358839050132
- type: max_accuracy
value: 87.98950944745782
- type: max_ap
value: 79.12654131533807
- type: max_f1
value: 71.90745366110163
- type: max_precision
value: 72.26082308925709
- type: max_recall
value: 72.16358839050132
- type: similarity_accuracy
value: 87.86433808189784
- type: similarity_accuracy_threshold
value: 85.5525255203247
- type: similarity_ap
value: 78.93155350890012
- type: similarity_f1
value: 71.80031864046734
- type: similarity_f1_threshold
value: 83.99585485458374
- type: similarity_precision
value: 72.26082308925709
- type: similarity_recall
value: 71.34564643799473
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.03248340901153
- type: cosine_accuracy_threshold
value: 84.39068794250488
- type: cosine_ap
value: 85.87150718008797
- type: cosine_f1
value: 78.39147286821706
- type: cosine_f1_threshold
value: 82.88650512695312
- type: cosine_precision
value: 75.96792834440913
- type: cosine_recall
value: 80.97474591931014
- type: dot_accuracy
value: 89.03248340901153
- type: dot_accuracy_threshold
value: 84.39069986343384
- type: dot_ap
value: 85.87150946221163
- type: dot_f1
value: 78.39147286821706
- type: dot_f1_threshold
value: 82.88650512695312
- type: dot_precision
value: 75.96792834440913
- type: dot_recall
value: 80.97474591931014
- type: euclidean_accuracy
value: 89.03248340901153
- type: euclidean_accuracy_threshold
value: 55.873626470565796
- type: euclidean_ap
value: 85.87151445202907
- type: euclidean_f1
value: 78.39147286821706
- type: euclidean_f1_threshold
value: 58.5038423538208
- type: euclidean_precision
value: 75.96792834440913
- type: euclidean_recall
value: 80.97474591931014
- type: main_score
value: 85.95871260636034
- type: manhattan_accuracy
value: 89.09069740365584
- type: manhattan_accuracy_threshold
value: 2603.150749206543
- type: manhattan_ap
value: 85.95871260636034
- type: manhattan_f1
value: 78.53649430651484
- type: manhattan_f1_threshold
value: 2714.5809173583984
- type: manhattan_precision
value: 76.23396390519677
- type: manhattan_recall
value: 80.9824453341546
- type: max_accuracy
value: 89.09069740365584
- type: max_ap
value: 85.95871260636034
- type: max_f1
value: 78.53649430651484
- type: max_precision
value: 76.23396390519677
- type: max_recall
value: 80.9824453341546
- type: similarity_accuracy
value: 89.03248340901153
- type: similarity_accuracy_threshold
value: 84.39068794250488
- type: similarity_ap
value: 85.87150718008797
- type: similarity_f1
value: 78.39147286821706
- type: similarity_f1_threshold
value: 82.88650512695312
- type: similarity_precision
value: 75.96792834440913
- type: similarity_recall
value: 80.97474591931014
---
| [
"BIOSSES",
"SCIFACT"
] |
deadman44/SDXL_Photoreal_Merged_Models | deadman44 | text-to-image | [
"text-to-image",
"stable-diffusion",
"safetensors",
"stable-diffusion-xl",
"en",
"license:other",
"region:us"
] | "2024-01-18T10:43:23Z" | 2025-03-12T05:05:33+00:00 | 0 | 62 | ---
language:
- en
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- safetensors
- stable-diffusion-xl
---
<style>
.title{
font-size: 2.5em;
letter-spacing: 0.01em;
padding: 0.5em 0;
}
.thumbwidth{
max-width: 180px;
}
.font_red{
color:red;
}
.font_blue{
color:blue;
}
.font_grey{
color: #aaaaaa;
}
</style>
# models
- [Zipatrious_XL_v1.0](#zipatrious1) (<span class="font_red">Illustrious Base</span>):2025-03-12<br />
- [Zipanoob_XL_Epred_v1.1](#zipanoob1) (<span class="font_red">Noob AI Base</span>):2025-01-31<br />
- [Zipanoob_XL_Vpred_v1.1](#zipanoob1) (<span class="font_red">Noob AI Base</span>):2025-01-14<br />
- [Zipang XL test03.1](#test031) (<span class="font_red">Animagine Base</span>): 3.1b:2024-07-22<br />
- [Ponypang_XL_giveup](#potest2) (<span class="font_red">Pony Base</span>): fix5:2024-07-03<br />
---
<a id="zipatrious1"></a>
<h1 class="title">
<span>Zipatrious XL v1.0</span>
</h1>
-20000+ images Finetune trained<br/>
-<span class="font_red">Experimental Version</span><br/>
-<span class="font_blue">More realistic</span><br/>
-<span class="font_red">Expression is not wide</span><br/>
-<span class="font_red">Many bankruptcies</span><br/>
-<span class="font_red">Animated characters cannot be output.</span><br/>
<br/>
-<a href="https://huggingface.co/tianweiy/DMD2/blob/main/dmd2_sdxl_4step_lora.safetensors">dmd2_sdxl_4step_lora</a> included<br/>
-<span class="font_red">ADetailer or HiresFix Recommendation.</span><br/><br/>
<br/>
<br/>
# Recommendation
<span class="font_blue">Euler a Uniform 10 steps CFG Scale:1.3</span>
<br/>
<br/>
[Download: v1.0](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/Zipatrious_XL_v1.0.safetensors?download=true)<br/>
<br/>
# - base model
-<a href="https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0">
Illustrious-xl-early-release-v0</a><br/>
-great XL model!<br />
<br />
## - trigger
```bash
japanese
european
3yo-30yo
myob, myjd, myjk, myjc, myjsh, myjsm, myjsl, myjy (one of these)
```
<br />
## - quality tags
```bash
masterpiece, best quality, realistic, photorealistic,
```
<br />
## - negative tags
```bash
low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
```
<br />
## - prompt
```bash
danbooru tag + natural english
```
<br />
## - Sampling method
```bash
Euler a :10 steps
```
<br />
## - Schedule type
```bash
Uniform
```
<br />
## - cfg scale
```bash
1-1.5
```
<br />
## - Resolution
```bash
768 x1024, 1024 x 768
1024 x 1024
896 x 1152, 1152 x 896
512 x 768, 768 x 512
832 x 1216
```
<br />
## - Recommended
```bash
-Hires.fix:
Hires steps:3, Denoising strength:0.1-0.2, Hires CFG Scale:1.3
or
ADetailer:
yoroface, Inpaint denoising strength:0.1-0.3
+ i2i(same settings t2i, Denoising strength:0.1-0.3) + script: Ultra SD upscale (*external script)
```
<br />
---
# sample prompt
[<img src=https://t12.pixhost.to/thumbs/866/575299674_20250312131222_zipatrious_xl_v1-0_1747029599.jpg />](https://img12.pixhost.to/images/866/575299674_20250312131222_zipatrious_xl_v1-0_1747029599.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole,
25yo, myob, japanese, 1girl, solo, black hair, long hair, black eyes, closed mouth, looking at viewer, smile,
sitting, arm support, on floor, sweater dress, ribbed sweater, sleeves past wrists, denim long pants, knee up, leaning back, hand on leg,
indoors, sunlight, day, window, curtains, backlighting, windowsill, balcony, feet out of frame
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 10, Sampler: Euler a, Schedule type: Uniform, CFG scale: 1.3, Seed: 1747029599, Size: 832x1216, Model hash: 1699514967, Model: Zipatrious_XL_v1.0, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t12.pixhost.to/thumbs/866/575299681_20250312132311_zipatrious_xl_v1-0_1091038412.jpg />](https://img12.pixhost.to/images/866/575299681_20250312132311_zipatrious_xl_v1-0_1091038412.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole,
19yo, myjd, japanese, 1girl, squatting, brown hair, smile, parted lips, brown eyes, shoulder bag, long hair, ponytail, looking at viewer, white shirt, dress shirt, black skirt, shadow, night, back alley, photo background, close-up
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 10, Sampler: Euler a, Schedule type: Uniform, CFG scale: 1.3, Seed: 1091038412, Size: 832x1216, Model hash: 1699514967, Model: Zipatrious_XL_v1.0, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t12.pixhost.to/thumbs/866/575299687_20250312133407_zipatrious_xl_v1-0_1089820708.jpg />](https://img12.pixhost.to/images/866/575299687_20250312133407_zipatrious_xl_v1-0_1089820708.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole,
12yo, myjsh, japanese, 1girl, solo, black casual hair, lying on couch, green couch, medium skirt, wooden room, white casual dress, long sleeve, detailed, grin, looking at viewer
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 10, Sampler: Euler a, Schedule type: Uniform, CFG scale: 1.3, Seed: 1089820708, Size: 832x1216, Model hash: 1699514967, Model: Zipatrious_XL_v1.0, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t12.pixhost.to/thumbs/866/575299694_20250312134416_zipatrious_xl_v1-0_3234313253.jpg />](https://img12.pixhost.to/images/866/575299694_20250312134416_zipatrious_xl_v1-0_3234313253.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole,
28yo, myob, european, 1girl, mature female, solo, long wavy hair, blonde hair, blue eyes, smile, salute, air force uniform, pilot, sceanery, blurry, cowboy shot
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 10, Sampler: Euler a, Schedule type: Uniform, CFG scale: 1.3, Seed: 3234313253, Size: 832x1216, Model hash: 1699514967, Model: Zipatrious_XL_v1.0, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
<br />
---
<a id="zipanoob1"></a>
<h1 class="title">
<span>Zipanoob XL Vpred v1.1</span>
</h1>
-20000+ images Finetune trained<br/>
<br/>
-(Epred) Base model:NoobAI XL Epsilon-pred 1.1-Version<br/>
-(Vpred) Base model:NoobAI XL V-Pred 1.0<br/>
-<a href="https://huggingface.co/tianweiy/DMD2/blob/main/dmd2_sdxl_4step_lora.safetensors">dmd2_sdxl_4step_lora</a> included<br/>
-<span class="font_red">ADetailer or HiresFix Recommendation.</span><br/><br/>
***<span class="font_red">(Vpred version)</span><br/>
-(forge)<span class="font_blue">turn on</span> Zero Terminal SNR<br/>
***<span class="font_blue">Turn off</span> when using flux or other epred models<br/>
<br/>
<br/>
[Download: Epred_v1.1](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/Zipanoob_XL_Epred_v1.1.safetensors?download=true)<br/>
[Download: Vpred_v1.1](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/Zipanoob_XL_Vpred_v1.1.safetensors?download=true)<br/>
<br/>
# - base model
-<a href="https://civitai.com/models/833294?modelVersionId=1116447">NoobAI XL Epsilon-pred 1.1-Version</a><br/>
-<a href="https://civitai.com/models/833294?modelVersionId=1190596">NoobAI XL V-Pred 1.0</a><br/>
-great XL model!<br />
<br />
## - trigger
```bash
japanese
european
3yo-30yo
myob, myjd, myjk, myjc, myjsh, myjsm, myjsl, myjy (one of these)
```
<br />
## - quality tags
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles,
```
<br />
## - negative tags
```bash
low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
```
<br />
## - prompt
```bash
danbooru tag + natural english
```
<br />
## - Sampling method
```bash
Euler a :6 steps
```
<br />
## - Schedule type
```bash
SGM Uniform, KL Optimal, Normal, Simple
```
<br />
## - cfg scale
```bash
1-1.5
```
<br />
## - Resolution
```bash
768 x1024, 1024 x 768
1024 x 1024
896 x 1152, 1152 x 896
512 x 768, 768 x 512
```
<br />
## - Recommended
```bash
-Hires.fix:
Hires steps:3, Denoising strength:0.1-0.2, Hires CFG Scale:1.3
or
ADetailer:
yoroface, Inpaint denoising strength:0.1-0.3
+ i2i(same settings t2i, Denoising strength:0.1-0.3) + script: Ultra SD upscale (*external script)
```
<br />
---
# sample prompt (Image is Vpred version)
[<img src=https://t101.pixhost.to/thumbs/558/555277222_20250114134415_zipanoob_xl_vpred_v1-1_429389538.jpg />](https://img101.pixhost.to/images/558/555277222_20250114134415_zipanoob_xl_vpred_v1-1_429389538.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
19yo, myjd, japanese, 1girl, solo, black hair, long hair, brown eyes, makeup, lipstick, smile, closed mouth,
white dress, lace-trimmed dress, lace trim, laceshort sleeves, sleeveless,
looking at viewer, squatting, knees up,
depth of field, lens flare
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 429389538, Size: 896x1152, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Hires Module 1: Use same choices, Hires CFG Scale: 1.3, Hires upscale: 2, Hires steps: 3, Hires upscaler: 4x-UltraSharp, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/558/555278714_20250114140909_zipanoob_xl_vpred_v1-1_1091415239.jpg />](https://img101.pixhost.to/images/558/555278714_20250114140909_zipanoob_xl_vpred_v1-1_1091415239.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
myjk, japanese, 2girls, multiple girls,
black hair, long hair, short hair, ponytail, bangs, brown eyes, school uniform, white shirt, short sleeves, bowtie,
smile, happy, looking at viewer, selfie, v, train interior, window
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 1091415239, Size: 1792x2304, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Ultimate SD upscale upscaler: 4x-UltraSharp, Ultimate SD upscale tile_width: 896, Ultimate SD upscale tile_height: 1152, Ultimate SD upscale mask_blur: 8, Ultimate SD upscale padding: 32, Mask blur: 8, Inpaint area: Only masked, Masked area padding: 32, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/558/555278715_20250114142203_zipanoob_xl_vpred_v1-1_3192029900.jpg />](https://img101.pixhost.to/images/558/555278715_20250114142203_zipanoob_xl_vpred_v1-1_3192029900.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
15yo, myjc, japanese, 1girl, solo,
very long hair, black hair, hair bow, black eyes,
blue dress, detached sleeves, wide sleeves, bare shoulders, blue skirt, fur-trimmed skirt, white over-kneehighs, white socks, boots,
grin, holding microphone, standing, looking at viewer, hand up, live stage, spot lighting, shadow, cowboy shot, winf
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 3192029900, Size: 896x1152, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.2, Hires Module 1: Use same choices, Hires CFG Scale: 1.3, Hires upscale: 2, Hires steps: 3, Hires upscaler: 4x-UltraSharp, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/559/555296170_20250114163258_zipanoob_xl_vpred_v1-1_2021607063.jpg />](https://img101.pixhost.to/images/559/555296170_20250114163258_zipanoob_xl_vpred_v1-1_2021607063.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
24yo, myob, japanese, 1girl, solo focus, crowd, Akihabara, maid, buissines suit, day lighting, wind, dutch angle
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 2021607063, Size: 1792x2304, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Ultimate SD upscale upscaler: 4x-UltraSharp, Ultimate SD upscale tile_width: 896, Ultimate SD upscale tile_height: 1152, Ultimate SD upscale mask_blur: 8, Ultimate SD upscale padding: 32, Mask blur: 8, Inpaint area: Only masked, Masked area padding: 32, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/559/555296176_20250114165441_zipanoob_xl_vpred_v1-1_197566853.jpg />](https://img101.pixhost.to/images/560/555302145_20250114165441_zipanoob_xl_vpred_v1-1_197566853.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
23yo, myob, european, woman, solo, blonde hair, looking at viewer, grin, blue eyes, wavy hair, space ship, sitting, knee up, crossed legs, windw, earth \(planet\), head tilt, cowboy shot, feet out of frame, milf, astronaut
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 197566853, Size: 896x1152, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.3, Hires Module 1: Use same choices, Hires CFG Scale: 1.3, Hires upscale: 2, Hires steps: 3, Hires upscaler: 4x-UltraSharp, Emphasis: No norm, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/559/555296169_20250114155644_zipanoob_xl_vpred_v1-1_4151704591.jpg />](https://img101.pixhost.to/images/559/555296169_20250114155644_zipanoob_xl_vpred_v1-1_4151704591.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
27yo, myob, european, sunset, woman, ocean, outdoors, standing, backlighting, blonde hair, blue eyes, long hair, silhouette, shadow, horizon, white dress, wind, smile, upper body, adjusting Hair, looking to the side, fence
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 4151704591, Size: 896x1152, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.2, Hires Module 1: Use same choices, Hires CFG Scale: 1.3, Hires upscale: 2, Hires steps: 3, Hires upscaler: R-ESRGAN 4x+, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
## Anime Characters (danbooru tag)
[<img src=https://t101.pixhost.to/thumbs/558/555278718_20250114143709_zipanoob_xl_vpred_v1-1_3217608951.jpg />](https://img101.pixhost.to/images/558/555278718_20250114143709_zipanoob_xl_vpred_v1-1_3217608951.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
10yo, myjsm, japanese, 1girl, solo, zundamon, white shirt, green overall, smile, looking at viewer, cowboy shot, outdoors, hands on own waist
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 3217608951, Size: 1792x2304, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Ultimate SD upscale upscaler: 4x-UltraSharp, Ultimate SD upscale tile_width: 896, Ultimate SD upscale tile_height: 1152, Ultimate SD upscale mask_blur: 8, Ultimate SD upscale padding: 32, Mask blur: 8, Inpaint area: Only masked, Masked area padding: 32, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
---
[<img src=https://t101.pixhost.to/thumbs/558/555278721_20250114144655_zipanoob_xl_vpred_v1-1_2061259503.jpg />](https://img101.pixhost.to/images/558/555278721_20250114144655_zipanoob_xl_vpred_v1-1_2061259503.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
myjc, european, frieren, fern \(sousou no frieren\), cosplay,
2girls, multiple girls, posing, at night, flash lighting, grin, looking at viewer, side-by-side
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 2061259503, Size: 1792x2304, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Ultimate SD upscale upscaler: 4x-UltraSharp, Ultimate SD upscale tile_width: 896, Ultimate SD upscale tile_height: 1152, Ultimate SD upscale mask_blur: 8, Ultimate SD upscale padding: 32, Mask blur: 8, Inpaint area: Only masked, Masked area padding: 32, Emphasis: No norm, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
[<img src=https://t101.pixhost.to/thumbs/558/555278723_20250114150144_zipanoob_xl_vpred_v1-1_1934868269.jpg />](https://img101.pixhost.to/images/558/555278723_20250114150144_zipanoob_xl_vpred_v1-1_1934868269.jpg)
```bash
masterpiece, best quality, realistic, photorealistic, mole, body freckles, body mole,
yoru \(spy x family\), anya \(spy x family\), 2girls, multiple girls, smile, sitting on chair, black uniform, natural lighting, looking at viewer, kitchen, side-by-side
Negative prompt: low quality, worst quality, lowres, bad, bad anatomy, bad hands, multiple hands, mutation hands, fewer, extra, missing, displeasing, extra digits
Steps: 6, Sampler: Euler a, Schedule type: SGM Uniform, CFG scale: 1.3, Seed: 1934868269, Size: 896x1152, Model hash: 2898567825, Model: Zipanoob_XL_Vpred_v1.1, Denoising strength: 0.1, Hires Module 1: Use same choices, Hires CFG Scale: 1.3, Hires upscale: 2, Hires steps: 3, Hires upscaler: R-ESRGAN 4x+, Emphasis: No norm, Noise Schedule: Zero Terminal SNR, Version: f2.0.1v1.10.1-previous-634-g37301b22, Module 1: sdxl.vae
```
<br />
<br />
---
## -Train Settings
- [sd-scripts (SD3 branch)](https://github.com/kohya-ss/sd-scripts/tree/sd3)<br>
```bash
base model: NoobAI-XL-Vpred-v1.0.safetensors
caption: JoyCaption Alpha Two
tags: WD EVA02-Large Tagger v3
--network_module "sdxl_train.py" ^
--caption_dropout_rate="0" ^
--vae_batch_size="1" ^
--gradient_checkpointing ^
--persistent_data_loader_workers ^
--cache_latents ^
--cache_latents_to_disk ^
--max_data_loader_n_workers=2 ^
--enable_bucket ^
--bucket_no_upscale ^
--save_model_as "safetensors" ^
--mixed_precision "bf16" ^
--learning_rate=5e-6 ^
--train_text_encoder ^
--learning_rate_te1=5e-7 ^
--learning_rate_te2=5e-7 ^
--resolution=1024,1024 ^
--train_batch_size 2 ^
--optimizer_type "adafactor" ^
--optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" ^
--lr_scheduler "constant_with_warmup" ^
--save_precision "bf16" ^
--caption_extension ".txt" ^
--no_half_vae ^
--full_bf16 ^
--max_grad_norm=0 ^
--min_snr_gamma=5 ^
--max_token_length=225 ^
--fused_backward_pass ^
//Epred version
--noise_offset=0.0375 ^
--adaptive_noise_scale=0.00375 ^
//
//Vpred version
--v_parameterization ^
--zero_terminal_snr ^
//
--save_state ^
--xformers
```
<br />
---
<br />
<a id="test031"></a>
<h1 class="title">
<span>Zipang XL test3.1</span>
</h1>
-4000+ twitter images trained & 10000+ images merged model<br/>
<br/>
-Animagine XL 3.1 Base<br/>
-Good Lighting<br/>
-photoreal like tag: shadow, flash lighting, backlighting, silhouette, sunset, night, day, bokeh, etc.<br/>
<br/>
[Download:test3.1](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/zipang_XL_test3.1.fp16.safetensors?download=true) (Recommended)<br/>[Download:test3.1b](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/zipang_XL_test3.1b.fp16.safetensors?download=true) (Newer isn't always better)
<br/>
<br/>
These images are test3.1.
<table>
<tr>
<td>
<a href="https://img95.pixhost.to/images/1089/472649581_20240528114225_zipang_xl_test3-1-fp16_1076069870.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1089/472649581_20240528114225_zipang_xl_test3-1-fp16_1076069870.jpg" alt="sample1" class="thumbwidth" >
</div>
</a>
<a href="https://img95.pixhost.to/images/1089/472649589_20240528120137_zipang_xl_test3-1-fp16_1729913506.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1089/472649589_20240528120137_zipang_xl_test3-1-fp16_1729913506.jpg" alt="sample2" class="thumbwidth" >
</div>
</a>
</td>
<td>
<a href="https://img95.pixhost.to/images/1089/472649600_20240528121846_zipang_xl_test3-1-fp16_2615163109.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1089/472649600_20240528121846_zipang_xl_test3-1-fp16_2615163109.jpg" alt="sample3" class="thumbwidth" >
</div>
</a>
<a href="https://img95.pixhost.to/images/1092/472679357_20240528181848_zipang_xl_test3-1-fp16_2936243811.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1092/472679357_20240528181848_zipang_xl_test3-1-fp16_2936243811.jpg" alt="sample4" class="thumbwidth" >
</td>
</div>
</a>
<td>
<a href="https://img95.pixhost.to/images/1089/472649592_20240528120835_zipang_xl_test3-1-fp16_718944311.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1089/472649592_20240528120835_zipang_xl_test3-1-fp16_718944311.jpg" alt="sample5" class="thumbwidth" >
</div>
</a>
<a href="https://img95.pixhost.to/images/1092/472679347_20240330175335_zipang_xl_test3-1-fp16_1986271078.jpg" target=”_blank”>
<div>
<img src="https://t95.pixhost.to/thumbs/1092/472679347_20240330175335_zipang_xl_test3-1-fp16_1986271078.jpg" alt="sample6" class="thumbwidth" >
</div>
</a>
</td>
</tr>
</table>
-refer to pnginfo
<br />
---
# - base model
-<a href="https://huggingface.co/cagliostrolab/animagine-xl-3.1">Animagine XL 3.1</a>
-great XL model!<br />
<br />
## - trigger
```bash
japanese
european
yo tag and myjs, myjc, myjk (e.g.:18yo, myjk)
```
<br />
## - quality tags
```bash
masterpiece, best quality, very aesthetic, absurdres,
```
<br />
## - negative tags
```bash
lowres, (bad), error, fewer, extra, missing, worst quality, low quality, extra digits
```
<br />
## - sampler
```bash
DPM++ 2M SDE Heun Karras :24-28 steps
(Lightning)DPM++ 2M SGMUniform :8-14 steps
(Hyper-SD XL 8steps)DPM++ 2s a,DPM++ 2M SGM Uniform ;8-16 steps (Bad)
```
<br />
## - cfg scale
```bash
3-5
(Lightning):1-3
(Hyper-SD XL):1
```
<br />
## - Resolution
```bash
768 x1024, 1024 x 768
1024 x 1024
896 x 1152, 1152 x 896
512 x 768, 768 x 512 (hires.fix required)
```
<br />
# Sample prompt
-[SDXL Lightning](https://huggingface.co/ByteDance/SDXL-Lightning) (too fast: recommended)
[<img src=https://t95.pixhost.to/thumbs/1092/472679384_20240528182902_zipang_xl_test3-1-fp16_2770074115.jpg class="thumbwidth" />](https://img95.pixhost.to/images/1092/472679384_20240528182902_zipang_xl_test3-1-fp16_2770074115.jpg)
```bash
masterpiece, best quality, very aesthetic, absurdres,
16yo, japanese, myjk, 1girl, solo, backlighting, silhouette, horizon, sunset, wind, water, outdoors, wading, scenery, white dress, photo background, skirt hold, realistic, long hair, standing, barefoot, from behind, facing away, close-up
Negative prompt: lowres, (bad), error, fewer, extra, missing, worst quality, low quality, extra digits
Steps: 24, Sampler: DPM++ 2M SDE Heun Karras, CFG scale: 3, Seed: 2770074115, Size: 896x1152, Model hash: 90bbe169ac, Model: zipang_XL_test3.1.fp16, Denoising strength: 0.3, Hires upscale: 2, Hires upscaler: 4x-UltraSharp, Version: f0.0.17v1.8.0rc-latest-276-g29be1da7
```
<br />
<br />
-train<br />
```bash
base1:trained 4000+images:manual tagging (Prodigy:70epoch)
model:trained 10000+images:wd14 tagger(swinv2 tagger v3) (base model:base1)(Nadam:lr:1e-04:27epoch)
```
<br />
<br />
---
<a id="potest2"></a>
<h1 class="title">
<span>Ponypang XL giveup</span>
</h1>
<div style="font-size: x-large; color:red">This is an incomplete model.</div>
<br />
Mixed LoRA due to problems with training not being able to continue.<br />
e621 tag might be better than danbooru tag.<br />
<br />
<br />
-4000+ twitter images trained & 10000+ images merged model<br/>
<br/>
-experimental<br/>
-Might look like Zipang.<br />
-Hand is not good.<br />
-ADetailer and HiresFix Recommendation.
<br />
<br />
[Download: fix5](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/ponypang_XL_giveup_fix5.fp16.safetensors?download=true)<br/>
[Download: fix4](https://huggingface.co/deadman44/SDXL_Photoreal_Merged_Models/resolve/main/ponypang_XL_giveup_fix4.fp16.safetensors?download=true)<br/>
<br/>
These images are fix4.<br/>
<table>
<tr>
<td>
<a href="https://img96.pixhost.to/images/373/478078979_20240614200500_ponypang_xl_giveup_fix4-fp16_872214255.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478078979_20240614200500_ponypang_xl_giveup_fix4-fp16_872214255.jpg" alt="sample1">
</div>
</a>
<a href="https://img96.pixhost.to/images/373/478078992_20240614201423_ponypang_xl_giveup_fix4-fp16_4257880844.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478078992_20240614201423_ponypang_xl_giveup_fix4-fp16_4257880844.jpg" alt="sample2">
</div>
</a>
</td>
<td>
<a href="https://img96.pixhost.to/images/373/478079010_20240614210742_ponypang_xl_giveup_fix4-fp16_2135390268.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478079010_20240614210742_ponypang_xl_giveup_fix4-fp16_2135390268.jpg" alt="sample3">
</div>
</a>
<a href="https://img96.pixhost.to/images/373/478079001_20240614205526_ponypang_xl_giveup_fix4-fp16_1127318035.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478079001_20240614205526_ponypang_xl_giveup_fix4-fp16_1127318035.jpg" alt="sample4">
</td>
</div>
</a>
<td>
<a href="https://img96.pixhost.to/images/373/478078971_20240614195456_ponypang_xl_giveup_fix4-fp16_3153432527.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478078971_20240614195456_ponypang_xl_giveup_fix4-fp16_3153432527.jpg" alt="sample5">
</div>
</a>
<a href="https://img96.pixhost.to/images/373/478078996_20240614202407_ponypang_xl_giveup_fix4-fp16_1774986372.jpg" target=”_blank”>
<div>
<img src="https://t96.pixhost.to/thumbs/373/478078996_20240614202407_ponypang_xl_giveup_fix4-fp16_1774986372.jpg" alt="sample6">
</div>
</a>
</td>
</tr>
</table>
-refer to pnginfo
---
# - base model
-<a href="https://civitai.com/models/257749/pony-diffusion-v6-xl">Pony Diffusion V6 XL</a>
-great XL model!<br />
-<a href="https://huggingface.co/tsukihara/xl_model">ebara pony 2.1</a>
-great XL model! too<br />
<br />
## - trigger
```bash
japanese
european
yo tag and myjs, myjc, myjk (e.g.:18yo, myjk)
```
<br />
## - quality tags
```bash
score_9, score_8_up, score_7_up, masterpiece, best quality, realistic, photorealistic,
```
<br />
## - negative tags
```bash
worst quality, low quality, normal quality, messy drawing, amateur drawing, lowres, bad anatomy, bad hands, source furry, source pony, source cartoon, comic, source filmmaker, 3d, \(bad\), error, fewer, missing, extra digits
```
<br />
<br />
<br />
## - sampler
```bash
DPM++ 2M SDE Heun Karras :24-28 steps
DPM++ SGM Uniform, DPM++ 2s a :8-16 steps (HyperSDXL)
```
<a href="https://huggingface.co/ByteDance/Hyper-SD">Hyper-SDXL-8steps-lora</a>
<br />
## - cfg scale
```bash
3-7
1-1.5 (HyperSDXL)
```
<br />
## - Resolution
```bash
768 x1024, 1024 x 768
1024 x 1024
896 x 1152, 1152 x 896
512 x 768, 768 x 512 (hires.fix required)
```
<br />
# sample prompt
[<img src=https://t96.pixhost.to/thumbs/373/478079012_20240614211529_ponypang_xl_giveup_fix4-fp16_2317472872.jpg />](https://img96.pixhost.to/images/373/478079012_20240614211529_ponypang_xl_giveup_fix4-fp16_2317472872.jpg)
```bash
score_9, score_8_up, score_7_up, masterpiece, best quality,
18yo, myjk, japanese, realistic, photorealistic,
1girl, solo, black hair, long hair, bangs, brown eyes, animal ears,school uniform,
stuffed toy, stuffed animal, teddy bear,
looking at viewer, grin, holding stuffed toy, upper body, sitting, hairband
Negative prompt: worst quality, low quality, normal quality, messy drawing, amateur drawing, lowres, bad anatomy, bad hands, source furry, source pony, source cartoon, comic, source filmmaker, 3d, \(bad\), error, fewer, missing, extra digits
Steps: 28, Sampler: DPM++ 2M SDE Heun Karras, CFG scale: 4, Seed: 2317472872, Size: 896x1152, Model hash: 58cd1b19e0, Model: ponypang_XL_giveup_fix4.fp16, Version: f0.0.17v1.8.0rc-latest-287-g77bdb9208
```
<br />
-merge & train<br />
```bash
base1:ebara pony 2.1 base Trained.
base2:base1 x ebara base LoRA (1,1,1,1,0.3,0.3,0.3,0.3,0.5,0.75,0,0,0,0.75,1,0.9,0.5,0.3,0.3,0.3,1,1,0,0,0,0)
base3:Merge Zipang test3.1 (0,0,0,0,0,0,0,0,0,0.5,0,0.3,0,0.1,0,0,0,0,0,0)
base4:base3 x pony base LoRA (0,0,0,0,0,0.1,0.25,0,0,0.7,0,0,0,0.5,0.25,1,0,0.1,0.75,0.65,0,0,0,0,0,0)
fix: base4 base trained (Lion 1e-07)
```
<br /> | [
"BEAR"
] |
ostapeno/library-phi_2-v3-25-flan-clusters | ostapeno | null | [
"region:us"
] | "2024-01-18T12:59:52Z" | 2024-01-18T13:00:11+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 25
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_lora_embed_25clustersc0o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_rationale,cos_e_v1_11_generate_explanation_given_text,cos_e_v1_11_i_think,cos_e_v1_11_explain_why_human,cot_ecqa,cos_e_v1_11_aligned_with_common_sense | lora |
| phi2_joint_lora_embed_25clustersc10o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/stream_qed,huggingface_xsum,cot_gsm8k,gem_wiki_lingua_english_en_1_1_0,gigaword_1_2_0,aeslc_1_0_0,cot_esnli_ii,coqa_1_0_0 | lora |
| phi2_joint_lora_embed_25clustersc8o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| phi2_joint_lora_embed_25clustersc23o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,adversarial_qa_dbidaf_generate_question,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,adversarial_qa_dbert_generate_question | lora |
| phi2_joint_lora_embed_25clustersc17o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_bio_key_content,wiki_bio_what_content,wiki_bio_who,wiki_bio_comprehension,multi_news_1_0_0 | lora |
| phi2_joint_lora_embed_25clustersc24o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,super_glue_wic_1_0_2,anli_r3_0_1_0,paws_wiki_1_1_0,wiki_qa_Is_This_True_,glue_qqp_2_0_0,qasc_is_correct_1,glue_qnli_2_0_0,glue_mnli_2_0_0,qasc_is_correct_2,glue_mrpc_2_0_0 | lora |
| phi2_joint_lora_embed_25clustersc15o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_based_on,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,quartz_use_info_from_question_paragraph,quartz_use_info_from_paragraph_question,quartz_paragraph_question_plain_concat | lora |
| phi2_joint_lora_embed_25clustersc18o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0,gem_common_gen_1_1_0,gem_web_nlg_en_1_1_0,app_reviews_generate_review,gem_e2e_nlg_1_1_0 | lora |
| phi2_joint_lora_embed_25clustersc6o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,sciq_Direct_Question,qasc_qa_with_separated_facts_2,cos_e_v1_11_question_option_description_id,qasc_qa_with_separated_facts_5,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,sciq_Multiple_Choice_Closed_Book_,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_lora_embed_25clustersc22o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa,duorc_ParaphraseRC_build_story_around_qa | lora |
| phi2_joint_lora_embed_25clustersc9o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,social_i_qa_Show_choices_and_generate_answer,app_reviews_categorize_rating_using_review,kilt_tasks_hotpotqa_final_exam,wiqa_does_the_supposed_perturbation_have_an_effect,cot_gsm8k_ii,super_glue_multirc_1_0_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,wiqa_effect_with_string_answer,kilt_tasks_hotpotqa_complex_question,wiki_qa_automatic_system,cot_creak_ii,cot_sensemaking_ii,social_i_qa_Check_if_a_random_answer_is_valid_or_not,social_i_qa_Generate_the_question_from_the_answer | lora |
| phi2_joint_lora_embed_25clustersc11o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/cot_sensemaking,cot_creak,stream_aqua,snli_1_1_0,glue_stsb_2_0_0,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_lora_embed_25clustersc4o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/super_glue_record_1_0_2,quoref_Guess_Title_For_Context,quac_1_0_0,squad_v2_0_3_0_0,wiki_bio_guess_person,drop_2_0_0,squad_v1_1_3_0_0 | lora |
| phi2_joint_lora_embed_25clustersc12o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/lambada_1_0_0,dream_generate_last_utterance,cnn_dailymail_3_4_0,race_middle_Write_a_multi_choice_question_for_the_following_article,race_high_Write_a_multi_choice_question_for_the_following_article,dream_generate_first_utterance,dream_answer_to_dialogue,race_high_Write_a_multi_choice_question_options_given_,race_middle_Write_a_multi_choice_question_options_given_ | lora |
| phi2_joint_lora_embed_25clustersc21o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,race_middle_Is_this_the_right_answer,race_high_Is_this_the_right_answer,quarel_do_not_use,dream_baseline,quarel_heres_a_story,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,quarel_testing_students,wiqa_effect_with_label_answer,cot_qasc,quarel_logic_test,stream_aqua_ii | lora |
| phi2_joint_lora_embed_25clustersc20o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,quoref_Found_Context_Online,quoref_Read_And_Extract_,quoref_What_Is_The_Answer,quoref_Answer_Test,quoref_Answer_Question_Given_Context,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,quoref_Given_Context_Answer_Question | lora |
| phi2_joint_lora_embed_25clustersc7o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0,web_questions_whats_the_answer,web_questions_question_answer,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,web_questions_get_the_answer,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,kilt_tasks_hotpotqa_formulate | lora |
| phi2_joint_lora_embed_25clustersc14o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_object,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_explain_relation,wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_generate_subject,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object | lora |
| phi2_joint_lora_embed_25clustersc13o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0,wmt16_translate_de_en_1_0_0,wmt16_translate_fi_en_1_0_0,para_crawl_enes,wmt14_translate_fr_en_1_0_0,wmt16_translate_tr_en_1_0_0 | lora |
| phi2_joint_lora_embed_25clustersc1o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_lora_embed_25clustersc2o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,true_case,cot_esnli,trec_1_0_0,yelp_polarity_reviews_0_2_0,glue_cola_2_0_0,ag_news_subset_1_0_0,math_dataset_algebra__linear_1d_1_0_0,fix_punct,imdb_reviews_plain_text_1_0_0,word_segment,anli_r2_0_1_0,anli_r1_0_1_0 | lora |
| phi2_joint_lora_embed_25clustersc3o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,wiqa_what_might_be_the_last_step_of_the_process,wiqa_what_is_the_missing_first_step | lora |
| phi2_joint_lora_embed_25clustersc19o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,adversarial_qa_droberta_tell_what_it_is,adversarial_qa_droberta_based_on,adversarial_qa_dbert_answer_the_following_q,adversarial_qa_droberta_question_context_answer,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora |
| phi2_joint_lora_embed_25clustersc5o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,unified_qa_science_inst,cot_strategyqa,cot_ecqa_ii,wiki_qa_exercise,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,super_glue_copa_1_0_2,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,glue_wnli_2_0_0,cot_strategyqa_ii | lora |
| phi2_joint_lora_embed_25clustersc16o25_2e_3epoch | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer,duorc_ParaphraseRC_answer_question,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,duorc_ParaphraseRC_generate_question,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,duorc_SelfRC_extract_answer,duorc_SelfRC_generate_question | lora |
Last updated on: 2024-01-18 12:59:52+00:00
| [
"SCIQ"
] |
ostapeno/library-phi_2-v3-10-rand-flan-clusters | ostapeno | null | [
"region:us"
] | "2024-01-19T15:28:16Z" | 2024-01-19T15:28:53+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_10randomclusterscluster_6_3epoch | phi-2 | sordonia/flan-10k-flat/multi_news_1_0_0,para_crawl_enes,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_id,glue_stsb_2_0_0,quail_context_description_question_answer_text,dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_description_option_text,cos_e_v1_11_description_question_option_text,race_high_Write_a_multi_choice_question_options_given_,ropes_prompt_bottom_no_hint,quarel_testing_students,wmt16_translate_tr_en_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_final_step_of_the_following_process,quoref_Given_Context_Answer_Question,wiki_hop_original_generate_object,quartz_use_info_from_question_paragraph,duorc_SelfRC_build_story_around_qa,drop_2_0_0,wiqa_effect_with_string_answer,race_high_Taking_a_test,wiki_hop_original_generate_subject_and_object,glue_qqp_2_0_0,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| phi2_joint_10randomclusterscluster_7_3epoch | phi-2 | sordonia/flan-10k-flat/cot_creak_ii,quail_description_context_question_answer_id,kilt_tasks_hotpotqa_final_exam,cot_gsm8k,cos_e_v1_11_aligned_with_common_sense,squad_v2_0_3_0_0,duorc_SelfRC_movie_director,anli_r2_0_1_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,duorc_ParaphraseRC_answer_question,ropes_background_situation_middle,dream_generate_first_utterance,quail_context_question_description_text,adversarial_qa_droberta_based_on,glue_wnli_2_0_0,super_glue_record_1_0_2,web_questions_get_the_answer,ropes_prompt_mix,app_reviews_generate_review,cos_e_v1_11_rationale,adversarial_qa_droberta_generate_question,yelp_polarity_reviews_0_2_0,ropes_given_background_situation,qasc_qa_with_separated_facts_3,quoref_Guess_Answer | lora |
| phi2_joint_10randomclusterscluster_4_3epoch | phi-2 | sordonia/flan-10k-flat/quoref_Found_Context_Online,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_explain_why_human,cot_sensemaking,quoref_Find_Answer,quail_context_description_question_text,social_i_qa_Generate_the_question_from_the_answer,quartz_having_read_above_passage,stream_qed_ii,wiqa_what_might_be_the_first_step_of_the_process,wiki_bio_who,duorc_SelfRC_generate_question_by_answer,race_high_Select_the_best_answer_generate_span_,lambada_1_0_0,coqa_1_0_0,race_high_Select_the_best_answer,adversarial_qa_droberta_question_context_answer,quail_context_question_answer_description_id,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_tell_what_it_is,cnn_dailymail_3_4_0,quail_no_prompt_id,ag_news_subset_1_0_0,trivia_qa_rc_1_1_0,ropes_prompt_bottom_hint_beginning,super_glue_wic_1_0_2 | lora |
| phi2_joint_10randomclusterscluster_1_3epoch | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0,quarel_choose_between,wiki_hop_original_explain_relation,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,race_high_Select_the_best_answer_no_instructions_,kilt_tasks_hotpotqa_combining_facts,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_generate_question,glue_cola_2_0_0,imdb_reviews_plain_text_1_0_0,squad_v1_1_3_0_0,race_high_Is_this_the_right_answer,qasc_qa_with_separated_facts_1,glue_sst2_2_0_0,wiqa_what_is_the_missing_first_step,duorc_ParaphraseRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,wiki_qa_Jeopardy_style,quartz_answer_question_below,ropes_prompt_beginning,ropes_read_background_situation,wiki_qa_Decide_good_answer,super_glue_cb_1_0_2,qasc_qa_with_separated_facts_4,cot_ecqa_ii | lora |
| phi2_joint_10randomclusterscluster_3_3epoch | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5,wiki_qa_automatic_system,stream_aqua_ii,dbpedia_14_pick_one_category_for_the_following_text,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Generate_Question_from_Topic,cot_gsm8k_ii,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_interrogative_2,quail_context_question_answer_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,qasc_qa_with_combined_facts_1,adversarial_qa_dbert_answer_the_following_q,social_i_qa_I_was_wondering,stream_aqua,word_segment,ropes_plain_no_background,super_glue_multirc_1_0_2,wiki_hop_original_choose_best_object_affirmative_3,app_reviews_convert_to_rating,anli_r3_0_1_0,app_reviews_convert_to_star_rating,quartz_paragraph_question_plain_concat,kilt_tasks_hotpotqa_complex_question,quartz_use_info_from_paragraph_question | lora |
| phi2_joint_10randomclusterscluster_2_3epoch | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id,social_i_qa_Show_choices_and_generate_index,web_questions_potential_correct_answer,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cosmos_qa_1_0_0,sciq_Direct_Question,super_glue_wsc_fixed_1_0_2,race_middle_Taking_a_test,wmt14_translate_fr_en_1_0_0,duorc_SelfRC_extract_answer,wiki_hop_original_generate_subject,duorc_SelfRC_answer_question,qasc_is_correct_1,cos_e_v1_11_i_think,wiki_qa_exercise,race_middle_Write_a_multi_choice_question_options_given_,quoref_Read_And_Extract_,web_questions_short_general_knowledge_q,web_questions_question_answer,quarel_logic_test,app_reviews_categorize_rating_using_review,cot_strategyqa_ii,glue_mnli_2_0_0,quoref_Answer_Test,super_glue_rte_1_0_2 | lora |
| phi2_joint_10randomclusterscluster_10_3epoch | phi-2 | sordonia/flan-10k-flat/stream_qed,cot_esnli_ii,quarel_heres_a_story,quoref_Guess_Title_For_Context,qasc_is_correct_2,wiqa_effect_with_label_answer,dream_generate_last_utterance,adversarial_qa_dbert_based_on,dream_answer_to_dialogue,sciq_Multiple_Choice_Question_First,quail_context_question_description_answer_text,wiki_qa_Direct_Answer_to_Question,ropes_background_new_situation_answer,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,quoref_What_Is_The_Answer,dbpedia_14_given_a_choice_of_categories_,qasc_qa_with_separated_facts_2,glue_mrpc_2_0_0,gem_e2e_nlg_1_1_0,anli_r1_0_1_0,race_middle_Read_the_article_and_answer_the_question_no_option_,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,adversarial_qa_dbidaf_based_on | lora |
| phi2_joint_10randomclusterscluster_9_3epoch | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article,cot_qasc,definite_pronoun_resolution_1_1_0,wiki_qa_found_on_google,wiki_bio_comprehension,wiki_qa_Topic_Prediction_Question_Only,wiki_bio_guess_person,fix_punct,race_middle_Select_the_best_answer_no_instructions_,quac_1_0_0,wiqa_does_the_supposed_perturbation_have_an_effect,quartz_given_the_fact_answer_the_q,ropes_new_situation_background_answer,social_i_qa_Generate_answer,gigaword_1_2_0,duorc_SelfRC_decide_worth_it,kilt_tasks_hotpotqa_straighforward_qa,quail_no_prompt_text,cot_esnli,quoref_Answer_Friend_Question,race_middle_Select_the_best_answer_generate_span_,unified_qa_science_inst,sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_strategyqa | lora |
| phi2_joint_10randomclusterscluster_8_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2,duorc_ParaphraseRC_movie_director,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,quartz_read_passage_below_choose,social_i_qa_Check_if_a_random_answer_is_valid_or_not,cot_creak,ropes_plain_bottom_hint,super_glue_copa_1_0_2,natural_questions_open_1_0_0,trec_1_0_0,gem_web_nlg_en_1_1_0,wiki_bio_key_content,wmt16_translate_fi_en_1_0_0,quoref_Answer_Question_Given_Context,duorc_ParaphraseRC_generate_question,math_dataset_algebra__linear_1d_1_0_0,duorc_ParaphraseRC_title_generation,quail_context_description_question_answer_id,wiki_bio_what_content,adversarial_qa_dbert_tell_what_it_is,sciq_Multiple_Choice_Closed_Book_,duorc_ParaphraseRC_extract_answer,dream_baseline,gem_wiki_lingua_english_en_1_1_0 | lora |
| phi2_joint_10randomclusterscluster_5_3epoch | phi-2 | sordonia/flan-10k-flat/snli_1_1_0,duorc_SelfRC_question_answering,cot_sensemaking_ii,huggingface_xsum,duorc_ParaphraseRC_build_story_around_qa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,quartz_answer_question_based_on,ropes_plain_background_situation,race_middle_Write_a_multi_choice_question_for_the_following_article,quail_description_context_question_text,web_questions_whats_the_answer,cot_ecqa,true_case,adversarial_qa_dbert_question_context_answer,duorc_SelfRC_title_generation,quail_context_question_description_answer_id,quarel_do_not_use,adversarial_qa_dbidaf_answer_the_following_q,duorc_ParaphraseRC_decide_worth_it,race_middle_Is_this_the_right_answer,wmt16_translate_de_en_1_0_0,wiki_qa_Is_This_True_,race_middle_Select_the_best_answer,aeslc_1_0_0,duorc_ParaphraseRC_question_answering,wiki_hop_original_choose_best_object_affirmative_1 | lora |
Last updated on: 2024-01-19 15:28:16+00:00
| [
"SCIQ"
] |
EleutherAI/pythia-410m-sciq | EleutherAI | null | [
"safetensors",
"en",
"arxiv:2312.01037",
"license:apache-2.0",
"region:us"
] | "2024-01-19T17:10:20Z" | 2024-02-07T00:07:52+00:00 | 0 | 0 | ---
language:
- en
license: apache-2.0
---
# Model Card for pythia-410m-sciq
A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
## Model Details
### Model Description
This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
### Model Sources [optional]
- **Repository:** https://github.com/EleutherAI/elk-generalization
## Uses
This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
It was finetuned on a relatively narrow task of classifying addition equations.
## Bias, Risks, and Limitations
Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
We invite contributions of new quirky datasets and models.
### Training Procedure
This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
#### Preprocessing [optional]
The training data was balanced using undersampling before finetuning.
## Evaluation
This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
## Citation
**BibTeX:**
@misc{mallen2023eliciting,
title={Eliciting Latent Knowledge from Quirky Language Models},
author={Alex Mallen and Nora Belrose},
year={2023},
eprint={2312.01037},
archivePrefix={arXiv},
primaryClass={cs.LG\}
}
| [
"SCIQ"
] |
zhan1993/library-phi_2-v3-10-sim-clusters | zhan1993 | null | [
"region:us"
] | "2024-01-19T19:00:56Z" | 2024-01-19T19:01:08+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0,quoref_Found_Context_Online,duorc_SelfRC_generate_question_by_answer,quoref_What_Is_The_Answer,duorc_ParaphraseRC_extract_answer,quoref_Guess_Title_For_Context,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_answer_question,duorc_SelfRC_extract_answer,drop_2_0_0,quoref_Read_And_Extract_,duorc_SelfRC_question_answering,squad_v1_1_3_0_0,duorc_ParaphraseRC_title_generation,quoref_Find_Answer,duorc_ParaphraseRC_generate_question_by_answer,duorc_SelfRC_title_generation,duorc_SelfRC_generate_question,duorc_SelfRC_movie_director,quoref_Given_Context_Answer_Question,duorc_SelfRC_answer_question,quoref_Answer_Question_Given_Context,quoref_Context_Contains_Answer,duorc_ParaphraseRC_movie_director,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_ParaphraseRC_generate_question,duorc_ParaphraseRC_decide_worth_it,quoref_Answer_Test,duorc_ParaphraseRC_question_answering,quac_1_0_0 | lora |
| cluster_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_generate_object,wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_generate_subject,wiki_hop_original_generate_subject_and_object,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_explain_relation | lora |
| cluster_5 | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test,race_high_Read_the_article_and_answer_the_question_no_option_,quail_no_prompt_id,quail_context_question_answer_description_id,race_high_Select_the_best_answer,race_high_Select_the_best_answer_no_instructions_,quail_no_prompt_text,quail_context_question_description_answer_text,race_middle_Select_the_best_answer,quail_context_description_question_answer_id,quail_context_description_question_answer_text,quail_description_context_question_answer_id,quail_context_question_description_answer_id,race_middle_Select_the_best_answer_no_instructions_,quail_description_context_question_text,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_text,quail_context_question_description_text,race_middle_Taking_a_test,race_middle_Read_the_article_and_answer_the_question_no_option_,quail_description_context_question_answer_text,quail_context_description_question_text,race_high_Select_the_best_answer_generate_span_ | lora |
| cluster_8 | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_,qasc_is_correct_1,cosmos_qa_1_0_0,app_reviews_convert_to_star_rating,glue_qqp_2_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,glue_stsb_2_0_0,paws_wiki_1_1_0,anli_r3_0_1_0,app_reviews_convert_to_rating,snli_1_1_0,qasc_is_correct_2,super_glue_multirc_1_0_2,super_glue_wic_1_0_2,cot_creak,social_i_qa_Check_if_a_random_answer_is_valid_or_not,super_glue_rte_1_0_2,glue_mrpc_2_0_0,cot_creak_ii | lora |
| cluster_6 | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question,adversarial_qa_droberta_generate_question,gem_web_nlg_en_1_1_0,wmt16_translate_de_en_1_0_0,cnn_dailymail_3_4_0,trec_1_0_0,yelp_polarity_reviews_0_2_0,para_crawl_enes,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,glue_sst2_2_0_0,cot_esnli,math_dataset_algebra__linear_1d_1_0_0,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_options_given_,lambada_1_0_0,fix_punct,dream_generate_first_utterance,aeslc_1_0_0,anli_r2_0_1_0,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_gsm8k,anli_r1_0_1_0,ag_news_subset_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,wmt14_translate_fr_en_1_0_0,true_case,race_high_Write_a_multi_choice_question_for_the_following_article,huggingface_xsum,wmt16_translate_fi_en_1_0_0,stream_qed,cot_ecqa,gigaword_1_2_0,dream_answer_to_dialogue,word_segment,glue_cola_2_0_0 | lora |
| cluster_1 | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice,qasc_qa_with_separated_facts_1,quartz_use_info_from_paragraph_question,qasc_qa_with_separated_facts_2,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,cos_e_v1_11_question_option_description_text,wiqa_effect_with_string_answer,wiqa_does_the_supposed_perturbation_have_an_effect,qasc_qa_with_separated_facts_5,quartz_read_passage_below_choose,qasc_qa_with_separated_facts_3,dream_baseline,cos_e_v1_11_question_description_option_text,sciq_Direct_Question_Closed_Book_,quartz_answer_question_below,sciq_Multiple_Choice_Closed_Book_,qasc_qa_with_separated_facts_4,quartz_given_the_fact_answer_the_q,sciq_Direct_Question,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_description_question_option_text,quartz_answer_question_based_on,cos_e_v1_11_description_question_option_id,sciq_Multiple_Choice_Question_First,dream_read_the_following_conversation_and_answer_the_question,qasc_qa_with_combined_facts_1,cos_e_v1_11_question_option_description_id,quartz_paragraph_question_plain_concat,wiqa_which_of_the_following_is_the_supposed_perturbation,quartz_use_info_from_question_paragraph | lora |
| cluster_4 | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise,quarel_testing_students,cot_gsm8k_ii,unified_qa_science_inst,wiki_qa_found_on_google,super_glue_cb_1_0_2,stream_aqua_ii,quarel_logic_test,super_glue_copa_1_0_2,wiqa_effect_with_label_answer,stream_qed_ii,cot_sensemaking_ii,wiki_qa_Jeopardy_style,cot_qasc,wiki_qa_Topic_Prediction_Question_Only,race_middle_Is_this_the_right_answer,app_reviews_categorize_rating_using_review,wiki_qa_Decide_good_answer,quarel_choose_between,race_high_Is_this_the_right_answer,cot_strategyqa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Direct_Answer_to_Question,cot_ecqa_ii,quarel_do_not_use,wiki_qa_Generate_Question_from_Topic,social_i_qa_Generate_the_question_from_the_answer,quarel_heres_a_story,cot_strategyqa_ii,kilt_tasks_hotpotqa_complex_question,definite_pronoun_resolution_1_1_0,wiki_qa_Topic_Prediction_Answer_Only,glue_wnli_2_0_0,wiki_qa_automatic_system,super_glue_wsc_fixed_1_0_2,social_i_qa_Show_choices_and_generate_index | lora |
| cluster_10 | phi-2 | sordonia/flan-10k-flat/ropes_plain_bottom_hint,ropes_prompt_mix,ropes_read_background_situation,ropes_given_background_situation,ropes_prompt_bottom_no_hint,ropes_background_new_situation_answer,ropes_prompt_beginning,ropes_background_situation_middle,ropes_new_situation_background_answer,ropes_plain_background_situation,ropes_plain_no_background,ropes_prompt_bottom_hint_beginning | lora |
| cluster_9 | phi-2 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0,wiki_bio_comprehension,wiki_bio_what_content,wiqa_what_is_the_missing_first_step,gem_common_gen_1_1_0,app_reviews_generate_review,duorc_SelfRC_build_story_around_qa,cot_esnli_ii,coqa_1_0_0,wiki_bio_key_content,wmt16_translate_tr_en_1_0_0,duorc_ParaphraseRC_build_story_around_qa,wiqa_what_is_the_final_step_of_the_following_process,gem_dart_1_1_0,wmt16_translate_ro_en_1_0_0,wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_might_be_the_last_step_of_the_process,multi_news_1_0_0,gem_e2e_nlg_1_1_0,wiki_bio_who | lora |
| cluster_7 | phi-2 | sordonia/flan-10k-flat/web_questions_get_the_answer,adversarial_qa_dbidaf_question_context_answer,web_questions_whats_the_answer,adversarial_qa_droberta_question_context_answer,kilt_tasks_hotpotqa_combining_facts,cos_e_v1_11_aligned_with_common_sense,web_questions_potential_correct_answer,adversarial_qa_droberta_tell_what_it_is,race_high_Write_a_multi_choice_question_options_given_,stream_aqua,adversarial_qa_dbidaf_answer_the_following_q,social_i_qa_Generate_answer,adversarial_qa_dbidaf_based_on,cos_e_v1_11_i_think,natural_questions_open_1_0_0,cos_e_v1_11_rationale,wiki_bio_guess_person,trivia_qa_rc_1_1_0,web_questions_question_answer,social_i_qa_I_was_wondering,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,super_glue_record_1_0_2,adversarial_qa_dbidaf_tell_what_it_is,web_questions_short_general_knowledge_q,dbpedia_14_given_a_choice_of_categories_,adversarial_qa_dbert_based_on,cos_e_v1_11_generate_explanation_given_text,cos_e_v1_11_explain_why_human,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,dbpedia_14_pick_one_category_for_the_following_text,kilt_tasks_hotpotqa_final_exam,adversarial_qa_dbert_question_context_answer,cot_sensemaking,dream_generate_last_utterance,kilt_tasks_hotpotqa_formulate,adversarial_qa_droberta_based_on | lora |
Last updated on: 2024-01-19T19:00:59.000Z
| [
"SCIQ"
] |
espnet/interspeech2024_dsuchallenge_wavlm_large_21_baseline | espnet | automatic-speech-recognition | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:interspeech2024_dsu_challenge",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | "2024-01-20T21:38:20Z" | 2024-01-20T21:44:55+00:00 | 0 | 0 | ---
datasets:
- interspeech2024_dsu_challenge
language: en
license: cc-by-4.0
tags:
- espnet
- audio
- automatic-speech-recognition
---
## ESPnet2 ASR model
### `espnet/interspeech2024_dsuchallenge_wavlm_large_21_baseline`
This model was trained by simpleoier using interspeech2024_dsu_challenge recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 0d77ccfd8d980a996ac821253234a67a15f63129
pip install -e .
cd egs2/interspeech2024_dsu_challenge/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/interspeech2024_dsuchallenge_wavlm_large_21_baseline
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Jan 17 08:22:49 EST 2024`
- python version: `3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]`
- espnet version: `espnet 202310`
- pytorch version: `pytorch 1.13.1`
- Git hash: ``
- Commit date: ``
## exp/asr_train_discrete_asr_e_branchformer1_1gpu_lr5e-4_warmup5k_raw_wavlm_large_21_km2000_bpe_rm3000_bpe_ts6000
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|54402|95.9|3.9|0.2|0.4|4.5|48.2|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|50948|92.5|6.9|0.6|0.6|8.1|60.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_1h|7439|57426|14.5|61.3|24.2|14.8|100.3|98.0|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|52576|96.0|3.8|0.3|0.4|4.4|47.6|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|52343|92.4|7.0|0.6|0.6|8.3|63.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|288456|98.9|0.7|0.5|0.4|1.5|48.2|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|265951|97.5|1.4|1.0|0.7|3.2|60.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_1h|7439|299326|44.4|28.4|27.2|17.0|72.6|98.0|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|281530|98.9|0.6|0.5|0.4|1.4|47.6|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|272758|97.6|1.4|1.0|0.7|3.1|63.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|82834|95.2|3.5|1.3|0.5|5.3|48.2|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|76205|91.6|6.4|2.0|1.1|9.5|60.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_1h|7439|159974|26.2|48.4|25.4|15.0|88.8|98.0|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|81195|95.6|3.2|1.2|0.5|4.9|47.6|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|78676|91.6|6.2|2.2|1.0|9.5|63.0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_discrete_asr_e_branchformer1_1gpu_lr5e-4_warmup5k.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_discrete_asr_e_branchformer1_1gpu_lr5e-4_warmup5k_raw_wavlm_large_21_km2000_bpe_rm3000_bpe_ts6000
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 1000
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 120000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe3000_bpe6000/train/src_text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe3000_bpe6000/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe3000_bpe6000/valid/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe3000_bpe6000/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/train/text.rm.wavlm_large_21_km2000
- src_text
- text
- - dump/raw/train/text.ts.en
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/text.ts.en
- text
- text
- - dump/raw/dev/text.rm.wavlm_large_21_km2000
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.0005
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 5000
token_list:
- <blank>
- <unk>
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- 丬
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- 啽
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- 咃
- 去
- 唓
- 啞
- 卽
- 哄
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- 咈
- 伵
- 啣
- 唤
- 仗
- 丕
- 严
- 咧
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
use_preprocessor: true
token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram6000_ts_en/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram3000_rm_wavlm_large_21_km2000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
tokenizer_encode_conf: null
src_tokenizer_encode_conf:
enable_sampling: true
alpha: 0.4
nbest_size: -1
frontend: embed
frontend_conf:
embed_dim: 512
positional_dropout_rate: 0.1
specaug: specaug
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: false
freq_mask_width_range:
- 0
- 10
num_freq_mask: 0
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv1d2
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.0
model: discrete_asr
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
share_decoder_input_output_embed: false
share_encoder_decoder_input_embed: false
required:
- output_dir
- src_token_list
- token_list
version: '202310'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| [
"BEAR"
] |
ostapeno/library-phi_2-v3-5-flan-clusters | ostapeno | null | [
"region:us"
] | "2024-01-20T23:46:28Z" | 2024-01-20T23:46:38+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 5
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_lora_embed_5clustersc1o5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,adversarial_qa_droberta_generate_question,true_case,stream_qed,cot_esnli,cot_sensemaking,trec_1_0_0,super_glue_wic_1_0_2,super_glue_record_1_0_2,yelp_polarity_reviews_0_2_0,cos_e_v1_11_rationale,natural_questions_open_1_0_0,web_questions_whats_the_answer,anli_r3_0_1_0,lambada_1_0_0,dream_generate_last_utterance,glue_cola_2_0_0,ag_news_subset_1_0_0,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cot_creak,math_dataset_algebra__linear_1d_1_0_0,web_questions_question_answer,stream_aqua,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,web_questions_short_general_knowledge_q,cnn_dailymail_3_4_0,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,snli_1_1_0,cos_e_v1_11_i_think,wiki_hop_original_explain_relation,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,web_questions_get_the_answer,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,cos_e_v1_11_explain_why_human,word_segment,anli_r2_0_1_0,race_high_Write_a_multi_choice_question_for_the_following_article,trivia_qa_rc_1_1_0,wmt16_translate_de_en_1_0_0,anli_r1_0_1_0,cot_ecqa,glue_stsb_2_0_0,cos_e_v1_11_aligned_with_common_sense,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,kilt_tasks_hotpotqa_formulate,para_crawl_enes,race_high_Write_a_multi_choice_question_options_given_,social_i_qa_I_was_wondering,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_lora_embed_5clustersc0o5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,super_glue_rte_1_0_2,wiki_qa_found_on_google,app_reviews_categorize_rating_using_review,race_middle_Is_this_the_right_answer,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,wiki_qa_Is_This_True_,unified_qa_science_inst,super_glue_multirc_1_0_2,app_reviews_convert_to_rating,race_high_Is_this_the_right_answer,cot_strategyqa,cot_ecqa_ii,glue_qqp_2_0_0,quarel_do_not_use,wiki_qa_exercise,kilt_tasks_hotpotqa_complex_question,wiki_qa_automatic_system,cot_creak_ii,quarel_heres_a_story,qasc_is_correct_1,quarel_choose_between,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,glue_mnli_2_0_0,super_glue_copa_1_0_2,social_i_qa_Check_if_a_random_answer_is_valid_or_not,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,qasc_is_correct_2,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,quarel_logic_test,stream_aqua_ii | lora |
| phi2_joint_lora_embed_5clustersc3o5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,huggingface_xsum,wiqa_what_is_the_final_step_of_the_following_process,cot_gsm8k,wmt16_translate_ro_en_1_0_0,gem_dart_1_1_0,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,gem_common_gen_1_1_0,duorc_SelfRC_build_story_around_qa,gem_wiki_lingua_english_en_1_1_0,gigaword_1_2_0,gem_web_nlg_en_1_1_0,app_reviews_generate_review,wiki_bio_what_content,wiki_bio_who,gem_e2e_nlg_1_1_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| phi2_joint_lora_embed_5clustersc2o5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,cos_e_v1_11_question_option_description_text,ropes_background_new_situation_answer,social_i_qa_Show_choices_and_generate_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,ropes_plain_background_situation,race_high_Read_the_article_and_answer_the_question_no_option_,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,kilt_tasks_hotpotqa_final_exam,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,sciq_Multiple_Choice,race_high_Taking_a_test,wiqa_does_the_supposed_perturbation_have_an_effect,cos_e_v1_11_question_description_option_text,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,cos_e_v1_11_question_option_description_id,ropes_new_situation_background_answer,quail_description_context_question_answer_text,ropes_given_background_situation,quail_context_question_answer_description_text,ropes_prompt_bottom_hint_beginning,wiqa_effect_with_string_answer,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,qasc_qa_with_separated_facts_5,dream_baseline,ropes_prompt_beginning,quail_context_question_answer_description_id,quartz_having_read_above_passage,quail_context_description_question_answer_text,cos_e_v1_11_question_description_option_id,ropes_read_background_situation,qasc_qa_with_separated_facts_1,ropes_plain_bottom_hint,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,ropes_plain_no_background,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,sciq_Multiple_Choice_Closed_Book_,race_middle_Taking_a_test,quartz_use_info_from_paragraph_question,qasc_qa_with_separated_facts_4,cosmos_qa_1_0_0,quail_no_prompt_id,quartz_read_passage_below_choose,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_,quartz_paragraph_question_plain_concat,ropes_prompt_mix,ropes_background_situation_middle,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_lora_embed_5clustersc4o5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,wiki_hop_original_generate_object,quoref_Found_Context_Online,adversarial_qa_droberta_tell_what_it_is,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,kilt_tasks_hotpotqa_combining_facts,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,wiki_hop_original_choose_best_object_affirmative_3,quoref_Guess_Title_For_Context,quac_1_0_0,quoref_Answer_Test,wiki_hop_original_choose_best_object_interrogative_1,duorc_SelfRC_question_answering,adversarial_qa_droberta_based_on,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,wiki_hop_original_choose_best_object_affirmative_1,duorc_ParaphraseRC_extract_answer,adversarial_qa_dbert_answer_the_following_q,duorc_SelfRC_answer_question,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,squad_v2_0_3_0_0,web_questions_potential_correct_answer,wiki_hop_original_generate_subject,wiki_bio_guess_person,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,wiki_hop_original_choose_best_object_affirmative_2,duorc_SelfRC_movie_director,adversarial_qa_dbert_based_on,duorc_SelfRC_generate_question,wiki_hop_original_generate_subject_and_object,adversarial_qa_dbidaf_based_on,drop_2_0_0,adversarial_qa_dbert_question_context_answer,quoref_Given_Context_Answer_Question,adversarial_qa_dbidaf_tell_what_it_is,squad_v1_1_3_0_0 | lora |
Last updated on: 2024-01-20 23:46:28+00:00
| [
"SCIQ"
] |
ostapeno/library-gptneo1B-10-flan-clusters | ostapeno | null | [
"region:us"
] | "2024-01-21T20:29:40Z" | 2024-01-21T20:29:59+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| gptneo_jointc_lora_embed_clustersc0o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,duorc_ParaphraseRC_generate_question,adversarial_qa_dbidaf_generate_question,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,fix_punct,imdb_reviews_plain_text_1_0_0,wiki_hop_original_choose_best_object_affirmative_1,quail_context_description_question_answer_text,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_generate_subject,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,wiki_hop_original_choose_best_object_affirmative_2,cosmos_qa_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_missing_first_step,quail_no_prompt_text | lora |
| gptneo_jointc_lora_embed_clustersc7o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,quoref_Find_Answer,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,adversarial_qa_droberta_tell_what_it_is,quoref_Read_And_Extract_,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,quoref_Guess_Title_For_Context,quoref_Answer_Test,duorc_SelfRC_question_answering,adversarial_qa_droberta_based_on,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,adversarial_qa_droberta_question_context_answer,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,adversarial_qa_dbert_tell_what_it_is,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,adversarial_qa_dbert_question_context_answer,quoref_Given_Context_Answer_Question,adversarial_qa_dbidaf_tell_what_it_is | lora |
| gptneo_jointc_lora_embed_clustersc4o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0,dream_read_the_following_conversation_and_answer_the_question,race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,true_case,quail_description_context_question_answer_id,quail_context_question_description_text,stream_qed,cot_esnli,quoref_Context_Contains_Answer,race_high_Read_the_article_and_answer_the_question_no_option_,duorc_ParaphraseRC_movie_director,quail_context_description_question_answer_id,ag_news_subset_1_0_0,wiki_hop_original_generate_object,race_high_Taking_a_test,cos_e_v1_11_question_description_option_text,kilt_tasks_hotpotqa_combining_facts,race_middle_Select_the_best_answer,kilt_tasks_hotpotqa_straighforward_qa,quail_context_question_description_answer_id,quac_1_0_0,wiki_hop_original_explain_relation,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,dream_baseline,adversarial_qa_dbert_answer_the_following_q,quail_context_question_answer_description_id,app_reviews_generate_review,wiki_bio_what_content,quoref_Answer_Question_Given_Context,trivia_qa_rc_1_1_0,wiki_bio_guess_person,glue_stsb_2_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,gem_e2e_nlg_1_1_0,race_middle_Taking_a_test,duorc_SelfRC_movie_director,quail_no_prompt_id,wiki_hop_original_generate_subject_and_object,race_middle_Select_the_best_answer_no_instructions_,squad_v1_1_3_0_0 | lora |
| gptneo_jointc_lora_embed_clustersc3o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,wiqa_what_is_the_final_step_of_the_following_process,yelp_polarity_reviews_0_2_0,cos_e_v1_11_rationale,anli_r3_0_1_0,duorc_SelfRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,gem_dart_1_1_0,cos_e_v1_11_generate_explanation_given_text,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,duorc_ParaphraseRC_title_generation,cos_e_v1_11_i_think,gem_wiki_lingua_english_en_1_1_0,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,gigaword_1_2_0,kilt_tasks_hotpotqa_complex_question,gem_web_nlg_en_1_1_0,cos_e_v1_11_explain_why_human,word_segment,anli_r2_0_1_0,wmt16_translate_de_en_1_0_0,anli_r1_0_1_0,cos_e_v1_11_aligned_with_common_sense,aeslc_1_0_0,wmt16_translate_fi_en_1_0_0,race_high_Write_a_multi_choice_question_options_given_,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0,cot_esnli_ii | lora |
| gptneo_jointc_lora_embed_clustersc9o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,ropes_plain_background_situation,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,quartz_use_info_from_question_paragraph,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,quartz_use_info_from_paragraph_question,quartz_paragraph_question_plain_concat,ropes_prompt_mix,ropes_background_situation_middle | lora |
| gptneo_jointc_lora_embed_clustersc5o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2,trec_1_0_0,natural_questions_open_1_0_0,lambada_1_0_0,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,kilt_tasks_hotpotqa_final_exam,glue_cola_2_0_0,paws_wiki_1_1_0,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,wiki_qa_Is_This_True_,unified_qa_science_inst,stream_aqua,super_glue_multirc_1_0_2,snli_1_1_0,cot_strategyqa,cot_ecqa_ii,glue_qqp_2_0_0,cot_creak_ii,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,glue_mnli_2_0_0,super_glue_copa_1_0_2,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,glue_wnli_2_0_0,glue_mrpc_2_0_0,cot_qasc,cot_strategyqa_ii,stream_aqua_ii | lora |
| gptneo_jointc_lora_embed_clustersc1o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,cos_e_v1_11_question_option_description_text,wiki_qa_found_on_google,social_i_qa_Show_choices_and_generate_answer,app_reviews_categorize_rating_using_review,super_glue_wic_1_0_2,super_glue_record_1_0_2,race_middle_Is_this_the_right_answer,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,race_high_Select_the_best_answer_no_instructions_,sciq_Multiple_Choice,wiqa_does_the_supposed_perturbation_have_an_effect,math_dataset_algebra__linear_1d_1_0_0,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cos_e_v1_11_question_option_description_id,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,race_high_Is_this_the_right_answer,wiqa_effect_with_string_answer,quarel_do_not_use,qasc_qa_with_separated_facts_5,wiki_qa_exercise,ropes_prompt_beginning,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,qasc_qa_with_separated_facts_1,wiki_qa_automatic_system,cos_e_v1_11_description_question_option_text,quarel_heres_a_story,qasc_qa_with_combined_facts_1,qasc_is_correct_1,squad_v2_0_3_0_0,cos_e_v1_11_description_question_option_id,quarel_choose_between,social_i_qa_Check_if_a_random_answer_is_valid_or_not,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,social_i_qa_I_was_wondering,qasc_is_correct_2,quarel_testing_students,qasc_qa_with_separated_facts_4,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,quartz_read_passage_below_choose,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,drop_2_0_0,social_i_qa_Generate_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| gptneo_jointc_lora_embed_clustersc6o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_whats_the_answer,web_questions_question_answer,web_questions_short_general_knowledge_q,web_questions_get_the_answer,web_questions_potential_correct_answer | lora |
| gptneo_jointc_lora_embed_clustersc8o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/huggingface_xsum,cot_gsm8k,cot_sensemaking,cot_creak,cnn_dailymail_3_4_0,race_middle_Write_a_multi_choice_question_for_the_following_article,race_high_Write_a_multi_choice_question_for_the_following_article,dream_generate_first_utterance,para_crawl_enes,wmt16_translate_tr_en_1_0_0 | lora |
| gptneo_jointc_lora_embed_clustersc2o10_2e_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_generate_last_utterance,wiki_bio_key_content,duorc_SelfRC_build_story_around_qa,cot_ecqa,wiki_bio_who,dream_answer_to_dialogue,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
Last updated on: 2024-01-21 20:29:41+00:00
| [
"SCIQ"
] |
sordonia/library-t5_xl_lm-2epc | sordonia | null | [
"region:us"
] | "2024-01-22T15:03:58Z" | 2024-01-25T00:53:55+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 38
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| wiqa | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/wiqa | lora |
| amazon_polarity | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/amazon_polarity | lora |
| quartz | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/quartz | lora |
| rotten_tomatoes | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/rotten_tomatoes | lora |
| xsum | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/xsum | lora |
| wiki_bio | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/wiki_bio | lora |
| common_gen | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/common_gen | lora |
| wiki_hop_original | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/wiki_hop_original | lora |
| glue_mrpc | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/glue_mrpc | lora |
| duorc_SelfRC | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/duorc_SelfRC | lora |
| trec | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/trec | lora |
| paws_labeled_final | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/paws_labeled_final | lora |
| quarel | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/quarel | lora |
| ag_news | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/ag_news | lora |
| glue_qqp | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/glue_qqp | lora |
| quail | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/quail | lora |
| cos_e_v1_11 | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/cos_e_v1_11 | lora |
| imdb | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/imdb | lora |
| adversarial_qa_dbert | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/adversarial_qa_dbert | lora |
| cosmos_qa | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/cosmos_qa | lora |
| qasc | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/qasc | lora |
| adversarial_qa_droberta | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/adversarial_qa_droberta | lora |
| quoref | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/quoref | lora |
| social_i_qa | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/social_i_qa | lora |
| app_reviews | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/app_reviews | lora |
| cnn_dailymail_3_0_0 | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/cnn_dailymail_3_0_0 | lora |
| adversarial_qa_dbidaf | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/adversarial_qa_dbidaf | lora |
| dream | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/dream | lora |
| dbpedia_14 | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/dbpedia_14 | lora |
| wiki_qa | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/wiki_qa | lora |
| samsum | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/samsum | lora |
| multi_news | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/multi_news | lora |
| ropes | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/ropes | lora |
| duorc_ParaphraseRC | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/duorc_ParaphraseRC | lora |
| kilt_tasks_hotpotqa | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/kilt_tasks_hotpotqa | lora |
| gigaword | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/gigaword | lora |
| sciq | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/sciq | lora |
| yelp_review_full | google/t5-xl-lm-adapt | sordonia/t0-1.6M-flat/yelp_review_full | lora |
Last updated on: 2024-01-22 15:04:02+00:00
| [
"SCIQ"
] |
zhan1993/library-phi_2-v3-10-clusters-loss | zhan1993 | null | [
"region:us"
] | "2024-01-23T04:15:31Z" | 2024-01-23T04:15:39+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_3epoch_loss_cluster_7 | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension,wiki_bio_what_content,cos_e_v1_11_aligned_with_common_sense,gem_web_nlg_en_1_1_0,cnn_dailymail_3_4_0,race_high_Write_a_multi_choice_question_options_given_,stream_aqua_ii,duorc_SelfRC_build_story_around_qa,cot_esnli_ii,cos_e_v1_11_i_think,stream_qed_ii,cos_e_v1_11_rationale,race_middle_Write_a_multi_choice_question_options_given_,trivia_qa_rc_1_1_0,cot_sensemaking_ii,wiki_bio_key_content,dream_generate_first_utterance,duorc_ParaphraseRC_build_story_around_qa,app_reviews_convert_to_rating,wiqa_what_is_the_final_step_of_the_following_process,cot_strategyqa,wiki_qa_Direct_Answer_to_Question,wiqa_what_might_be_the_first_step_of_the_process,race_middle_Write_a_multi_choice_question_for_the_following_article,wiqa_what_might_be_the_last_step_of_the_process,cos_e_v1_11_generate_explanation_given_text,true_case,cos_e_v1_11_explain_why_human,race_high_Write_a_multi_choice_question_for_the_following_article,huggingface_xsum,cot_creak_ii,wiki_bio_who,dream_answer_to_dialogue,word_segment | lora |
| phi2_joint_3epoch_loss_cluster_9 | phi-2 | sordonia/flan-10k-flat/stream_aqua,cot_esnli,fix_punct,cot_qasc,cot_gsm8k,multi_news_1_0_0,cot_creak,stream_qed,cot_sensemaking,cot_ecqa | lora |
| phi2_joint_3epoch_loss_cluster_8 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0,glue_qqp_2_0_0,paws_wiki_1_1_0,anli_r3_0_1_0,anli_r2_0_1_0,anli_r1_0_1_0,ag_news_subset_1_0_0 | lora |
| phi2_joint_3epoch_loss_cluster_10 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,imdb_reviews_plain_text_1_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,snli_1_1_0,super_glue_multirc_1_0_2,super_glue_wic_1_0_2,definite_pronoun_resolution_1_1_0,super_glue_rte_1_0_2,glue_mrpc_2_0_0,glue_wnli_2_0_0,super_glue_wsc_fixed_1_0_2,glue_cola_2_0_0 | lora |
| phi2_joint_3epoch_loss_cluster_1 | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice,qasc_qa_with_separated_facts_1,qasc_qa_with_separated_facts_2,quartz_having_read_above_passage,dream_baseline,sciq_Multiple_Choice_Closed_Book_,qasc_qa_with_separated_facts_4,quail_context_question_answer_description_text,sciq_Multiple_Choice_Question_First,dream_read_the_following_conversation_and_answer_the_question,qasc_qa_with_combined_facts_1 | lora |
| phi2_joint_3epoch_loss_cluster_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1,quarel_testing_students,quartz_use_info_from_paragraph_question,ropes_plain_bottom_hint,ropes_prompt_mix,ropes_read_background_situation,wiki_hop_original_choose_best_object_affirmative_1,quail_no_prompt_text,ropes_given_background_situation,quarel_logic_test,quail_context_question_description_answer_text,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_affirmative_2,wiqa_effect_with_string_answer,quail_context_description_question_answer_text,quartz_read_passage_below_choose,wiki_hop_original_choose_best_object_affirmative_3,quarel_choose_between,sciq_Direct_Question_Closed_Book_,quartz_answer_question_below,ropes_prompt_bottom_no_hint,quartz_given_the_fact_answer_the_q,race_middle_Select_the_best_answer_generate_span_,ropes_background_new_situation_answer,sciq_Direct_Question,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,quarel_do_not_use,ropes_prompt_beginning,quarel_heres_a_story,ropes_background_situation_middle,ropes_new_situation_background_answer,ropes_plain_background_situation,quail_description_context_question_answer_text,quartz_paragraph_question_plain_concat,wiqa_which_of_the_following_is_the_supposed_perturbation,quartz_use_info_from_question_paragraph,ropes_plain_no_background,race_high_Select_the_best_answer_generate_span_,ropes_prompt_bottom_hint_beginning | lora |
| phi2_joint_3epoch_loss_cluster_4 | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise,duorc_SelfRC_generate_question_by_answer,cot_gsm8k_ii,gem_wiki_lingua_english_en_1_1_0,wiki_hop_original_generate_object,wiqa_what_is_the_missing_first_step,wiki_qa_found_on_google,wmt16_translate_de_en_1_0_0,quoref_Guess_Title_For_Context,gem_common_gen_1_1_0,super_glue_cb_1_0_2,para_crawl_enes,app_reviews_generate_review,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,super_glue_copa_1_0_2,math_dataset_algebra__linear_1d_1_0_0,app_reviews_convert_to_star_rating,wiki_bio_guess_person,wiqa_does_the_supposed_perturbation_have_an_effect,wiki_qa_Jeopardy_style,wmt16_translate_tr_en_1_0_0,duorc_ParaphraseRC_title_generation,race_middle_Is_this_the_right_answer,app_reviews_categorize_rating_using_review,gem_dart_1_1_0,duorc_ParaphraseRC_generate_question_by_answer,wiki_hop_original_generate_subject,aeslc_1_0_0,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,wmt16_translate_ro_en_1_0_0,race_high_Is_this_the_right_answer,duorc_SelfRC_title_generation,duorc_SelfRC_generate_question,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_hop_original_generate_subject_and_object,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_explain_relation,super_glue_record_1_0_2,cot_ecqa_ii,wmt14_translate_fr_en_1_0_0,dbpedia_14_given_a_choice_of_categories_,wiki_qa_Generate_Question_from_Topic,dbpedia_14_pick_one_category_for_the_following_text,gem_e2e_nlg_1_1_0,social_i_qa_Generate_the_question_from_the_answer,cot_strategyqa_ii,wmt16_translate_fi_en_1_0_0,duorc_ParaphraseRC_generate_question,dream_generate_last_utterance,quac_1_0_0 | lora |
| phi2_joint_3epoch_loss_cluster_3 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0,adversarial_qa_dbert_generate_question,quoref_Found_Context_Online,web_questions_get_the_answer,unified_qa_science_inst,quoref_What_Is_The_Answer,adversarial_qa_droberta_generate_question,adversarial_qa_dbidaf_question_context_answer,web_questions_whats_the_answer,adversarial_qa_droberta_question_context_answer,kilt_tasks_hotpotqa_combining_facts,web_questions_potential_correct_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_decide_worth_it,adversarial_qa_droberta_tell_what_it_is,duorc_ParaphraseRC_answer_question,duorc_SelfRC_extract_answer,drop_2_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_dbidaf_based_on,natural_questions_open_1_0_0,lambada_1_0_0,quoref_Read_And_Extract_,duorc_SelfRC_question_answering,squad_v1_1_3_0_0,coqa_1_0_0,web_questions_question_answer,wiki_qa_Topic_Prediction_Question_Only,quoref_Find_Answer,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,cos_e_v1_11_question_description_option_text,duorc_SelfRC_movie_director,quoref_Given_Context_Answer_Question,adversarial_qa_dbidaf_tell_what_it_is,web_questions_short_general_knowledge_q,duorc_SelfRC_answer_question,quoref_Answer_Question_Given_Context,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_text,adversarial_qa_dbert_based_on,duorc_ParaphraseRC_movie_director,quoref_Guess_Answer,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,kilt_tasks_hotpotqa_final_exam,quoref_Answer_Friend_Question,adversarial_qa_dbert_question_context_answer,kilt_tasks_hotpotqa_complex_question,wiki_qa_Topic_Prediction_Answer_Only,duorc_ParaphraseRC_decide_worth_it,quoref_Answer_Test,kilt_tasks_hotpotqa_formulate,gigaword_1_2_0,duorc_ParaphraseRC_question_answering,adversarial_qa_droberta_based_on | lora |
| phi2_joint_3epoch_loss_cluster_5 | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test,quail_no_prompt_id,quail_context_question_answer_description_id,race_high_Select_the_best_answer,race_high_Select_the_best_answer_no_instructions_,wiqa_effect_with_label_answer,race_middle_Select_the_best_answer,cos_e_v1_11_question_description_option_id,quail_context_description_question_answer_id,quail_description_context_question_answer_id,wiki_qa_Decide_good_answer,quail_context_question_description_answer_id,race_middle_Select_the_best_answer_no_instructions_,cos_e_v1_11_description_question_option_id,race_middle_Taking_a_test,cos_e_v1_11_question_option_description_id,social_i_qa_Show_choices_and_generate_index | lora |
| phi2_joint_3epoch_loss_cluster_6 | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_,race_high_Read_the_article_and_answer_the_question_no_option_,yelp_polarity_reviews_0_2_0,qasc_is_correct_1,social_i_qa_Generate_answer,cosmos_qa_1_0_0,qasc_qa_with_separated_facts_5,glue_stsb_2_0_0,social_i_qa_I_was_wondering,qasc_qa_with_separated_facts_3,quail_description_context_question_text,qasc_is_correct_2,quail_context_question_description_text,social_i_qa_Check_if_a_random_answer_is_valid_or_not,race_middle_Read_the_article_and_answer_the_question_no_option_,quail_context_description_question_text,wiki_qa_automatic_system | lora |
Last updated on: 2024-01-23T04:15:32.000Z
| [
"SCIQ"
] |
ntc-ai/SDXL-LoRA-slider.smoking-a-cigarette-looking-cool | ntc-ai | text-to-image | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | "2024-01-23T13:25:41Z" | 2024-01-23T13:25:44+00:00 | 0 | 0 | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/evaluate/smoking a cigarette looking cool.../smoking a cigarette
looking cool_17_3.0.png
widget:
- text: smoking a cigarette looking cool
output:
url: images/smoking a cigarette looking cool_17_3.0.png
- text: smoking a cigarette looking cool
output:
url: images/smoking a cigarette looking cool_19_3.0.png
- text: smoking a cigarette looking cool
output:
url: images/smoking a cigarette looking cool_20_3.0.png
- text: smoking a cigarette looking cool
output:
url: images/smoking a cigarette looking cool_21_3.0.png
- text: smoking a cigarette looking cool
output:
url: images/smoking a cigarette looking cool_22_3.0.png
inference: false
instance_prompt: smoking a cigarette looking cool
---
# ntcai.xyz slider - smoking a cigarette looking cool (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/smoking a cigarette looking cool_17_-3.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_17_0.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_17_3.0.png" width=256 height=256 /> |
| <img src="images/smoking a cigarette looking cool_19_-3.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_19_0.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_19_3.0.png" width=256 height=256 /> |
| <img src="images/smoking a cigarette looking cool_20_-3.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_20_0.0.png" width=256 height=256 /> | <img src="images/smoking a cigarette looking cool_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
smoking a cigarette looking cool
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.smoking-a-cigarette-looking-cool', weight_name='smoking a cigarette looking cool.safetensors', adapter_name="smoking a cigarette looking cool")
# Activate the LoRA
pipe.set_adapters(["smoking a cigarette looking cool"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, smoking a cigarette looking cool"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
| [
"CRAFT"
] |
ostapeno/library-gptneo1B-10-rand-flan-clusters | ostapeno | null | [
"region:us"
] | "2024-01-23T17:00:30Z" | 2024-09-18T19:26:25+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| gptneo_jointc_lora_embed_randclusterscluster_3_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5,wiki_qa_automatic_system,stream_aqua_ii,dbpedia_14_pick_one_category_for_the_following_text,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Generate_Question_from_Topic,cot_gsm8k_ii,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_interrogative_2,quail_context_question_answer_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,qasc_qa_with_combined_facts_1,adversarial_qa_dbert_answer_the_following_q,social_i_qa_I_was_wondering,stream_aqua,word_segment,ropes_plain_no_background,super_glue_multirc_1_0_2,wiki_hop_original_choose_best_object_affirmative_3,app_reviews_convert_to_rating,anli_r3_0_1_0,app_reviews_convert_to_star_rating,quartz_paragraph_question_plain_concat,kilt_tasks_hotpotqa_complex_question,quartz_use_info_from_paragraph_question | lora |
| gptneo_jointc_lora_embed_randclusterscluster_9_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article,cot_qasc,definite_pronoun_resolution_1_1_0,wiki_qa_found_on_google,wiki_bio_comprehension,wiki_qa_Topic_Prediction_Question_Only,wiki_bio_guess_person,fix_punct,race_middle_Select_the_best_answer_no_instructions_,quac_1_0_0,wiqa_does_the_supposed_perturbation_have_an_effect,quartz_given_the_fact_answer_the_q,ropes_new_situation_background_answer,social_i_qa_Generate_answer,gigaword_1_2_0,duorc_SelfRC_decide_worth_it,kilt_tasks_hotpotqa_straighforward_qa,quail_no_prompt_text,cot_esnli,quoref_Answer_Friend_Question,race_middle_Select_the_best_answer_generate_span_,unified_qa_science_inst,sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_strategyqa | lora |
| gptneo_jointc_lora_embed_randclusterscluster_5_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0,duorc_SelfRC_question_answering,cot_sensemaking_ii,huggingface_xsum,duorc_ParaphraseRC_build_story_around_qa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,quartz_answer_question_based_on,ropes_plain_background_situation,race_middle_Write_a_multi_choice_question_for_the_following_article,quail_description_context_question_text,web_questions_whats_the_answer,cot_ecqa,true_case,adversarial_qa_dbert_question_context_answer,duorc_SelfRC_title_generation,quail_context_question_description_answer_id,quarel_do_not_use,adversarial_qa_dbidaf_answer_the_following_q,duorc_ParaphraseRC_decide_worth_it,race_middle_Is_this_the_right_answer,wmt16_translate_de_en_1_0_0,wiki_qa_Is_This_True_,race_middle_Select_the_best_answer,aeslc_1_0_0,duorc_ParaphraseRC_question_answering,wiki_hop_original_choose_best_object_affirmative_1 | lora |
| gptneo_jointc_lora_embed_randclusterscluster_4_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_explain_why_human,cot_sensemaking,quoref_Find_Answer,quail_context_description_question_text,social_i_qa_Generate_the_question_from_the_answer,quartz_having_read_above_passage,stream_qed_ii,wiqa_what_might_be_the_first_step_of_the_process,wiki_bio_who,duorc_SelfRC_generate_question_by_answer,race_high_Select_the_best_answer_generate_span_,lambada_1_0_0,coqa_1_0_0,race_high_Select_the_best_answer,adversarial_qa_droberta_question_context_answer,quail_context_question_answer_description_id,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_tell_what_it_is,cnn_dailymail_3_4_0,quail_no_prompt_id,ag_news_subset_1_0_0,trivia_qa_rc_1_1_0,ropes_prompt_bottom_hint_beginning,super_glue_wic_1_0_2 | lora |
| gptneo_jointc_lora_embed_randclusterscluster_6_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0,para_crawl_enes,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_id,glue_stsb_2_0_0,quail_context_description_question_answer_text,dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_description_option_text,cos_e_v1_11_description_question_option_text,race_high_Write_a_multi_choice_question_options_given_,ropes_prompt_bottom_no_hint,quarel_testing_students,wmt16_translate_tr_en_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_final_step_of_the_following_process,quoref_Given_Context_Answer_Question,wiki_hop_original_generate_object,quartz_use_info_from_question_paragraph,duorc_SelfRC_build_story_around_qa,drop_2_0_0,wiqa_effect_with_string_answer,race_high_Taking_a_test,wiki_hop_original_generate_subject_and_object,glue_qqp_2_0_0,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| gptneo_jointc_lora_embed_randclusterscluster_1_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0,quarel_choose_between,wiki_hop_original_explain_relation,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,race_high_Select_the_best_answer_no_instructions_,kilt_tasks_hotpotqa_combining_facts,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_generate_question,glue_cola_2_0_0,imdb_reviews_plain_text_1_0_0,squad_v1_1_3_0_0,race_high_Is_this_the_right_answer,qasc_qa_with_separated_facts_1,glue_sst2_2_0_0,wiqa_what_is_the_missing_first_step,duorc_ParaphraseRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,wiki_qa_Jeopardy_style,quartz_answer_question_below,ropes_prompt_beginning,ropes_read_background_situation,wiki_qa_Decide_good_answer,super_glue_cb_1_0_2,qasc_qa_with_separated_facts_4,cot_ecqa_ii | lora |
| gptneo_jointc_lora_embed_randclusterscluster_10_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed,cot_esnli_ii,quarel_heres_a_story,quoref_Guess_Title_For_Context,qasc_is_correct_2,wiqa_effect_with_label_answer,dream_generate_last_utterance,adversarial_qa_dbert_based_on,dream_answer_to_dialogue,sciq_Multiple_Choice_Question_First,quail_context_question_description_answer_text,wiki_qa_Direct_Answer_to_Question,ropes_background_new_situation_answer,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,quoref_What_Is_The_Answer,dbpedia_14_given_a_choice_of_categories_,qasc_qa_with_separated_facts_2,glue_mrpc_2_0_0,gem_e2e_nlg_1_1_0,anli_r1_0_1_0,race_middle_Read_the_article_and_answer_the_question_no_option_,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,adversarial_qa_dbidaf_based_on | lora |
| gptneo_jointc_lora_embed_randclusterscluster_2_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id,social_i_qa_Show_choices_and_generate_index,web_questions_potential_correct_answer,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cosmos_qa_1_0_0,sciq_Direct_Question,super_glue_wsc_fixed_1_0_2,race_middle_Taking_a_test,wmt14_translate_fr_en_1_0_0,duorc_SelfRC_extract_answer,wiki_hop_original_generate_subject,duorc_SelfRC_answer_question,qasc_is_correct_1,cos_e_v1_11_i_think,wiki_qa_exercise,race_middle_Write_a_multi_choice_question_options_given_,quoref_Read_And_Extract_,web_questions_short_general_knowledge_q,web_questions_question_answer,quarel_logic_test,app_reviews_categorize_rating_using_review,cot_strategyqa_ii,glue_mnli_2_0_0,quoref_Answer_Test,super_glue_rte_1_0_2 | lora |
| gptneo_jointc_lora_embed_randclusterscluster_7_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii,quail_description_context_question_answer_id,kilt_tasks_hotpotqa_final_exam,cot_gsm8k,cos_e_v1_11_aligned_with_common_sense,squad_v2_0_3_0_0,duorc_SelfRC_movie_director,anli_r2_0_1_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,duorc_ParaphraseRC_answer_question,ropes_background_situation_middle,dream_generate_first_utterance,quail_context_question_description_text,adversarial_qa_droberta_based_on,glue_wnli_2_0_0,super_glue_record_1_0_2,web_questions_get_the_answer,ropes_prompt_mix,app_reviews_generate_review,cos_e_v1_11_rationale,adversarial_qa_droberta_generate_question,yelp_polarity_reviews_0_2_0,ropes_given_background_situation,qasc_qa_with_separated_facts_3,quoref_Guess_Answer | lora |
| gptneo_jointc_lora_embed_randclusterscluster_8_3epoch | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2,duorc_ParaphraseRC_movie_director,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,quartz_read_passage_below_choose,social_i_qa_Check_if_a_random_answer_is_valid_or_not,cot_creak,ropes_plain_bottom_hint,super_glue_copa_1_0_2,natural_questions_open_1_0_0,trec_1_0_0,gem_web_nlg_en_1_1_0,wiki_bio_key_content,wmt16_translate_fi_en_1_0_0,quoref_Answer_Question_Given_Context,duorc_ParaphraseRC_generate_question,math_dataset_algebra__linear_1d_1_0_0,duorc_ParaphraseRC_title_generation,quail_context_description_question_answer_id,wiki_bio_what_content,adversarial_qa_dbert_tell_what_it_is,sciq_Multiple_Choice_Closed_Book_,duorc_ParaphraseRC_extract_answer,dream_baseline,gem_wiki_lingua_english_en_1_1_0 | lora |
Last updated on: 2024-01-23 17:00:30+00:00
| [
"SCIQ"
] |
sethuiyer/txtai-medsearch | sethuiyer | sentence-similarity | [
"txtai",
"sentence-similarity",
"en",
"zh",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] | "2024-01-24T07:49:41Z" | 2024-01-24T08:33:47+00:00 | 0 | 6 | ---
language:
- en
- zh
library_name: txtai
license:
- cc-by-sa-3.0
- gfdl
tags:
- sentence-similarity
inference: false
---
# Medical txtai embeddings index
This is a [txtai](https://github.com/neuml/txtai) embeddings index specifically designed for medical texts, encompassing a diverse corpus in both English and Chinese.
The model is primed for integration into medical information systems, aiding in the quick retrieval of relevant clinical information.
### Data Sources
The model is trained on a substantial dataset, including 434411 entries from a bilingual (English and Chinese) corpus of clinical texts. The sources are:
- `shibing624/medical`, a dataset featuring a variety of medical scenarios and questions in both English and Chinese, suitable for text generation and medical question-answering systems. It's licensed under Apache 2.0.
- `keivalya/MedQuad-MedicalQnADataset`, offering detailed insights into various health conditions and their treatments, covering prevention, diagnosis, treatment, and susceptibility.
- `GBaker/MedQA-USMLE-4-options`, a collection of multiple-choice questions based on the USMLE, focusing on a wide range of medical topics and scenarios.
- `medalpaca/medical_meadow_medqa`, a dataset for question answering in English and Chinese, encompassing clinical scenarios and medical queries with multiple-choice answers.
- `medalpaca/medical_meadow_medical_flashcards`, featuring over 34,000 rows of question and answer pairs derived from medical flashcards, focusing on a wide range of medical subjects.
Each of these datasets contributes to the depth and diversity of the medical knowledge encapsulated in the txtai embeddings model, making it an effective tool for medical information retrieval and analysis.
## Indexing
The txtai embeddings model utilizes 'efederici/multilingual-e5-small-4096', a transformer-based model with 12 layers and an embedding size of 384, supporting 94 languages.
### Configuration
The embedding model is quantized to 4 bits for size efficiency and supports batch encoding of 15 for optimized performance.
The indexing is implemented using simple numpy cosine similarity, ensuring straightforward and efficient retrieval.
## Usage
1. Load the dataset using the provided JSON file.
2. Initialize and load the embeddings using txtai:
```python
from txtai import Embeddings
embeddings = Embeddings()
embeddings.load('index.tar.gz')
```
## Next Steps
1. More detailed usage, including using txtai to create inter-operability between English and Chinese
2. Create an usecase with [CrewAI](https://github.com/joaomdmoura/crewAI) and [Dr.Samantha](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
## License
This model is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License and the GNU Free Documentation License.
| [
"MEDQA"
] |
zhan1993/library-phi_2-v3-10-clusters-sim-alex | zhan1993 | null | [
"region:us"
] | "2024-01-24T13:39:11Z" | 2024-07-06T14:03:15+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_3epoch_sim_cluster_10 | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,app_reviews_convert_to_star_rating,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,kilt_tasks_hotpotqa_final_exam,sciq_Multiple_Choice,wiqa_does_the_supposed_perturbation_have_an_effect,cos_e_v1_11_question_description_option_text,wiki_qa_Is_This_True_,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cos_e_v1_11_question_option_description_id,wiqa_effect_with_string_answer,qasc_qa_with_separated_facts_5,dream_baseline,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,qasc_is_correct_1,cos_e_v1_11_description_question_option_id,social_i_qa_Check_if_a_random_answer_is_valid_or_not,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,qasc_is_correct_2,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_3epoch_sim_cluster_3 | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,app_reviews_categorize_rating_using_review,race_middle_Is_this_the_right_answer,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,unified_qa_science_inst,race_high_Is_this_the_right_answer,cot_strategyqa,cot_ecqa_ii,quarel_do_not_use,wiki_qa_exercise,wiki_qa_automatic_system,cot_creak_ii,quarel_heres_a_story,quarel_choose_between,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,super_glue_copa_1_0_2,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,quarel_logic_test,stream_aqua_ii | lora |
| phi2_joint_3epoch_sim_cluster_9 | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,cot_sensemaking,super_glue_wic_1_0_2,cos_e_v1_11_rationale,anli_r3_0_1_0,dream_generate_last_utterance,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cot_creak,stream_aqua,snli_1_1_0,cos_e_v1_11_i_think,glue_qqp_2_0_0,cos_e_v1_11_explain_why_human,anli_r2_0_1_0,anli_r1_0_1_0,glue_stsb_2_0_0,cos_e_v1_11_aligned_with_common_sense,glue_mnli_2_0_0,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_3epoch_sim_cluster_1 | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0,web_questions_whats_the_answer,web_questions_question_answer,dbpedia_14_pick_one_category_for_the_following_text,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,adversarial_qa_droberta_based_on,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,kilt_tasks_hotpotqa_formulate,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,squad_v1_1_3_0_0 | lora |
| phi2_joint_3epoch_sim_cluster_5 | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| phi2_joint_3epoch_sim_cluster_8 | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_3epoch_sim_cluster_2 | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,super_glue_record_1_0_2,wiki_hop_original_generate_object,adversarial_qa_droberta_tell_what_it_is,dbpedia_14_given_a_choice_of_categories_,wiki_hop_original_choose_best_object_affirmative_3,quac_1_0_0,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_choose_best_object_affirmative_1,adversarial_qa_dbert_answer_the_following_q,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,squad_v2_0_3_0_0,wiki_hop_original_generate_subject,wiki_bio_guess_person,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,race_high_Write_a_multi_choice_question_options_given_,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora |
| phi2_joint_3epoch_sim_cluster_7 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_gsm8k,trec_1_0_0,yelp_polarity_reviews_0_2_0,lambada_1_0_0,glue_cola_2_0_0,ag_news_subset_1_0_0,gem_dart_1_1_0,math_dataset_algebra__linear_1d_1_0_0,cnn_dailymail_3_4_0,wiki_hop_original_explain_relation,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,gem_wiki_lingua_english_en_1_1_0,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,gem_web_nlg_en_1_1_0,word_segment,race_high_Write_a_multi_choice_question_for_the_following_article,wmt16_translate_de_en_1_0_0,cot_ecqa,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,para_crawl_enes,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0 | lora |
| phi2_joint_3epoch_sim_cluster_6 | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| phi2_joint_3epoch_sim_cluster_4 | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,wmt16_translate_ro_en_1_0_0,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,gem_common_gen_1_1_0,duorc_SelfRC_build_story_around_qa,app_reviews_generate_review,wiki_bio_what_content,wiki_bio_who,gem_e2e_nlg_1_1_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
Last updated on: 2024-01-24T13:39:11.000Z
| [
"SCIQ"
] |
ostapeno/phi_2-v3-10-flan-clusters_1ep | ostapeno | null | [
"region:us"
] | "2024-01-24T15:20:59Z" | 2024-01-24T15:24:21+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 5
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_lora_embed_clustersc3_1epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,cos_e_v1_11_question_option_description_id,cot_ecqa_ii,wiqa_effect_with_string_answer,quarel_do_not_use,qasc_qa_with_separated_facts_5,dream_baseline,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,quarel_heres_a_story,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,quarel_testing_students,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,wiqa_effect_with_label_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_lora_embed_clustersc9_1epoch | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,quartz_paragraph_question_plain_concat,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_lora_embed_clustersc2_1epoch | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quac_1_0_0,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| phi2_joint_lora_embed_clustersc1_1epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,huggingface_xsum,cot_esnli,cot_gsm8k,gem_dart_1_1_0,gem_common_gen_1_1_0,stream_aqua,cnn_dailymail_3_4_0,gem_wiki_lingua_english_en_1_1_0,gigaword_1_2_0,gem_web_nlg_en_1_1_0,word_segment,app_reviews_generate_review,wmt16_translate_de_en_1_0_0,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,gem_e2e_nlg_1_1_0,para_crawl_enes,wmt14_translate_fr_en_1_0_0,cot_esnli_ii,wiqa_what_is_the_missing_first_step,coqa_1_0_0 | lora |
| phi2_joint_lora_embed_clustersc0_1epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,true_case,cot_sensemaking,trec_1_0_0,quartz_answer_question_based_on,cos_e_v1_11_rationale,natural_questions_open_1_0_0,web_questions_whats_the_answer,anli_r3_0_1_0,kilt_tasks_hotpotqa_final_exam,glue_cola_2_0_0,cos_e_v1_11_generate_explanation_given_text,adversarial_qa_droberta_tell_what_it_is,cot_creak,cot_gsm8k_ii,math_dataset_algebra__linear_1d_1_0_0,quartz_use_info_from_question_paragraph,web_questions_question_answer,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,cos_e_v1_11_i_think,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,adversarial_qa_droberta_based_on,web_questions_get_the_answer,adversarial_qa_dbert_answer_the_following_q,kilt_tasks_hotpotqa_complex_question,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,adversarial_qa_droberta_question_context_answer,cos_e_v1_11_explain_why_human,cot_creak_ii,cos_e_v1_11_description_question_option_text,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,stream_qed_ii,wiki_bio_guess_person,cos_e_v1_11_aligned_with_common_sense,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,cot_sensemaking_ii,adversarial_qa_dbert_tell_what_it_is,kilt_tasks_hotpotqa_formulate,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is,social_i_qa_Generate_answer,squad_v1_1_3_0_0 | lora |
Last updated on: 2024-01-24 15:20:59+00:00
CLusters were obtained using embeddings of experts trained for 5 epochs. THese are the first 5 clusters out of 10. | [
"SCIQ"
] |
zhan1993/library-phi_2-v3-10-clusters-loss-one_epoch | zhan1993 | null | [
"region:us"
] | "2024-01-25T02:57:28Z" | 2024-01-25T02:57:34+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_3epoch_loss_one_epoch_cluster_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1,quarel_testing_students,quartz_use_info_from_paragraph_question,ropes_plain_bottom_hint,ropes_prompt_mix,ropes_read_background_situation,wiki_hop_original_choose_best_object_affirmative_1,quail_no_prompt_text,ropes_given_background_situation,quarel_logic_test,quail_context_question_description_answer_text,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_affirmative_2,wiqa_effect_with_string_answer,quail_context_description_question_answer_text,quartz_read_passage_below_choose,wiki_hop_original_choose_best_object_affirmative_3,quarel_choose_between,sciq_Direct_Question_Closed_Book_,quartz_answer_question_below,ropes_prompt_bottom_no_hint,quartz_given_the_fact_answer_the_q,race_middle_Select_the_best_answer_generate_span_,ropes_background_new_situation_answer,sciq_Direct_Question,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,quarel_do_not_use,ropes_prompt_beginning,quarel_heres_a_story,ropes_background_situation_middle,ropes_new_situation_background_answer,ropes_plain_background_situation,quail_description_context_question_answer_text,quartz_paragraph_question_plain_concat,wiqa_which_of_the_following_is_the_supposed_perturbation,quartz_use_info_from_question_paragraph,ropes_plain_no_background,race_high_Select_the_best_answer_generate_span_,ropes_prompt_bottom_hint_beginning | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_7 | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension,wiki_bio_what_content,cos_e_v1_11_aligned_with_common_sense,gem_web_nlg_en_1_1_0,cnn_dailymail_3_4_0,race_high_Write_a_multi_choice_question_options_given_,stream_aqua_ii,duorc_SelfRC_build_story_around_qa,cot_esnli_ii,cos_e_v1_11_i_think,stream_qed_ii,cos_e_v1_11_rationale,race_middle_Write_a_multi_choice_question_options_given_,trivia_qa_rc_1_1_0,cot_sensemaking_ii,wiki_bio_key_content,dream_generate_first_utterance,duorc_ParaphraseRC_build_story_around_qa,app_reviews_convert_to_rating,wiqa_what_is_the_final_step_of_the_following_process,cot_strategyqa,wiki_qa_Direct_Answer_to_Question,wiqa_what_might_be_the_first_step_of_the_process,race_middle_Write_a_multi_choice_question_for_the_following_article,wiqa_what_might_be_the_last_step_of_the_process,cos_e_v1_11_generate_explanation_given_text,true_case,cos_e_v1_11_explain_why_human,race_high_Write_a_multi_choice_question_for_the_following_article,huggingface_xsum,cot_creak_ii,wiki_bio_who,dream_answer_to_dialogue,word_segment | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_3 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0,adversarial_qa_dbert_generate_question,quoref_Found_Context_Online,web_questions_get_the_answer,unified_qa_science_inst,quoref_What_Is_The_Answer,adversarial_qa_droberta_generate_question,adversarial_qa_dbidaf_question_context_answer,web_questions_whats_the_answer,adversarial_qa_droberta_question_context_answer,kilt_tasks_hotpotqa_combining_facts,web_questions_potential_correct_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_decide_worth_it,adversarial_qa_droberta_tell_what_it_is,duorc_ParaphraseRC_answer_question,duorc_SelfRC_extract_answer,drop_2_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_dbidaf_based_on,natural_questions_open_1_0_0,lambada_1_0_0,quoref_Read_And_Extract_,duorc_SelfRC_question_answering,squad_v1_1_3_0_0,coqa_1_0_0,web_questions_question_answer,wiki_qa_Topic_Prediction_Question_Only,quoref_Find_Answer,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,cos_e_v1_11_question_description_option_text,duorc_SelfRC_movie_director,quoref_Given_Context_Answer_Question,adversarial_qa_dbidaf_tell_what_it_is,web_questions_short_general_knowledge_q,duorc_SelfRC_answer_question,quoref_Answer_Question_Given_Context,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_text,adversarial_qa_dbert_based_on,duorc_ParaphraseRC_movie_director,quoref_Guess_Answer,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,kilt_tasks_hotpotqa_final_exam,quoref_Answer_Friend_Question,adversarial_qa_dbert_question_context_answer,kilt_tasks_hotpotqa_complex_question,wiki_qa_Topic_Prediction_Answer_Only,duorc_ParaphraseRC_decide_worth_it,quoref_Answer_Test,kilt_tasks_hotpotqa_formulate,gigaword_1_2_0,duorc_ParaphraseRC_question_answering,adversarial_qa_droberta_based_on | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_5 | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test,quail_no_prompt_id,quail_context_question_answer_description_id,race_high_Select_the_best_answer,race_high_Select_the_best_answer_no_instructions_,wiqa_effect_with_label_answer,race_middle_Select_the_best_answer,cos_e_v1_11_question_description_option_id,quail_context_description_question_answer_id,quail_description_context_question_answer_id,wiki_qa_Decide_good_answer,quail_context_question_description_answer_id,race_middle_Select_the_best_answer_no_instructions_,cos_e_v1_11_description_question_option_id,race_middle_Taking_a_test,cos_e_v1_11_question_option_description_id,social_i_qa_Show_choices_and_generate_index | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_9 | phi-2 | sordonia/flan-10k-flat/stream_aqua,cot_esnli,fix_punct,cot_qasc,cot_gsm8k,multi_news_1_0_0,cot_creak,stream_qed,cot_sensemaking,cot_ecqa | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_1 | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice,qasc_qa_with_separated_facts_1,qasc_qa_with_separated_facts_2,quartz_having_read_above_passage,dream_baseline,sciq_Multiple_Choice_Closed_Book_,qasc_qa_with_separated_facts_4,quail_context_question_answer_description_text,sciq_Multiple_Choice_Question_First,dream_read_the_following_conversation_and_answer_the_question,qasc_qa_with_combined_facts_1 | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_6 | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_,race_high_Read_the_article_and_answer_the_question_no_option_,yelp_polarity_reviews_0_2_0,qasc_is_correct_1,social_i_qa_Generate_answer,cosmos_qa_1_0_0,qasc_qa_with_separated_facts_5,glue_stsb_2_0_0,social_i_qa_I_was_wondering,qasc_qa_with_separated_facts_3,quail_description_context_question_text,qasc_is_correct_2,quail_context_question_description_text,social_i_qa_Check_if_a_random_answer_is_valid_or_not,race_middle_Read_the_article_and_answer_the_question_no_option_,quail_context_description_question_text,wiki_qa_automatic_system | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_8 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0,glue_qqp_2_0_0,paws_wiki_1_1_0,anli_r3_0_1_0,anli_r2_0_1_0,anli_r1_0_1_0,ag_news_subset_1_0_0 | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_10 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,imdb_reviews_plain_text_1_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,snli_1_1_0,super_glue_multirc_1_0_2,super_glue_wic_1_0_2,definite_pronoun_resolution_1_1_0,super_glue_rte_1_0_2,glue_mrpc_2_0_0,glue_wnli_2_0_0,super_glue_wsc_fixed_1_0_2,glue_cola_2_0_0 | lora |
| phi2_joint_3epoch_loss_one_epoch_cluster_4 | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise,duorc_SelfRC_generate_question_by_answer,cot_gsm8k_ii,gem_wiki_lingua_english_en_1_1_0,wiki_hop_original_generate_object,wiqa_what_is_the_missing_first_step,wiki_qa_found_on_google,wmt16_translate_de_en_1_0_0,quoref_Guess_Title_For_Context,gem_common_gen_1_1_0,super_glue_cb_1_0_2,para_crawl_enes,app_reviews_generate_review,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,super_glue_copa_1_0_2,math_dataset_algebra__linear_1d_1_0_0,app_reviews_convert_to_star_rating,wiki_bio_guess_person,wiqa_does_the_supposed_perturbation_have_an_effect,wiki_qa_Jeopardy_style,wmt16_translate_tr_en_1_0_0,duorc_ParaphraseRC_title_generation,race_middle_Is_this_the_right_answer,app_reviews_categorize_rating_using_review,gem_dart_1_1_0,duorc_ParaphraseRC_generate_question_by_answer,wiki_hop_original_generate_subject,aeslc_1_0_0,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,wmt16_translate_ro_en_1_0_0,race_high_Is_this_the_right_answer,duorc_SelfRC_title_generation,duorc_SelfRC_generate_question,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_hop_original_generate_subject_and_object,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_explain_relation,super_glue_record_1_0_2,cot_ecqa_ii,wmt14_translate_fr_en_1_0_0,dbpedia_14_given_a_choice_of_categories_,wiki_qa_Generate_Question_from_Topic,dbpedia_14_pick_one_category_for_the_following_text,gem_e2e_nlg_1_1_0,social_i_qa_Generate_the_question_from_the_answer,cot_strategyqa_ii,wmt16_translate_fi_en_1_0_0,duorc_ParaphraseRC_generate_question,dream_generate_last_utterance,quac_1_0_0 | lora |
Last updated on: 2024-01-25T02:57:28.000Z
| [
"SCIQ"
] |
zhan1993/gptneo_1B_flan_random-epoch_0 | zhan1993 | null | [
"region:us"
] | "2024-01-25T20:55:19Z" | 2024-01-26T15:00:44+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5,wiki_qa_automatic_system,stream_aqua_ii,dbpedia_14_pick_one_category_for_the_following_text,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Generate_Question_from_Topic,cot_gsm8k_ii,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_interrogative_2,quail_context_question_answer_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,qasc_qa_with_combined_facts_1,adversarial_qa_dbert_answer_the_following_q,social_i_qa_I_was_wondering,stream_aqua,word_segment,ropes_plain_no_background,super_glue_multirc_1_0_2,wiki_hop_original_choose_best_object_affirmative_3,app_reviews_convert_to_rating,anli_r3_0_1_0,app_reviews_convert_to_star_rating,quartz_paragraph_question_plain_concat,kilt_tasks_hotpotqa_complex_question,quartz_use_info_from_paragraph_question | lora |
| cluster_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0,para_crawl_enes,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_id,glue_stsb_2_0_0,quail_context_description_question_answer_text,dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_description_option_text,cos_e_v1_11_description_question_option_text,race_high_Write_a_multi_choice_question_options_given_,ropes_prompt_bottom_no_hint,quarel_testing_students,wmt16_translate_tr_en_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_final_step_of_the_following_process,quoref_Given_Context_Answer_Question,wiki_hop_original_generate_object,quartz_use_info_from_question_paragraph,duorc_SelfRC_build_story_around_qa,drop_2_0_0,wiqa_effect_with_string_answer,race_high_Taking_a_test,wiki_hop_original_generate_subject_and_object,glue_qqp_2_0_0,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| cluster_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0,duorc_SelfRC_question_answering,cot_sensemaking_ii,huggingface_xsum,duorc_ParaphraseRC_build_story_around_qa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,quartz_answer_question_based_on,ropes_plain_background_situation,race_middle_Write_a_multi_choice_question_for_the_following_article,quail_description_context_question_text,web_questions_whats_the_answer,cot_ecqa,true_case,adversarial_qa_dbert_question_context_answer,duorc_SelfRC_title_generation,quail_context_question_description_answer_id,quarel_do_not_use,adversarial_qa_dbidaf_answer_the_following_q,duorc_ParaphraseRC_decide_worth_it,race_middle_Is_this_the_right_answer,wmt16_translate_de_en_1_0_0,wiki_qa_Is_This_True_,race_middle_Select_the_best_answer,aeslc_1_0_0,duorc_ParaphraseRC_question_answering,wiki_hop_original_choose_best_object_affirmative_1 | lora |
| cluster_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_explain_why_human,cot_sensemaking,quoref_Find_Answer,quail_context_description_question_text,social_i_qa_Generate_the_question_from_the_answer,quartz_having_read_above_passage,stream_qed_ii,wiqa_what_might_be_the_first_step_of_the_process,wiki_bio_who,duorc_SelfRC_generate_question_by_answer,race_high_Select_the_best_answer_generate_span_,lambada_1_0_0,coqa_1_0_0,race_high_Select_the_best_answer,adversarial_qa_droberta_question_context_answer,quail_context_question_answer_description_id,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_tell_what_it_is,cnn_dailymail_3_4_0,quail_no_prompt_id,ag_news_subset_1_0_0,trivia_qa_rc_1_1_0,ropes_prompt_bottom_hint_beginning,super_glue_wic_1_0_2 | lora |
| cluster_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2,duorc_ParaphraseRC_movie_director,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,quartz_read_passage_below_choose,social_i_qa_Check_if_a_random_answer_is_valid_or_not,cot_creak,ropes_plain_bottom_hint,super_glue_copa_1_0_2,natural_questions_open_1_0_0,trec_1_0_0,gem_web_nlg_en_1_1_0,wiki_bio_key_content,wmt16_translate_fi_en_1_0_0,quoref_Answer_Question_Given_Context,duorc_ParaphraseRC_generate_question,math_dataset_algebra__linear_1d_1_0_0,duorc_ParaphraseRC_title_generation,quail_context_description_question_answer_id,wiki_bio_what_content,adversarial_qa_dbert_tell_what_it_is,sciq_Multiple_Choice_Closed_Book_,duorc_ParaphraseRC_extract_answer,dream_baseline,gem_wiki_lingua_english_en_1_1_0 | lora |
| cluster_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id,social_i_qa_Show_choices_and_generate_index,web_questions_potential_correct_answer,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cosmos_qa_1_0_0,sciq_Direct_Question,super_glue_wsc_fixed_1_0_2,race_middle_Taking_a_test,wmt14_translate_fr_en_1_0_0,duorc_SelfRC_extract_answer,wiki_hop_original_generate_subject,duorc_SelfRC_answer_question,qasc_is_correct_1,cos_e_v1_11_i_think,wiki_qa_exercise,race_middle_Write_a_multi_choice_question_options_given_,quoref_Read_And_Extract_,web_questions_short_general_knowledge_q,web_questions_question_answer,quarel_logic_test,app_reviews_categorize_rating_using_review,cot_strategyqa_ii,glue_mnli_2_0_0,quoref_Answer_Test,super_glue_rte_1_0_2 | lora |
| cluster_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed,cot_esnli_ii,quarel_heres_a_story,quoref_Guess_Title_For_Context,qasc_is_correct_2,wiqa_effect_with_label_answer,dream_generate_last_utterance,adversarial_qa_dbert_based_on,dream_answer_to_dialogue,sciq_Multiple_Choice_Question_First,quail_context_question_description_answer_text,wiki_qa_Direct_Answer_to_Question,ropes_background_new_situation_answer,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,quoref_What_Is_The_Answer,dbpedia_14_given_a_choice_of_categories_,qasc_qa_with_separated_facts_2,glue_mrpc_2_0_0,gem_e2e_nlg_1_1_0,anli_r1_0_1_0,race_middle_Read_the_article_and_answer_the_question_no_option_,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,adversarial_qa_dbidaf_based_on | lora |
| cluster_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii,quail_description_context_question_answer_id,kilt_tasks_hotpotqa_final_exam,cot_gsm8k,cos_e_v1_11_aligned_with_common_sense,squad_v2_0_3_0_0,duorc_SelfRC_movie_director,anli_r2_0_1_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,duorc_ParaphraseRC_answer_question,ropes_background_situation_middle,dream_generate_first_utterance,quail_context_question_description_text,adversarial_qa_droberta_based_on,glue_wnli_2_0_0,super_glue_record_1_0_2,web_questions_get_the_answer,ropes_prompt_mix,app_reviews_generate_review,cos_e_v1_11_rationale,adversarial_qa_droberta_generate_question,yelp_polarity_reviews_0_2_0,ropes_given_background_situation,qasc_qa_with_separated_facts_3,quoref_Guess_Answer | lora |
| cluster_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article,cot_qasc,definite_pronoun_resolution_1_1_0,wiki_qa_found_on_google,wiki_bio_comprehension,wiki_qa_Topic_Prediction_Question_Only,wiki_bio_guess_person,fix_punct,race_middle_Select_the_best_answer_no_instructions_,quac_1_0_0,wiqa_does_the_supposed_perturbation_have_an_effect,quartz_given_the_fact_answer_the_q,ropes_new_situation_background_answer,social_i_qa_Generate_answer,gigaword_1_2_0,duorc_SelfRC_decide_worth_it,kilt_tasks_hotpotqa_straighforward_qa,quail_no_prompt_text,cot_esnli,quoref_Answer_Friend_Question,race_middle_Select_the_best_answer_generate_span_,unified_qa_science_inst,sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_strategyqa | lora |
| cluster_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0,quarel_choose_between,wiki_hop_original_explain_relation,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,race_high_Select_the_best_answer_no_instructions_,kilt_tasks_hotpotqa_combining_facts,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_generate_question,glue_cola_2_0_0,imdb_reviews_plain_text_1_0_0,squad_v1_1_3_0_0,race_high_Is_this_the_right_answer,qasc_qa_with_separated_facts_1,glue_sst2_2_0_0,wiqa_what_is_the_missing_first_step,duorc_ParaphraseRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,wiki_qa_Jeopardy_style,quartz_answer_question_below,ropes_prompt_beginning,ropes_read_background_situation,wiki_qa_Decide_good_answer,super_glue_cb_1_0_2,qasc_qa_with_separated_facts_4,cot_ecqa_ii | lora |
Last updated on: 2024-01-26T14:57:09.000Z
| [
"SCIQ"
] |
ostapeno/library-gptneo_1B_flan_5ep_underparam | ostapeno | null | [
"region:us"
] | "2024-01-25T23:24:02Z" | 2024-02-08T18:50:18+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 256
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Is_this_the_right_answer | lora |
| true_case | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/true_case | lora |
| cot_strategyqa_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_strategyqa_ii | lora |
| quarel_do_not_use | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_do_not_use | lora |
| word_segment | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Decide_good_answer | lora |
| stream_aqua_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/stream_aqua_ii | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| duorc_ParaphraseRC_movie_director | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cot_gsm8k_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_gsm8k_ii | lora |
| social_i_qa_Show_choices_and_generate_index | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dream_generate_last_utterance | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dream_generate_last_utterance | lora |
| quail_context_question_description_answer_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_question_description_answer_id | lora |
| cot_creak_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_creak_ii | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| cot_esnli | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_esnli | lora |
| anli_r3_0_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/anli_r3_0_1_0 | lora |
| adversarial_qa_droberta_question_context_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| wiki_bio_who | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_bio_who | lora |
| cos_e_v1_11_i_think | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_i_think | lora |
| gem_wiki_lingua_english_en_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_prompt_bottom_no_hint | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| wiki_qa_Topic_Prediction_Answer_Only | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| cot_creak | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_creak | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/trivia_qa_rc_1_1_0 | lora |
| duorc_SelfRC_title_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_title_generation | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| glue_mnli_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_mnli_2_0_0 | lora |
| quail_context_question_description_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Guess_Answer | lora |
| adversarial_qa_dbidaf_generate_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| adversarial_qa_droberta_answer_the_following_q | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| cos_e_v1_11_question_description_option_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| wiki_hop_original_generate_subject_and_object | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| para_crawl_enes | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/para_crawl_enes | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| adversarial_qa_dbert_generate_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbert_generate_question | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| cot_strategyqa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_strategyqa | lora |
| duorc_ParaphraseRC_question_answering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| adversarial_qa_dbert_based_on | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| race_high_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| cot_gsm8k | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_gsm8k | lora |
| glue_mrpc_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_mrpc_2_0_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| app_reviews_categorize_rating_using_review | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/app_reviews_categorize_rating_using_review | lora |
| adversarial_qa_dbert_answer_the_following_q | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| quail_context_question_answer_description_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_question_answer_description_text | lora |
| qasc_qa_with_combined_facts_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| quoref_What_Is_The_Answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_description_question_answer_text | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_stsb_2_0_0 | lora |
| qasc_qa_with_separated_facts_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| web_questions_short_general_knowledge_q | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_testing_students | lora |
| cnn_dailymail_3_4_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cnn_dailymail_3_4_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_complex_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| huggingface_xsum | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/huggingface_xsum | lora |
| cot_sensemaking_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_sensemaking_ii | lora |
| glue_sst2_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_sst2_2_0_0 | lora |
| dbpedia_14_pick_one_category_for_the_following_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| qasc_is_correct_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_is_correct_2 | lora |
| wiki_qa_Generate_Question_from_Topic | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quartz_given_the_fact_answer_the_q | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| wmt16_translate_ro_en_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| cot_sensemaking | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_sensemaking | lora |
| wiki_bio_what_content | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Direct_Question | lora |
| quail_context_question_description_answer_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_prompt_mix | lora |
| quartz_answer_question_based_on | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_answer_question_based_on | lora |
| fix_punct | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/fix_punct | lora |
| qasc_is_correct_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_is_correct_1 | lora |
| cos_e_v1_11_question_description_option_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| wmt16_translate_fi_en_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_multirc_1_0_2 | lora |
| wiki_qa_Is_This_True_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/multi_news_1_0_0 | lora |
| web_questions_question_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/web_questions_question_answer | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_choose_between | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_choose_between | lora |
| adversarial_qa_droberta_tell_what_it_is | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| cot_ecqa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_ecqa | lora |
| web_questions_get_the_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_movie_director | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_movie_director | lora |
| dream_answer_to_dialogue | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dream_answer_to_dialogue | lora |
| ropes_plain_background_situation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_plain_background_situation | lora |
| cos_e_v1_11_rationale | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_rationale | lora |
| duorc_ParaphraseRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_exercise | lora |
| kilt_tasks_hotpotqa_straighforward_qa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| wiki_hop_original_explain_relation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| natural_questions_open_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/natural_questions_open_1_0_0 | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/anli_r1_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Given_Context_Answer_Question | lora |
| duorc_SelfRC_answer_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_answer_question | lora |
| wmt16_translate_de_en_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| duorc_ParaphraseRC_extract_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| unified_qa_science_inst | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_description_context_question_text | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| quarel_logic_test | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Jeopardy_style | lora |
| wiki_qa_automatic_system | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| duorc_SelfRC_build_story_around_qa | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Answer_Friend_Question | lora |
| quartz_having_read_above_passage | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_having_read_above_passage | lora |
| glue_cola_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_cola_2_0_0 | lora |
| wiqa_effect_with_string_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_effect_with_string_answer | lora |
| duorc_ParaphraseRC_answer_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ag_news_subset_1_0_0 | lora |
| dbpedia_14_given_a_choice_of_categories_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| dream_baseline | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dream_baseline | lora |
| qasc_qa_with_separated_facts_4 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_read_passage_below_choose | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_plain_no_background | lora |
| qasc_qa_with_separated_facts_5 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Read_And_Extract_ | lora |
| duorc_ParaphraseRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| duorc_SelfRC_question_answering | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_question_answering | lora |
| definite_pronoun_resolution_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| stream_qed | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/stream_qed | lora |
| app_reviews_convert_to_rating | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/app_reviews_convert_to_rating | lora |
| wiqa_what_might_be_the_first_step_of_the_process | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| social_i_qa_Show_choices_and_generate_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| cos_e_v1_11_generate_explanation_given_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| quartz_use_info_from_question_paragraph | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_use_info_from_question_paragraph | lora |
| anli_r2_0_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/anli_r2_0_1_0 | lora |
| duorc_ParaphraseRC_generate_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| cos_e_v1_11_aligned_with_common_sense | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| duorc_SelfRC_extract_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_extract_answer | lora |
| race_middle_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_qasc | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_qasc | lora |
| adversarial_qa_dbert_tell_what_it_is | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| paws_wiki_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/paws_wiki_1_1_0 | lora |
| quail_context_description_question_answer_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| kilt_tasks_hotpotqa_final_exam | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| trec_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_effect_with_label_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| snli_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/snli_1_1_0 | lora |
| cot_ecqa_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_ecqa_ii | lora |
| quail_context_question_answer_description_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_question_answer_description_id | lora |
| gigaword_1_2_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gigaword_1_2_0 | lora |
| cos_e_v1_11_question_option_description_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| glue_qnli_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_qnli_2_0_0 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| kilt_tasks_hotpotqa_formulate | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| quartz_paragraph_question_plain_concat | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_paragraph_question_plain_concat | lora |
| adversarial_qa_dbert_question_context_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| ropes_prompt_bottom_hint_beginning | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| adversarial_qa_droberta_based_on | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_droberta_based_on | lora |
| dream_generate_first_utterance | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dream_generate_first_utterance | lora |
| duorc_SelfRC_decide_worth_it | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| quail_context_description_question_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_context_description_question_text | lora |
| race_high_Is_this_the_right_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/drop_2_0_0 | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| glue_wnli_2_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/glue_wnli_2_0_0 | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| wiki_qa_Direct_Answer_to_Question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| web_questions_whats_the_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_found_on_google | lora |
| quail_no_prompt_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_no_prompt_text | lora |
| duorc_ParaphraseRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| cos_e_v1_11_question_option_description_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| super_glue_copa_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_copa_1_0_2 | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| app_reviews_convert_to_star_rating | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/app_reviews_convert_to_star_rating | lora |
| gem_web_nlg_en_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| quoref_Context_Contains_Answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Context_Contains_Answer | lora |
| gem_e2e_nlg_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| quoref_Answer_Question_Given_Context | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quartz_answer_question_below | lora |
| duorc_SelfRC_generate_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_SelfRC_generate_question | lora |
| race_high_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Select_the_best_answer | lora |
| stream_qed_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/stream_qed_ii | lora |
| cos_e_v1_11_description_question_option_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_bio_comprehension | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_bio_comprehension | lora |
| duorc_ParaphraseRC_title_generation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_no_prompt_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_no_prompt_id | lora |
| adversarial_qa_dbidaf_answer_the_following_q | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| quoref_Found_Context_Online | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Select_the_best_answer | lora |
| race_middle_Taking_a_test | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Answer_Test | lora |
| gem_common_gen_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gem_common_gen_1_1_0 | lora |
| race_high_Taking_a_test | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_description_context_question_answer_id | lora |
| gem_dart_1_1_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/gem_dart_1_1_0 | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quac_1_0_0 | lora |
| cosmos_qa_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cosmos_qa_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| lambada_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/lambada_1_0_0 | lora |
| ropes_given_background_situation | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| adversarial_qa_droberta_generate_question | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_droberta_generate_question | lora |
| adversarial_qa_dbidaf_question_context_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| adversarial_qa_dbidaf_based_on | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| cot_esnli_ii | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/cot_esnli_ii | lora |
| quail_description_context_question_answer_text | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/super_glue_record_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
Last updated on: 2024-01-26 17:15:01+00:00
| [
"SCIQ"
] |
zhan1993/gptneo_1B_flan_random-epoch_1 | zhan1993 | null | [
"region:us"
] | "2024-01-26T00:53:18Z" | 2024-01-26T15:02:22+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5,wiki_qa_automatic_system,stream_aqua_ii,dbpedia_14_pick_one_category_for_the_following_text,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Generate_Question_from_Topic,cot_gsm8k_ii,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_interrogative_2,quail_context_question_answer_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,qasc_qa_with_combined_facts_1,adversarial_qa_dbert_answer_the_following_q,social_i_qa_I_was_wondering,stream_aqua,word_segment,ropes_plain_no_background,super_glue_multirc_1_0_2,wiki_hop_original_choose_best_object_affirmative_3,app_reviews_convert_to_rating,anli_r3_0_1_0,app_reviews_convert_to_star_rating,quartz_paragraph_question_plain_concat,kilt_tasks_hotpotqa_complex_question,quartz_use_info_from_paragraph_question | lora |
| cluster_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0,para_crawl_enes,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_id,glue_stsb_2_0_0,quail_context_description_question_answer_text,dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_description_option_text,cos_e_v1_11_description_question_option_text,race_high_Write_a_multi_choice_question_options_given_,ropes_prompt_bottom_no_hint,quarel_testing_students,wmt16_translate_tr_en_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_final_step_of_the_following_process,quoref_Given_Context_Answer_Question,wiki_hop_original_generate_object,quartz_use_info_from_question_paragraph,duorc_SelfRC_build_story_around_qa,drop_2_0_0,wiqa_effect_with_string_answer,race_high_Taking_a_test,wiki_hop_original_generate_subject_and_object,glue_qqp_2_0_0,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| cluster_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0,duorc_SelfRC_question_answering,cot_sensemaking_ii,huggingface_xsum,duorc_ParaphraseRC_build_story_around_qa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,quartz_answer_question_based_on,ropes_plain_background_situation,race_middle_Write_a_multi_choice_question_for_the_following_article,quail_description_context_question_text,web_questions_whats_the_answer,cot_ecqa,true_case,adversarial_qa_dbert_question_context_answer,duorc_SelfRC_title_generation,quail_context_question_description_answer_id,quarel_do_not_use,adversarial_qa_dbidaf_answer_the_following_q,duorc_ParaphraseRC_decide_worth_it,race_middle_Is_this_the_right_answer,wmt16_translate_de_en_1_0_0,wiki_qa_Is_This_True_,race_middle_Select_the_best_answer,aeslc_1_0_0,duorc_ParaphraseRC_question_answering,wiki_hop_original_choose_best_object_affirmative_1 | lora |
| cluster_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_explain_why_human,cot_sensemaking,quoref_Find_Answer,quail_context_description_question_text,social_i_qa_Generate_the_question_from_the_answer,quartz_having_read_above_passage,stream_qed_ii,wiqa_what_might_be_the_first_step_of_the_process,wiki_bio_who,duorc_SelfRC_generate_question_by_answer,race_high_Select_the_best_answer_generate_span_,lambada_1_0_0,coqa_1_0_0,race_high_Select_the_best_answer,adversarial_qa_droberta_question_context_answer,quail_context_question_answer_description_id,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_tell_what_it_is,cnn_dailymail_3_4_0,quail_no_prompt_id,ag_news_subset_1_0_0,trivia_qa_rc_1_1_0,ropes_prompt_bottom_hint_beginning,super_glue_wic_1_0_2 | lora |
| cluster_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2,duorc_ParaphraseRC_movie_director,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,quartz_read_passage_below_choose,social_i_qa_Check_if_a_random_answer_is_valid_or_not,cot_creak,ropes_plain_bottom_hint,super_glue_copa_1_0_2,natural_questions_open_1_0_0,trec_1_0_0,gem_web_nlg_en_1_1_0,wiki_bio_key_content,wmt16_translate_fi_en_1_0_0,quoref_Answer_Question_Given_Context,duorc_ParaphraseRC_generate_question,math_dataset_algebra__linear_1d_1_0_0,duorc_ParaphraseRC_title_generation,quail_context_description_question_answer_id,wiki_bio_what_content,adversarial_qa_dbert_tell_what_it_is,sciq_Multiple_Choice_Closed_Book_,duorc_ParaphraseRC_extract_answer,dream_baseline,gem_wiki_lingua_english_en_1_1_0 | lora |
| cluster_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0,quarel_choose_between,wiki_hop_original_explain_relation,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,race_high_Select_the_best_answer_no_instructions_,kilt_tasks_hotpotqa_combining_facts,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_generate_question,glue_cola_2_0_0,imdb_reviews_plain_text_1_0_0,squad_v1_1_3_0_0,race_high_Is_this_the_right_answer,qasc_qa_with_separated_facts_1,glue_sst2_2_0_0,wiqa_what_is_the_missing_first_step,duorc_ParaphraseRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,wiki_qa_Jeopardy_style,quartz_answer_question_below,ropes_prompt_beginning,ropes_read_background_situation,wiki_qa_Decide_good_answer,super_glue_cb_1_0_2,qasc_qa_with_separated_facts_4,cot_ecqa_ii | lora |
| cluster_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed,cot_esnli_ii,quarel_heres_a_story,quoref_Guess_Title_For_Context,qasc_is_correct_2,wiqa_effect_with_label_answer,dream_generate_last_utterance,adversarial_qa_dbert_based_on,dream_answer_to_dialogue,sciq_Multiple_Choice_Question_First,quail_context_question_description_answer_text,wiki_qa_Direct_Answer_to_Question,ropes_background_new_situation_answer,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,quoref_What_Is_The_Answer,dbpedia_14_given_a_choice_of_categories_,qasc_qa_with_separated_facts_2,glue_mrpc_2_0_0,gem_e2e_nlg_1_1_0,anli_r1_0_1_0,race_middle_Read_the_article_and_answer_the_question_no_option_,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,adversarial_qa_dbidaf_based_on | lora |
| cluster_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii,quail_description_context_question_answer_id,kilt_tasks_hotpotqa_final_exam,cot_gsm8k,cos_e_v1_11_aligned_with_common_sense,squad_v2_0_3_0_0,duorc_SelfRC_movie_director,anli_r2_0_1_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,duorc_ParaphraseRC_answer_question,ropes_background_situation_middle,dream_generate_first_utterance,quail_context_question_description_text,adversarial_qa_droberta_based_on,glue_wnli_2_0_0,super_glue_record_1_0_2,web_questions_get_the_answer,ropes_prompt_mix,app_reviews_generate_review,cos_e_v1_11_rationale,adversarial_qa_droberta_generate_question,yelp_polarity_reviews_0_2_0,ropes_given_background_situation,qasc_qa_with_separated_facts_3,quoref_Guess_Answer | lora |
| cluster_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article,cot_qasc,definite_pronoun_resolution_1_1_0,wiki_qa_found_on_google,wiki_bio_comprehension,wiki_qa_Topic_Prediction_Question_Only,wiki_bio_guess_person,fix_punct,race_middle_Select_the_best_answer_no_instructions_,quac_1_0_0,wiqa_does_the_supposed_perturbation_have_an_effect,quartz_given_the_fact_answer_the_q,ropes_new_situation_background_answer,social_i_qa_Generate_answer,gigaword_1_2_0,duorc_SelfRC_decide_worth_it,kilt_tasks_hotpotqa_straighforward_qa,quail_no_prompt_text,cot_esnli,quoref_Answer_Friend_Question,race_middle_Select_the_best_answer_generate_span_,unified_qa_science_inst,sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_strategyqa | lora |
| cluster_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id,social_i_qa_Show_choices_and_generate_index,web_questions_potential_correct_answer,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cosmos_qa_1_0_0,sciq_Direct_Question,super_glue_wsc_fixed_1_0_2,race_middle_Taking_a_test,wmt14_translate_fr_en_1_0_0,duorc_SelfRC_extract_answer,wiki_hop_original_generate_subject,duorc_SelfRC_answer_question,qasc_is_correct_1,cos_e_v1_11_i_think,wiki_qa_exercise,race_middle_Write_a_multi_choice_question_options_given_,quoref_Read_And_Extract_,web_questions_short_general_knowledge_q,web_questions_question_answer,quarel_logic_test,app_reviews_categorize_rating_using_review,cot_strategyqa_ii,glue_mnli_2_0_0,quoref_Answer_Test,super_glue_rte_1_0_2 | lora |
Last updated on: 2024-01-26T14:59:27.000Z
| [
"SCIQ"
] |
ntc-ai/SDXL-LoRA-slider.wearing-a-suit-and-tie | ntc-ai | text-to-image | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | "2024-01-26T01:27:49Z" | 2024-01-26T01:27:57+00:00 | 0 | 0 | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/evaluate/wearing a suit and tie.../wearing a suit and tie_17_3.0.png
widget:
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_17_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_19_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_20_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_21_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_22_3.0.png
inference: false
instance_prompt: wearing a suit and tie
---
# ntcai.xyz slider - wearing a suit and tie (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/wearing a suit and tie_17_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_17_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_17_3.0.png" width=256 height=256 /> |
| <img src="images/wearing a suit and tie_19_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_19_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_19_3.0.png" width=256 height=256 /> |
| <img src="images/wearing a suit and tie_20_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_20_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
wearing a suit and tie
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.wearing-a-suit-and-tie', weight_name='wearing a suit and tie.safetensors', adapter_name="wearing a suit and tie")
# Activate the LoRA
pipe.set_adapters(["wearing a suit and tie"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, wearing a suit and tie"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
| [
"CRAFT"
] |
zhan1993/gptneo_1B_flan_random-epoch_2 | zhan1993 | null | [
"region:us"
] | "2024-01-26T03:22:38Z" | 2024-01-26T17:08:25+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5,wiki_qa_automatic_system,stream_aqua_ii,dbpedia_14_pick_one_category_for_the_following_text,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Generate_Question_from_Topic,cot_gsm8k_ii,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_option_description_text,wiki_hop_original_choose_best_object_interrogative_2,quail_context_question_answer_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,qasc_qa_with_combined_facts_1,adversarial_qa_dbert_answer_the_following_q,social_i_qa_I_was_wondering,stream_aqua,word_segment,ropes_plain_no_background,super_glue_multirc_1_0_2,wiki_hop_original_choose_best_object_affirmative_3,app_reviews_convert_to_rating,anli_r3_0_1_0,app_reviews_convert_to_star_rating,quartz_paragraph_question_plain_concat,kilt_tasks_hotpotqa_complex_question,quartz_use_info_from_paragraph_question | lora |
| cluster_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/multi_news_1_0_0,para_crawl_enes,quoref_Context_Contains_Answer,cos_e_v1_11_description_question_option_id,glue_stsb_2_0_0,quail_context_description_question_answer_text,dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_description_option_text,cos_e_v1_11_description_question_option_text,race_high_Write_a_multi_choice_question_options_given_,ropes_prompt_bottom_no_hint,quarel_testing_students,wmt16_translate_tr_en_1_0_0,duorc_SelfRC_generate_question,wiqa_what_is_the_final_step_of_the_following_process,quoref_Given_Context_Answer_Question,wiki_hop_original_generate_object,quartz_use_info_from_question_paragraph,duorc_SelfRC_build_story_around_qa,drop_2_0_0,wiqa_effect_with_string_answer,race_high_Taking_a_test,wiki_hop_original_generate_subject_and_object,glue_qqp_2_0_0,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| cluster_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/snli_1_1_0,duorc_SelfRC_question_answering,cot_sensemaking_ii,huggingface_xsum,duorc_ParaphraseRC_build_story_around_qa,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,quartz_answer_question_based_on,ropes_plain_background_situation,race_middle_Write_a_multi_choice_question_for_the_following_article,quail_description_context_question_text,web_questions_whats_the_answer,cot_ecqa,true_case,adversarial_qa_dbert_question_context_answer,duorc_SelfRC_title_generation,quail_context_question_description_answer_id,quarel_do_not_use,adversarial_qa_dbidaf_answer_the_following_q,duorc_ParaphraseRC_decide_worth_it,race_middle_Is_this_the_right_answer,wmt16_translate_de_en_1_0_0,wiki_qa_Is_This_True_,race_middle_Select_the_best_answer,aeslc_1_0_0,duorc_ParaphraseRC_question_answering,wiki_hop_original_choose_best_object_affirmative_1 | lora |
| cluster_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quoref_Found_Context_Online,social_i_qa_Show_choices_and_generate_answer,cos_e_v1_11_explain_why_human,cot_sensemaking,quoref_Find_Answer,quail_context_description_question_text,social_i_qa_Generate_the_question_from_the_answer,quartz_having_read_above_passage,stream_qed_ii,wiqa_what_might_be_the_first_step_of_the_process,wiki_bio_who,duorc_SelfRC_generate_question_by_answer,race_high_Select_the_best_answer_generate_span_,lambada_1_0_0,coqa_1_0_0,race_high_Select_the_best_answer,adversarial_qa_droberta_question_context_answer,quail_context_question_answer_description_id,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_tell_what_it_is,cnn_dailymail_3_4_0,quail_no_prompt_id,ag_news_subset_1_0_0,trivia_qa_rc_1_1_0,ropes_prompt_bottom_hint_beginning,super_glue_wic_1_0_2 | lora |
| cluster_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2,duorc_ParaphraseRC_movie_director,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,quartz_read_passage_below_choose,social_i_qa_Check_if_a_random_answer_is_valid_or_not,cot_creak,ropes_plain_bottom_hint,super_glue_copa_1_0_2,natural_questions_open_1_0_0,trec_1_0_0,gem_web_nlg_en_1_1_0,wiki_bio_key_content,wmt16_translate_fi_en_1_0_0,quoref_Answer_Question_Given_Context,duorc_ParaphraseRC_generate_question,math_dataset_algebra__linear_1d_1_0_0,duorc_ParaphraseRC_title_generation,quail_context_description_question_answer_id,wiki_bio_what_content,adversarial_qa_dbert_tell_what_it_is,sciq_Multiple_Choice_Closed_Book_,duorc_ParaphraseRC_extract_answer,dream_baseline,gem_wiki_lingua_english_en_1_1_0 | lora |
| cluster_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id,social_i_qa_Show_choices_and_generate_index,web_questions_potential_correct_answer,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cosmos_qa_1_0_0,sciq_Direct_Question,super_glue_wsc_fixed_1_0_2,race_middle_Taking_a_test,wmt14_translate_fr_en_1_0_0,duorc_SelfRC_extract_answer,wiki_hop_original_generate_subject,duorc_SelfRC_answer_question,qasc_is_correct_1,cos_e_v1_11_i_think,wiki_qa_exercise,race_middle_Write_a_multi_choice_question_options_given_,quoref_Read_And_Extract_,web_questions_short_general_knowledge_q,web_questions_question_answer,quarel_logic_test,app_reviews_categorize_rating_using_review,cot_strategyqa_ii,glue_mnli_2_0_0,quoref_Answer_Test,super_glue_rte_1_0_2 | lora |
| cluster_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/gem_dart_1_1_0,quarel_choose_between,wiki_hop_original_explain_relation,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,race_high_Select_the_best_answer_no_instructions_,kilt_tasks_hotpotqa_combining_facts,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_generate_question,glue_cola_2_0_0,imdb_reviews_plain_text_1_0_0,squad_v1_1_3_0_0,race_high_Is_this_the_right_answer,qasc_qa_with_separated_facts_1,glue_sst2_2_0_0,wiqa_what_is_the_missing_first_step,duorc_ParaphraseRC_generate_question_by_answer,wmt16_translate_ro_en_1_0_0,wiki_qa_Jeopardy_style,quartz_answer_question_below,ropes_prompt_beginning,ropes_read_background_situation,wiki_qa_Decide_good_answer,super_glue_cb_1_0_2,qasc_qa_with_separated_facts_4,cot_ecqa_ii | lora |
| cluster_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cot_creak_ii,quail_description_context_question_answer_id,kilt_tasks_hotpotqa_final_exam,cot_gsm8k,cos_e_v1_11_aligned_with_common_sense,squad_v2_0_3_0_0,duorc_SelfRC_movie_director,anli_r2_0_1_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,duorc_ParaphraseRC_answer_question,ropes_background_situation_middle,dream_generate_first_utterance,quail_context_question_description_text,adversarial_qa_droberta_based_on,glue_wnli_2_0_0,super_glue_record_1_0_2,web_questions_get_the_answer,ropes_prompt_mix,app_reviews_generate_review,cos_e_v1_11_rationale,adversarial_qa_droberta_generate_question,yelp_polarity_reviews_0_2_0,ropes_given_background_situation,qasc_qa_with_separated_facts_3,quoref_Guess_Answer | lora |
| cluster_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article,cot_qasc,definite_pronoun_resolution_1_1_0,wiki_qa_found_on_google,wiki_bio_comprehension,wiki_qa_Topic_Prediction_Question_Only,wiki_bio_guess_person,fix_punct,race_middle_Select_the_best_answer_no_instructions_,quac_1_0_0,wiqa_does_the_supposed_perturbation_have_an_effect,quartz_given_the_fact_answer_the_q,ropes_new_situation_background_answer,social_i_qa_Generate_answer,gigaword_1_2_0,duorc_SelfRC_decide_worth_it,kilt_tasks_hotpotqa_straighforward_qa,quail_no_prompt_text,cot_esnli,quoref_Answer_Friend_Question,race_middle_Select_the_best_answer_generate_span_,unified_qa_science_inst,sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_strategyqa | lora |
| cluster_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/stream_qed,cot_esnli_ii,quarel_heres_a_story,quoref_Guess_Title_For_Context,qasc_is_correct_2,wiqa_effect_with_label_answer,dream_generate_last_utterance,adversarial_qa_dbert_based_on,dream_answer_to_dialogue,sciq_Multiple_Choice_Question_First,quail_context_question_description_answer_text,wiki_qa_Direct_Answer_to_Question,ropes_background_new_situation_answer,adversarial_qa_droberta_answer_the_following_q,kilt_tasks_hotpotqa_formulate,quoref_What_Is_The_Answer,dbpedia_14_given_a_choice_of_categories_,qasc_qa_with_separated_facts_2,glue_mrpc_2_0_0,gem_e2e_nlg_1_1_0,anli_r1_0_1_0,race_middle_Read_the_article_and_answer_the_question_no_option_,wiki_hop_original_choose_best_object_interrogative_1,quail_description_context_question_answer_text,adversarial_qa_dbidaf_based_on | lora |
Last updated on: 2024-01-26T15:04:01.000Z
| [
"SCIQ"
] |
CyberHarem/leto_arknights | CyberHarem | text-to-image | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:Cyberharem/leto_arknights",
"license:mit",
"region:us"
] | "2024-01-26T12:04:30Z" | 2024-03-23T00:17:49+00:00 | 0 | 0 | ---
datasets:
- Cyberharem/leto_arknights
license: mit
pipeline_tag: text-to-image
tags:
- art
- not-for-all-audiences
---
# LoRA model of leto/烈夏 (Arknights)
## What Is This?
This is the LoRA model of waifu leto/烈夏 (Arknights).
## How Is It Trained?
* This model is trained with [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts), and the test images are generated with [a1111's webui](AUTOMATIC1111/stable-diffusion-webui) and [API sdk](https://github.com/mix1009/sdwebuiapi).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The architecture of base model is is `SD1.5`.
* Dataset used for training is the `stage3-p480-1200` in [Cyberharem/leto_arknights](https://huggingface.co/datasets/Cyberharem/leto_arknights), which contains 83 images.
* **Trigger word is `leto_arknights`.**
* Pruned core tags for this waifu are `animal ears, bear ears, multicolored hair, streaked hair, brown hair, hair ornament, red eyes, short hair, black hair, white hair, long hair`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
* For more details in training, you can take a look at [training configuration file](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/train.toml).
* For more details in LoRA, you can download it, and read the metadata with a1111's webui.
## How to Use It?
After downloading the safetensors files for the specified step, you need to use them like common LoRA.
* Recommended LoRA weight is 0.5-0.85.
* Recommended trigger word weight is 0.7-1.1.
For example, if you want to use the model from step 2070, you need to download [`2070/leto_arknights.safetensors`](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/2070/leto_arknights.safetensors) as LoRA. By using this model, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 2070.
1026 images (1.03 GiB) were generated for auto-testing.
The base model used for generating preview images is [meinamix_v11](https://huggingface.co/meinamix_v11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_0_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 2070 | 69 | 0.917 | 0.984 | **0.862** | **0.681** | [Download](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/2070/leto_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2400 | 80 | 0.917 | 0.989 | 0.858 | 0.675 | [Download](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/2400/leto_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1170 | 39 | 0.913 | **0.991** | 0.858 | 0.671 | [Download](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/1170/leto_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 450 | 15 | 0.919 | 0.988 | 0.855 | 0.671 | [Download](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/450/leto_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1080 | 36 | **0.921** | 0.990 | 0.851 | 0.666 | [Download](https://huggingface.co/Cyberharem/leto_arknights/resolve/main/1080/leto_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1620 to 2400](all/0.md)
* [Steps From 720 to 1530](all/1.md)
* [Steps From 90 to 630](all/2.md)
| [
"BEAR"
] |
zhan1993/gptneo_1B_flan_10_experts-epoch_0 | zhan1993 | null | [
"region:us"
] | "2024-01-26T22:17:58Z" | 2024-01-27T14:20:24+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_10experts_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_record_1_0_2,quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quac_1_0_0,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| cluster_10experts_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,ropes_background_new_situation_answer,ropes_plain_background_situation,adversarial_qa_droberta_tell_what_it_is,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,adversarial_qa_droberta_based_on,adversarial_qa_dbert_answer_the_following_q,ropes_prompt_beginning,ropes_read_background_situation,adversarial_qa_droberta_question_context_answer,ropes_plain_bottom_hint,quoref_Answer_Question_Given_Context,squad_v2_0_3_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is,ropes_prompt_mix,ropes_background_situation_middle,squad_v1_1_3_0_0 | lora |
| cluster_10experts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0,trec_1_0_0,cos_e_v1_11_rationale,natural_questions_open_1_0_0,web_questions_whats_the_answer,kilt_tasks_hotpotqa_final_exam,cos_e_v1_11_generate_explanation_given_text,cot_gsm8k_ii,web_questions_question_answer,stream_aqua,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,cos_e_v1_11_i_think,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_ecqa_ii,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,cos_e_v1_11_explain_why_human,cot_creak_ii,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,stream_qed_ii,wiki_bio_guess_person,cos_e_v1_11_aligned_with_common_sense,cot_sensemaking_ii,kilt_tasks_hotpotqa_formulate,social_i_qa_I_was_wondering | lora |
| cluster_10experts_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2,wiki_qa_found_on_google,super_glue_wic_1_0_2,glue_cola_2_0_0,paws_wiki_1_1_0,super_glue_wsc_fixed_1_0_2,wiki_qa_Is_This_True_,snli_1_1_0,glue_qqp_2_0_0,wiki_qa_automatic_system,qasc_is_correct_1,glue_stsb_2_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,social_i_qa_Check_if_a_random_answer_is_valid_or_not,qasc_is_correct_2,glue_wnli_2_0_0,glue_mrpc_2_0_0 | lora |
| cluster_10experts_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_sensemaking,yelp_polarity_reviews_0_2_0,anli_r3_0_1_0,lambada_1_0_0,wmt16_translate_ro_en_1_0_0,dream_generate_last_utterance,ag_news_subset_1_0_0,gem_dart_1_1_0,gem_common_gen_1_1_0,cot_creak,adversarial_qa_dbidaf_generate_question,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,gem_web_nlg_en_1_1_0,word_segment,anli_r2_0_1_0,app_reviews_generate_review,wmt16_translate_de_en_1_0_0,anli_r1_0_1_0,cot_ecqa,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,social_i_qa_Generate_the_question_from_the_answer,para_crawl_enes,race_high_Write_a_multi_choice_question_options_given_,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0,cosmos_qa_1_0_0,cot_esnli_ii | lora |
| cluster_10experts_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_middle_Is_this_the_right_answer,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,race_high_Is_this_the_right_answer,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,dream_baseline,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| cluster_10experts_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,cot_gsm8k,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,cnn_dailymail_3_4_0,duorc_SelfRC_build_story_around_qa,gem_wiki_lingua_english_en_1_1_0,wiki_bio_what_content,race_high_Write_a_multi_choice_question_for_the_following_article,wiki_bio_who,aeslc_1_0_0,dream_answer_to_dialogue,gem_e2e_nlg_1_1_0,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| cluster_10experts_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,cos_e_v1_11_question_option_description_id,quarel_do_not_use,qasc_qa_with_separated_facts_5,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,quarel_heres_a_story,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,ropes_plain_no_background,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,quarel_testing_students,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,social_i_qa_Generate_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| cluster_10experts_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,app_reviews_categorize_rating_using_review,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,wiqa_does_the_supposed_perturbation_have_an_effect,math_dataset_algebra__linear_1d_1_0_0,unified_qa_science_inst,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cot_strategyqa,wiqa_effect_with_string_answer,wiki_qa_exercise,wiki_qa_Topic_Prediction_Question_Only,super_glue_copa_1_0_2,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,cot_qasc,cot_strategyqa_ii,stream_aqua_ii | lora |
| cluster_10experts_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_object,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_explain_relation,wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_generate_subject,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object | lora |
Last updated on: 2024-01-27T09:13:37.000Z
| [
"SCIQ"
] |
zhan1993/gptneo_1B_flan_10_experts-epoch_1 | zhan1993 | null | [
"region:us"
] | "2024-01-26T23:47:52Z" | 2024-01-27T15:23:21+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_10experts_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_record_1_0_2,quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quac_1_0_0,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| cluster_10experts_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,ropes_background_new_situation_answer,ropes_plain_background_situation,adversarial_qa_droberta_tell_what_it_is,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,adversarial_qa_droberta_based_on,adversarial_qa_dbert_answer_the_following_q,ropes_prompt_beginning,ropes_read_background_situation,adversarial_qa_droberta_question_context_answer,ropes_plain_bottom_hint,quoref_Answer_Question_Given_Context,squad_v2_0_3_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is,ropes_prompt_mix,ropes_background_situation_middle,squad_v1_1_3_0_0 | lora |
| cluster_10experts_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0,trec_1_0_0,cos_e_v1_11_rationale,natural_questions_open_1_0_0,web_questions_whats_the_answer,kilt_tasks_hotpotqa_final_exam,cos_e_v1_11_generate_explanation_given_text,cot_gsm8k_ii,web_questions_question_answer,stream_aqua,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,cos_e_v1_11_i_think,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_ecqa_ii,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,cos_e_v1_11_explain_why_human,cot_creak_ii,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,stream_qed_ii,wiki_bio_guess_person,cos_e_v1_11_aligned_with_common_sense,cot_sensemaking_ii,kilt_tasks_hotpotqa_formulate,social_i_qa_I_was_wondering | lora |
| cluster_10experts_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2,wiki_qa_found_on_google,super_glue_wic_1_0_2,glue_cola_2_0_0,paws_wiki_1_1_0,super_glue_wsc_fixed_1_0_2,wiki_qa_Is_This_True_,snli_1_1_0,glue_qqp_2_0_0,wiki_qa_automatic_system,qasc_is_correct_1,glue_stsb_2_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,social_i_qa_Check_if_a_random_answer_is_valid_or_not,qasc_is_correct_2,glue_wnli_2_0_0,glue_mrpc_2_0_0 | lora |
| cluster_10experts_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_sensemaking,yelp_polarity_reviews_0_2_0,anli_r3_0_1_0,lambada_1_0_0,wmt16_translate_ro_en_1_0_0,dream_generate_last_utterance,ag_news_subset_1_0_0,gem_dart_1_1_0,gem_common_gen_1_1_0,cot_creak,adversarial_qa_dbidaf_generate_question,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,gem_web_nlg_en_1_1_0,word_segment,anli_r2_0_1_0,app_reviews_generate_review,wmt16_translate_de_en_1_0_0,anli_r1_0_1_0,cot_ecqa,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,social_i_qa_Generate_the_question_from_the_answer,para_crawl_enes,race_high_Write_a_multi_choice_question_options_given_,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0,cosmos_qa_1_0_0,cot_esnli_ii | lora |
| cluster_10experts_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_middle_Is_this_the_right_answer,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,race_high_Is_this_the_right_answer,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,dream_baseline,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| cluster_10experts_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,cot_gsm8k,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,cnn_dailymail_3_4_0,duorc_SelfRC_build_story_around_qa,gem_wiki_lingua_english_en_1_1_0,wiki_bio_what_content,race_high_Write_a_multi_choice_question_for_the_following_article,wiki_bio_who,aeslc_1_0_0,dream_answer_to_dialogue,gem_e2e_nlg_1_1_0,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| cluster_10experts_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,cos_e_v1_11_question_option_description_id,quarel_do_not_use,qasc_qa_with_separated_facts_5,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,quarel_heres_a_story,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,ropes_plain_no_background,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,quarel_testing_students,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,social_i_qa_Generate_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| cluster_10experts_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,app_reviews_categorize_rating_using_review,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,wiqa_does_the_supposed_perturbation_have_an_effect,math_dataset_algebra__linear_1d_1_0_0,unified_qa_science_inst,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cot_strategyqa,wiqa_effect_with_string_answer,wiki_qa_exercise,wiki_qa_Topic_Prediction_Question_Only,super_glue_copa_1_0_2,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,cot_qasc,cot_strategyqa_ii,stream_aqua_ii | lora |
| cluster_10experts_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_object,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_explain_relation,wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_generate_subject,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object | lora |
Last updated on: 2024-01-27T11:57:47.000Z
| [
"SCIQ"
] |
zhan1993/gptneo_1B_flan_10_experts-epoch_2 | zhan1993 | null | [
"region:us"
] | "2024-01-27T21:46:31Z" | 2024-03-06T22:03:09+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_sensemaking,yelp_polarity_reviews_0_2_0,anli_r3_0_1_0,lambada_1_0_0,wmt16_translate_ro_en_1_0_0,dream_generate_last_utterance,ag_news_subset_1_0_0,gem_dart_1_1_0,gem_common_gen_1_1_0,cot_creak,adversarial_qa_dbidaf_generate_question,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,gem_web_nlg_en_1_1_0,word_segment,anli_r2_0_1_0,app_reviews_generate_review,wmt16_translate_de_en_1_0_0,anli_r1_0_1_0,cot_ecqa,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,social_i_qa_Generate_the_question_from_the_answer,para_crawl_enes,race_high_Write_a_multi_choice_question_options_given_,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0,cosmos_qa_1_0_0,cot_esnli_ii | lora |
| cluster_6 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2,wiki_qa_found_on_google,super_glue_wic_1_0_2,glue_cola_2_0_0,paws_wiki_1_1_0,super_glue_wsc_fixed_1_0_2,wiki_qa_Is_This_True_,snli_1_1_0,glue_qqp_2_0_0,wiki_qa_automatic_system,qasc_is_correct_1,glue_stsb_2_0_0,glue_qnli_2_0_0,glue_mnli_2_0_0,social_i_qa_Check_if_a_random_answer_is_valid_or_not,qasc_is_correct_2,glue_wnli_2_0_0,glue_mrpc_2_0_0 | lora |
| cluster_5 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,app_reviews_categorize_rating_using_review,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,wiqa_does_the_supposed_perturbation_have_an_effect,math_dataset_algebra__linear_1d_1_0_0,unified_qa_science_inst,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cot_strategyqa,wiqa_effect_with_string_answer,wiki_qa_exercise,wiki_qa_Topic_Prediction_Question_Only,super_glue_copa_1_0_2,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,cot_qasc,cot_strategyqa_ii,stream_aqua_ii | lora |
| cluster_4 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,ropes_background_new_situation_answer,ropes_plain_background_situation,adversarial_qa_droberta_tell_what_it_is,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,adversarial_qa_droberta_based_on,adversarial_qa_dbert_answer_the_following_q,ropes_prompt_beginning,ropes_read_background_situation,adversarial_qa_droberta_question_context_answer,ropes_plain_bottom_hint,quoref_Answer_Question_Given_Context,squad_v2_0_3_0_0,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is,ropes_prompt_mix,ropes_background_situation_middle,squad_v1_1_3_0_0 | lora |
| cluster_8 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,cos_e_v1_11_question_option_description_id,quarel_do_not_use,qasc_qa_with_separated_facts_5,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,quarel_heres_a_story,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,ropes_plain_no_background,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,quarel_testing_students,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,social_i_qa_Generate_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| cluster_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_middle_Is_this_the_right_answer,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,race_high_Is_this_the_right_answer,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,dream_baseline,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| cluster_1 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_sst2_2_0_0,trec_1_0_0,cos_e_v1_11_rationale,natural_questions_open_1_0_0,web_questions_whats_the_answer,kilt_tasks_hotpotqa_final_exam,cos_e_v1_11_generate_explanation_given_text,cot_gsm8k_ii,web_questions_question_answer,stream_aqua,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,cos_e_v1_11_i_think,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,cot_ecqa_ii,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,cos_e_v1_11_explain_why_human,cot_creak_ii,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,stream_qed_ii,wiki_bio_guess_person,cos_e_v1_11_aligned_with_common_sense,cot_sensemaking_ii,kilt_tasks_hotpotqa_formulate,social_i_qa_I_was_wondering | lora |
| cluster_7 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,cot_gsm8k,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,cnn_dailymail_3_4_0,duorc_SelfRC_build_story_around_qa,gem_wiki_lingua_english_en_1_1_0,wiki_bio_what_content,race_high_Write_a_multi_choice_question_for_the_following_article,wiki_bio_who,aeslc_1_0_0,dream_answer_to_dialogue,gem_e2e_nlg_1_1_0,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| cluster_9 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_record_1_0_2,quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quac_1_0_0,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| cluster_10 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_generate_object,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_explain_relation,wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_generate_subject,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object | lora |
Last updated on: 2024-01-27T21:49:34.000Z
| [
"SCIQ"
] |
zhan1993/phi2_flan_10_random_unbalanced-epoch_0 | zhan1993 | null | [
"region:us"
] | "2024-01-31T11:45:24Z" | 2024-02-08T04:28:58+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_3 | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,race_high_Write_a_multi_choice_question_for_the_following_article,kilt_tasks_hotpotqa_complex_question,quoref_Answer_Test,adversarial_qa_dbidaf_based_on,duorc_ParaphraseRC_title_generation,cot_strategyqa,sciq_Direct_Question,adversarial_qa_dbert_answer_the_following_q,quartz_paragraph_question_plain_concat,wiki_hop_original_generate_subject,race_middle_Read_the_article_and_answer_the_question_no_option_,adversarial_qa_dbert_tell_what_it_is,cos_e_v1_11_aligned_with_common_sense,anli_r2_0_1_0,cot_gsm8k,qasc_qa_with_separated_facts_1,wiqa_effect_with_string_answer,wiki_bio_what_content,cot_qasc,gem_dart_1_1_0,natural_questions_open_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,wiki_qa_Topic_Prediction_Answer_Only,dream_generate_first_utterance,dream_read_the_following_conversation_and_answer_the_question,ropes_prompt_mix,wmt16_translate_ro_en_1_0_0,gem_wiki_lingua_english_en_1_1_0,social_i_qa_Generate_answer,cot_gsm8k_ii,stream_aqua_ii,quoref_Context_Contains_Answer,quail_context_question_description_text,ropes_prompt_beginning,drop_2_0_0 | lora |
| cluster_6 | phi-2 | sordonia/flan-10k-flat/stream_aqua,anli_r3_0_1_0,quail_context_question_description_answer_id,wiki_hop_original_choose_best_object_interrogative_1,true_case,wmt16_translate_tr_en_1_0_0,qasc_is_correct_1,ropes_prompt_bottom_hint_beginning,quarel_heres_a_story,wiki_hop_original_explain_relation,adversarial_qa_droberta_answer_the_following_q,lambada_1_0_0,squad_v2_0_3_0_0,wiqa_effect_with_label_answer,cos_e_v1_11_i_think,quoref_Guess_Answer,cos_e_v1_11_question_description_option_text,wiki_qa_found_on_google,duorc_SelfRC_build_story_around_qa,quartz_read_passage_below_choose,qasc_qa_with_separated_facts_2,wiqa_what_might_be_the_last_step_of_the_process,multi_news_1_0_0,quoref_Read_And_Extract_,adversarial_qa_dbert_question_context_answer,app_reviews_categorize_rating_using_review,qasc_is_correct_2 | lora |
| cluster_5 | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer,duorc_SelfRC_answer_question,wiki_qa_Is_This_True_,cos_e_v1_11_explain_why_human,race_middle_Write_a_multi_choice_question_options_given_,dbpedia_14_pick_one_category_for_the_following_text,quartz_answer_question_based_on,fix_punct,squad_v1_1_3_0_0,sciq_Multiple_Choice_Question_First,quoref_Given_Context_Answer_Question,super_glue_copa_1_0_2,cnn_dailymail_3_4_0,race_middle_Is_this_the_right_answer,quail_context_description_question_answer_text,race_high_Read_the_article_and_answer_the_question_no_option_,duorc_ParaphraseRC_generate_question_by_answer,imdb_reviews_plain_text_1_0_0,quartz_use_info_from_question_paragraph,ropes_plain_bottom_hint,quarel_choose_between,glue_sst2_2_0_0,adversarial_qa_dbert_based_on,wmt16_translate_de_en_1_0_0 | lora |
| cluster_4 | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id,duorc_ParaphraseRC_movie_director,super_glue_cb_1_0_2,wiqa_what_is_the_final_step_of_the_following_process,cot_creak,glue_mnli_2_0_0,wiki_qa_Topic_Prediction_Question_Only,quarel_logic_test,ropes_plain_no_background,gem_e2e_nlg_1_1_0,wiqa_does_the_supposed_perturbation_have_an_effect,cot_ecqa,quarel_testing_students,wiki_bio_comprehension,wmt14_translate_fr_en_1_0_0,cos_e_v1_11_description_question_option_text,social_i_qa_Check_if_a_random_answer_is_valid_or_not,duorc_ParaphraseRC_decide_worth_it | lora |
| cluster_8 | phi-2 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q,dream_generate_last_utterance,web_questions_question_answer,quoref_What_Is_The_Answer,stream_qed_ii,cos_e_v1_11_question_description_option_id,adversarial_qa_droberta_based_on,para_crawl_enes,glue_qqp_2_0_0,cos_e_v1_11_generate_explanation_given_text,glue_mrpc_2_0_0,duorc_ParaphraseRC_answer_question | lora |
| cluster_2 | phi-2 | sordonia/flan-10k-flat/ropes_read_background_situation,yelp_polarity_reviews_0_2_0,kilt_tasks_hotpotqa_straighforward_qa,qasc_qa_with_combined_facts_1,snli_1_1_0,wiki_hop_original_choose_best_object_affirmative_2,cot_strategyqa_ii,gem_common_gen_1_1_0,race_middle_Select_the_best_answer_no_instructions_,quoref_Find_Answer,trec_1_0_0,duorc_SelfRC_question_answering,race_middle_Taking_a_test,ropes_plain_background_situation,super_glue_record_1_0_2,ropes_background_new_situation_answer,cos_e_v1_11_rationale,web_questions_get_the_answer,quail_no_prompt_id,quoref_Answer_Question_Given_Context,duorc_SelfRC_movie_director,app_reviews_convert_to_star_rating,duorc_SelfRC_decide_worth_it,stream_qed | lora |
| cluster_1 | phi-2 | sordonia/flan-10k-flat/anli_r1_0_1_0,dream_answer_to_dialogue,wiki_bio_guess_person,web_questions_potential_correct_answer,ropes_new_situation_background_answer,duorc_SelfRC_title_generation,quartz_use_info_from_paragraph_question,quartz_having_read_above_passage,super_glue_wic_1_0_2,huggingface_xsum,cot_ecqa_ii,cos_e_v1_11_question_option_description_id,race_middle_Select_the_best_answer,kilt_tasks_hotpotqa_combining_facts,cot_creak_ii,race_high_Select_the_best_answer_no_instructions_,sciq_Multiple_Choice | lora |
| cluster_7 | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer,adversarial_qa_dbidaf_generate_question,math_dataset_algebra__linear_1d_1_0_0,race_high_Is_this_the_right_answer,adversarial_qa_droberta_generate_question,wiqa_which_of_the_following_is_the_supposed_perturbation,adversarial_qa_droberta_question_context_answer,ag_news_subset_1_0_0,adversarial_qa_droberta_tell_what_it_is,wiki_qa_automatic_system,quail_description_context_question_answer_text,web_questions_whats_the_answer,gem_web_nlg_en_1_1_0,ropes_given_background_situation,ropes_background_situation_middle,quartz_answer_question_below,duorc_ParaphraseRC_extract_answer,social_i_qa_I_was_wondering,wiki_hop_original_choose_best_object_affirmative_1,cos_e_v1_11_question_option_description_text,coqa_1_0_0,qasc_qa_with_separated_facts_4,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,app_reviews_generate_review,wiki_qa_Direct_Answer_to_Question,race_middle_Select_the_best_answer_generate_span_,race_high_Taking_a_test,glue_stsb_2_0_0,wiki_qa_Decide_good_answer,super_glue_wsc_fixed_1_0_2,social_i_qa_Show_choices_and_generate_answer,adversarial_qa_dbidaf_question_context_answer,cot_esnli,cot_esnli_ii,wiki_qa_Jeopardy_style,quoref_Answer_Friend_Question | lora |
| cluster_9 | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint,qasc_qa_with_separated_facts_5,quail_context_description_question_answer_id,wmt16_translate_fi_en_1_0_0,wiki_hop_original_generate_object,quoref_Guess_Title_For_Context,qasc_qa_with_separated_facts_3,wiki_qa_Generate_Question_from_Topic,duorc_ParaphraseRC_question_answering,social_i_qa_Show_choices_and_generate_index,quac_1_0_0,duorc_SelfRC_generate_question,kilt_tasks_hotpotqa_formulate,definite_pronoun_resolution_1_1_0,adversarial_qa_dbidaf_answer_the_following_q,dbpedia_14_given_a_choice_of_categories_,gigaword_1_2_0,race_high_Write_a_multi_choice_question_options_given_,quoref_Found_Context_Online,quail_context_question_description_answer_text,wiki_bio_key_content,quail_context_description_question_text,quail_context_question_answer_description_id | lora |
| cluster_10 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object,aeslc_1_0_0,sciq_Multiple_Choice_Closed_Book_,wiki_qa_exercise,duorc_ParaphraseRC_build_story_around_qa,glue_wnli_2_0_0,wiki_hop_original_choose_best_object_interrogative_2,trivia_qa_rc_1_1_0,duorc_SelfRC_extract_answer,cot_sensemaking_ii,quail_description_context_question_text,quail_no_prompt_text,duorc_ParaphraseRC_generate_question,unified_qa_science_inst,app_reviews_convert_to_rating,glue_qnli_2_0_0,adversarial_qa_dbert_generate_question,wiki_bio_who,quail_description_context_question_answer_id,wiqa_what_might_be_the_first_step_of_the_process,dream_baseline,web_questions_short_general_knowledge_q,quarel_do_not_use,glue_cola_2_0_0,word_segment,race_high_Select_the_best_answer_generate_span_,duorc_SelfRC_generate_question_by_answer,cot_sensemaking,wiki_hop_original_choose_best_object_affirmative_3,super_glue_multirc_1_0_2,adversarial_qa_dbidaf_tell_what_it_is,paws_wiki_1_1_0,wiqa_what_is_the_missing_first_step,super_glue_rte_1_0_2,kilt_tasks_hotpotqa_final_exam,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,cosmos_qa_1_0_0,quail_context_question_answer_description_text | lora |
Last updated on: 2024-02-01 08:00:01+00:00
| [
"SCIQ"
] |
ostapeno/phi2_10clusters_kmeans_balanced_1epoch | ostapeno | null | [
"region:us"
] | "2024-01-31T14:36:49Z" | 2024-01-31T14:38:16+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_10c9o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,super_glue_cb_1_0_2,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,unified_qa_science_inst,super_glue_multirc_1_0_2,cot_strategyqa,cot_ecqa_ii,quarel_do_not_use,wiki_qa_exercise,stream_qed_ii,super_glue_copa_1_0_2,social_i_qa_Generate_the_question_from_the_answer,quoref_Answer_Friend_Question,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,stream_aqua_ii | lora |
| phi2_joint_10c6o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,quartz_answer_question_based_on,ropes_plain_background_situation,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,wiqa_does_the_supposed_perturbation_have_an_effect,duorc_ParaphraseRC_title_generation,quartz_use_info_from_question_paragraph,wiqa_which_of_the_following_is_the_supposed_perturbation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,wiqa_effect_with_string_answer,ropes_prompt_beginning,quartz_having_read_above_passage,ropes_read_background_situation,ropes_plain_bottom_hint,quarel_heres_a_story,ropes_plain_no_background,quartz_use_info_from_paragraph_question,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_10c5o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,app_reviews_categorize_rating_using_review,cot_sensemaking,super_glue_wic_1_0_2,anli_r3_0_1_0,paws_wiki_1_1_0,cot_creak,wiki_qa_Is_This_True_,snli_1_1_0,glue_qqp_2_0_0,wiki_qa_automatic_system,cot_creak_ii,anli_r2_0_1_0,qasc_is_correct_1,anli_r1_0_1_0,glue_stsb_2_0_0,glue_qnli_2_0_0,cot_sensemaking_ii,glue_mnli_2_0_0,social_i_qa_Check_if_a_random_answer_is_valid_or_not,social_i_qa_I_was_wondering,qasc_is_correct_2,cosmos_qa_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_10c7o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,app_reviews_convert_to_star_rating,true_case,natural_questions_open_1_0_0,web_questions_whats_the_answer,wiki_qa_Topic_Prediction_Answer_Only,kilt_tasks_hotpotqa_final_exam,web_questions_question_answer,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,app_reviews_convert_to_rating,adversarial_qa_droberta_based_on,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,squad_v2_0_3_0_0,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,wiki_qa_Topic_Prediction_Question_Only,kilt_tasks_hotpotqa_formulate,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,drop_2_0_0,squad_v1_1_3_0_0 | lora |
| phi2_joint_10c1o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_middle_Is_this_the_right_answer,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,race_high_Is_this_the_right_answer,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| phi2_joint_10c8o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,stream_qed,super_glue_record_1_0_2,cos_e_v1_11_rationale,cos_e_v1_11_generate_explanation_given_text,wiki_hop_original_generate_object,adversarial_qa_droberta_tell_what_it_is,dbpedia_14_given_a_choice_of_categories_,wiki_hop_original_choose_best_object_affirmative_3,cos_e_v1_11_i_think,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_explain_relation,wiki_hop_original_choose_best_object_affirmative_1,adversarial_qa_dbert_answer_the_following_q,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,wiki_hop_original_generate_subject,cos_e_v1_11_aligned_with_common_sense,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora |
| phi2_joint_10c0o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,huggingface_xsum,trec_1_0_0,yelp_polarity_reviews_0_2_0,lambada_1_0_0,dream_generate_last_utterance,ag_news_subset_1_0_0,wiki_bio_key_content,math_dataset_algebra__linear_1d_1_0_0,stream_aqua,dbpedia_14_pick_one_category_for_the_following_text,cnn_dailymail_3_4_0,duorc_SelfRC_build_story_around_qa,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,duorc_SelfRC_title_generation,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,wiki_bio_what_content,race_high_Write_a_multi_choice_question_for_the_following_article,wiki_bio_guess_person,cot_ecqa,dream_generate_first_utterance,dream_answer_to_dialogue,race_high_Write_a_multi_choice_question_options_given_,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| phi2_joint_10c2o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quac_1_0_0,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
| phi2_joint_10c3o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,sciq_Multiple_Choice,cos_e_v1_11_question_description_option_text,sciq_Direct_Question,qasc_qa_with_separated_facts_2,cos_e_v1_11_question_option_description_id,qasc_qa_with_separated_facts_5,dream_baseline,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_explain_why_human,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,cos_e_v1_11_description_question_option_id,quarel_choose_between,social_i_qa_Show_choices_and_generate_index,sciq_Multiple_Choice_Closed_Book_,qasc_qa_with_separated_facts_4,wiqa_effect_with_label_answer,quarel_logic_test,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_10c4o10_2e_1epoch_balanced_kmeans_on_sim_matrix | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,cot_esnli,cot_gsm8k,wmt16_translate_ro_en_1_0_0,glue_cola_2_0_0,gem_dart_1_1_0,wiqa_what_might_be_the_last_step_of_the_process,gem_common_gen_1_1_0,gem_wiki_lingua_english_en_1_1_0,fix_punct,gigaword_1_2_0,gem_web_nlg_en_1_1_0,word_segment,app_reviews_generate_review,wmt16_translate_de_en_1_0_0,wiki_bio_who,aeslc_1_0_0,wmt16_translate_fi_en_1_0_0,gem_e2e_nlg_1_1_0,para_crawl_enes,wmt14_translate_fr_en_1_0_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step | lora |
Last updated on: 2024-01-31 14:36:51+00:00
Balanced k-means clusters created by applying k-means to similarity matrix (so task similarities are the features here) | [
"SCIQ"
] |
OctaSpace/Mistral7B-fintuned-multi_x_science | OctaSpace | null | [
"safetensors",
"arxiv:2010.14235",
"license:apache-2.0",
"region:us"
] | "2024-02-01T15:07:22Z" | 2024-02-13T09:27:33+00:00 | 0 | 0 | ---
license: apache-2.0
---
# Fine-tuned Mistral Model for Multi-Document Summarization
This model a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on
[multi_x_science_sum](https://huggingface.co/datasets/multi_x_science_sum) dataset.
## Model description
Mistral-7B-multixscience-finetuned is finetuned on multi_x_science_sum
dataset in order to extend the capabilities of the original
Mistral model in multi-document summarization tasks.
The fine-tuned model leverages the power of Mistral model fundation,
adapting it to synthesize and summarize information from
multiple documents efficiently.
## Training and evaluation dataset
Multi_x_science_sum is a large-scale multi-document
summarization dataset created from scientific articles.
Multi-XScience introduces a challenging multi-document
summarization task: writing the related-work section of a
paper based on its abstract and the articles it references.
* [paper](https://arxiv.org/pdf/2010.14235.pdf)
* [Source](https://huggingface.co/datasets/multi_x_science_sum)
The training and evaluation datasets were uniquely generated
to facilitate the fine-tuning of the model for
multi-document summarization, particularly focusing on
generating related work sections for scientific papers.
Using a custom-designed prompt-generation process, the dataset
is created to simulate the task of synthesizing related work
sections based on a given paper's abstract and the abstracts
of its referenced papers.
### Dataset Generation process
The process involves generating prompts that instruct the
model to use the abstract of the current paper along with
the abstracts of cited papers to generate a new related work
section. This approach aims to mimic the real-world scenario
where a researcher synthesizes information from multiple
sources to draft the related work section of a paper.
* **Prompt Structure:** Each data point consists of an instructional prompt that includes:
* The abstract of the current paper.
* Abstracts from cited papers, labeled with unique identifiers.
* An expected model response in the form of a generated related work section.
### Prompt generation Code
```
def generate_related_work_prompt(data):
prompt = "[INST] <<SYS>>\n"
prompt += "Use the abstract of the current paper and the abstracts of the cited papers to generate new related work.\n"
prompt += "<</SYS>>\n\n"
prompt += "Input:\nCurrent Paper's Abstract:\n- {}\n\n".format(data['abstract'])
prompt += "Cited Papers' Abstracts:\n"
for cite_id, cite_abstract in zip(data['ref_abstract']['cite_N'], data['ref_abstract']['abstract']):
prompt += "- {}: {}\n".format(cite_id, cite_abstract)
prompt += "\n[/INST]\n\nGenerated Related Work:\n{}\n".format(data['related_work'])
return {"text": prompt}
```
The dataset generated through this process was used to train
and evaluate the finetuned model, ensuring that it learns to
accurately synthesize information from multiple sources into
cohesive summaries.
## Training hyperparameters
The following hyperparameters were used during training:
```
learning_rate: 2e-5
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: adamw_8bit
num_epochs: 5
```
## Usage
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
base_model = "mistralai/Mistral-7B-v0.1"
adapter = "OctaSpace/Mistral7B-fintuned"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
base_model,
add_bos_token=True,
trust_remote_code=True,
padding_side='left'
)
# Create peft model using base_model and finetuned adapter
config = PeftConfig.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
load_in_4bit=True,
device_map='auto',
torch_dtype='auto')
model = PeftModel.from_pretrained(model, adapter)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
# Prompt content:
messages = [] # Put here your related work generation instruction
input_ids = tokenizer.apply_chat_template(conversation=messages,
tokenize=True,
add_generation_prompt=True,
return_tensors='pt').to(device)
summary_ids = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, pad_token_id=2)
summaries = tokenizer.batch_decode(summary_ids.detach().cpu().numpy(), skip_special_tokens = True)
# Model response:
print(summaries[0])
``` | [
"MULTI-XSCIENCE"
] |
croissantllm/CroissantLLM_training_logs | croissantllm | null | [
"tensorboard",
"legal",
"code",
"text-generation-inference",
"art",
"fr",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:uonlp/CulturaX",
"dataset:pg19",
"dataset:bigcode/starcoderdata",
"dataset:manu/croissant_dataset",
"arxiv:2402.00786",
"license:mit",
"region:us"
] | "2024-02-03T16:21:57Z" | 2024-02-03T16:26:32+00:00 | 0 | 0 | ---
datasets:
- cerebras/SlimPajama-627B
- uonlp/CulturaX
- pg19
- bigcode/starcoderdata
- manu/croissant_dataset
language:
- fr
- en
license: mit
tags:
- legal
- code
- text-generation-inference
- art
---
# CroissantLLM - Base Training Logs
These logs are the training tensorboard logs for the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens.
To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1.
https://arxiv.org/abs/2402.00786
## Abstract
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.
To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.
This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.
## Citation
Our work can be cited as:
```bash
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno Guerreiro and António Loison and Duarte Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro Martins and Antoni Bigata Casademunt and François Yvon and André Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"CRAFT"
] |
biswa921/bge-m3 | biswa921 | null | [
"mteb",
"model-index",
"region:us"
] | "2024-02-06T05:39:28Z" | 2024-03-22T12:51:52+00:00 | 0 | 0 | ---
tags:
- mteb
model-index:
- name: bge-m3
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.6268656716418
- type: ap
value: 39.50276109614102
- type: f1
value: 70.00224623431103
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.013675
- type: ap
value: 87.30227544778319
- type: f1
value: 91.00157923673694
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986000000000004
- type: f1
value: 44.93316837240337
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.521
- type: map_at_10
value: 45.062999999999995
- type: map_at_100
value: 45.965
- type: map_at_1000
value: 45.972
- type: map_at_3
value: 40.078
- type: map_at_5
value: 43.158
- type: mrr_at_1
value: 29.232000000000003
- type: mrr_at_10
value: 45.305
- type: mrr_at_100
value: 46.213
- type: mrr_at_1000
value: 46.22
- type: mrr_at_3
value: 40.339000000000006
- type: mrr_at_5
value: 43.394
- type: ndcg_at_1
value: 28.521
- type: ndcg_at_10
value: 53.959999999999994
- type: ndcg_at_100
value: 57.691
- type: ndcg_at_1000
value: 57.858
- type: ndcg_at_3
value: 43.867
- type: ndcg_at_5
value: 49.38
- type: precision_at_1
value: 28.521
- type: precision_at_10
value: 8.222
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.279
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 28.521
- type: recall_at_10
value: 82.219
- type: recall_at_100
value: 98.222
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 54.836
- type: recall_at_5
value: 68.137
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.409674498704625
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.52757354203137
- type: mrr
value: 74.28241656773513
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.39442490594014
- type: cos_sim_spearman
value: 83.37599616417513
- type: euclidean_pearson
value: 83.23317790460271
- type: euclidean_spearman
value: 83.37599616417513
- type: manhattan_pearson
value: 83.23182214744224
- type: manhattan_spearman
value: 83.5428674363298
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.93181818181819
- type: f1
value: 81.0852312152688
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.784
- type: map_at_10
value: 38.879000000000005
- type: map_at_100
value: 40.161
- type: map_at_1000
value: 40.291
- type: map_at_3
value: 36.104
- type: map_at_5
value: 37.671
- type: mrr_at_1
value: 35.924
- type: mrr_at_10
value: 44.471
- type: mrr_at_100
value: 45.251000000000005
- type: mrr_at_1000
value: 45.296
- type: mrr_at_3
value: 42.367
- type: mrr_at_5
value: 43.635000000000005
- type: ndcg_at_1
value: 35.924
- type: ndcg_at_10
value: 44.369
- type: ndcg_at_100
value: 48.925999999999995
- type: ndcg_at_1000
value: 50.964
- type: ndcg_at_3
value: 40.416999999999994
- type: ndcg_at_5
value: 42.309999999999995
- type: precision_at_1
value: 35.924
- type: precision_at_10
value: 8.344
- type: precision_at_100
value: 1.367
- type: precision_at_1000
value: 0.181
- type: precision_at_3
value: 19.469
- type: precision_at_5
value: 13.771
- type: recall_at_1
value: 28.784
- type: recall_at_10
value: 53.92400000000001
- type: recall_at_100
value: 72.962
- type: recall_at_1000
value: 85.90100000000001
- type: recall_at_3
value: 42.574
- type: recall_at_5
value: 47.798
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 50.16499999999999
- type: f1
value: 43.57906972116264
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.737
- type: map_at_10
value: 33.566
- type: map_at_100
value: 35.367
- type: map_at_1000
value: 35.546
- type: map_at_3
value: 29.881999999999998
- type: map_at_5
value: 31.818
- type: mrr_at_1
value: 41.975
- type: mrr_at_10
value: 50.410999999999994
- type: mrr_at_100
value: 51.172
- type: mrr_at_1000
value: 51.214999999999996
- type: mrr_at_3
value: 48.611
- type: mrr_at_5
value: 49.522
- type: ndcg_at_1
value: 41.975
- type: ndcg_at_10
value: 41.299
- type: ndcg_at_100
value: 47.768
- type: ndcg_at_1000
value: 50.882000000000005
- type: ndcg_at_3
value: 38.769
- type: ndcg_at_5
value: 39.106
- type: precision_at_1
value: 41.975
- type: precision_at_10
value: 11.296000000000001
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 26.029000000000003
- type: precision_at_5
value: 18.457
- type: recall_at_1
value: 20.737
- type: recall_at_10
value: 47.284
- type: recall_at_100
value: 71.286
- type: recall_at_1000
value: 89.897
- type: recall_at_3
value: 35.411
- type: recall_at_5
value: 39.987
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 87.84
- type: ap
value: 82.68294664793142
- type: f1
value: 87.8226441992267
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.35841313269493
- type: f1
value: 93.060022693275
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.58002735978113
- type: f1
value: 46.995919480823055
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.07935440484196
- type: f1
value: 69.13197875645403
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.63752521856087
- type: f1
value: 75.61348469613843
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.234
- type: map_at_10
value: 11.718
- type: map_at_100
value: 14.396
- type: map_at_1000
value: 15.661
- type: map_at_3
value: 8.951
- type: map_at_5
value: 10.233
- type: mrr_at_1
value: 43.034
- type: mrr_at_10
value: 52.161
- type: mrr_at_100
value: 52.729000000000006
- type: mrr_at_1000
value: 52.776
- type: mrr_at_3
value: 50.671
- type: mrr_at_5
value: 51.476
- type: ndcg_at_1
value: 41.331
- type: ndcg_at_10
value: 31.411
- type: ndcg_at_100
value: 28.459
- type: ndcg_at_1000
value: 37.114000000000004
- type: ndcg_at_3
value: 37.761
- type: ndcg_at_5
value: 35.118
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 22.878999999999998
- type: precision_at_100
value: 7.093000000000001
- type: precision_at_1000
value: 1.9560000000000002
- type: precision_at_3
value: 35.707
- type: precision_at_5
value: 30.279
- type: recall_at_1
value: 5.234
- type: recall_at_10
value: 14.745
- type: recall_at_100
value: 28.259
- type: recall_at_1000
value: 59.16400000000001
- type: recall_at_3
value: 10.08
- type: recall_at_5
value: 11.985
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.33269306539026
- type: cos_sim_spearman
value: 79.71441518631086
- type: euclidean_pearson
value: 80.98109404189279
- type: euclidean_spearman
value: 79.71444969096095
- type: manhattan_pearson
value: 80.97223989357175
- type: manhattan_spearman
value: 79.64929261210406
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.7127498314437
- type: cos_sim_spearman
value: 78.73426610516154
- type: euclidean_pearson
value: 79.72827173736742
- type: euclidean_spearman
value: 78.731973450314
- type: manhattan_pearson
value: 79.71391822179304
- type: manhattan_spearman
value: 78.69626503719782
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 78.33449726355023
- type: cos_sim_spearman
value: 79.59703323420547
- type: euclidean_pearson
value: 79.87238808505464
- type: euclidean_spearman
value: 79.59703323420547
- type: manhattan_pearson
value: 79.5006260085966
- type: manhattan_spearman
value: 79.21864659717262
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.00088445445654
- type: cos_sim_spearman
value: 78.99977508575147
- type: euclidean_pearson
value: 78.63222924140206
- type: euclidean_spearman
value: 78.99976994069327
- type: manhattan_pearson
value: 78.35504771673297
- type: manhattan_spearman
value: 78.76306077740067
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.13160613452308
- type: cos_sim_spearman
value: 87.81435104273643
- type: euclidean_pearson
value: 87.22395745487297
- type: euclidean_spearman
value: 87.81435041827874
- type: manhattan_pearson
value: 87.17630476262896
- type: manhattan_spearman
value: 87.76535338976686
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.76424652225954
- type: cos_sim_spearman
value: 85.39745570134193
- type: euclidean_pearson
value: 84.6971466556576
- type: euclidean_spearman
value: 85.39745570134193
- type: manhattan_pearson
value: 84.61210275324463
- type: manhattan_spearman
value: 85.30727114432379
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.87956530541486
- type: cos_sim_spearman
value: 87.13412608536781
- type: euclidean_pearson
value: 87.80084186244981
- type: euclidean_spearman
value: 87.13412608536781
- type: manhattan_pearson
value: 87.73101535306475
- type: manhattan_spearman
value: 87.05897655963285
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.70737517925419
- type: cos_sim_spearman
value: 84.84687698325351
- type: euclidean_pearson
value: 84.36525309890885
- type: euclidean_spearman
value: 84.84688249844098
- type: manhattan_pearson
value: 84.31171573973266
- type: manhattan_spearman
value: 84.79550448196474
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 48.178
- type: map_at_10
value: 59.24
- type: map_at_100
value: 59.902
- type: map_at_1000
value: 59.941
- type: map_at_3
value: 56.999
- type: map_at_5
value: 58.167
- type: mrr_at_1
value: 51.0
- type: mrr_at_10
value: 60.827
- type: mrr_at_100
value: 61.307
- type: mrr_at_1000
value: 61.341
- type: mrr_at_3
value: 59.0
- type: mrr_at_5
value: 60.033
- type: ndcg_at_1
value: 51.0
- type: ndcg_at_10
value: 64.366
- type: ndcg_at_100
value: 67.098
- type: ndcg_at_1000
value: 68.08
- type: ndcg_at_3
value: 60.409
- type: ndcg_at_5
value: 62.150000000000006
- type: precision_at_1
value: 51.0
- type: precision_at_10
value: 8.799999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 15.8
- type: recall_at_1
value: 48.178
- type: recall_at_10
value: 78.34400000000001
- type: recall_at_100
value: 90.36699999999999
- type: recall_at_1000
value: 98.0
- type: recall_at_3
value: 67.35
- type: recall_at_5
value: 71.989
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87722772277228
- type: cos_sim_ap
value: 97.32479581402639
- type: cos_sim_f1
value: 93.74369323915236
- type: cos_sim_precision
value: 94.60285132382892
- type: cos_sim_recall
value: 92.9
- type: dot_accuracy
value: 99.87722772277228
- type: dot_ap
value: 97.32479581402637
- type: dot_f1
value: 93.74369323915236
- type: dot_precision
value: 94.60285132382892
- type: dot_recall
value: 92.9
- type: euclidean_accuracy
value: 99.87722772277228
- type: euclidean_ap
value: 97.32479581402639
- type: euclidean_f1
value: 93.74369323915236
- type: euclidean_precision
value: 94.60285132382892
- type: euclidean_recall
value: 92.9
- type: manhattan_accuracy
value: 99.87524752475248
- type: manhattan_ap
value: 97.29133330261223
- type: manhattan_f1
value: 93.59359359359361
- type: manhattan_precision
value: 93.687374749499
- type: manhattan_recall
value: 93.5
- type: max_accuracy
value: 99.87722772277228
- type: max_ap
value: 97.32479581402639
- type: max_f1
value: 93.74369323915236
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.60060000000001
- type: ap
value: 15.719924742317021
- type: f1
value: 56.30561683159878
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.71250707413696
- type: f1
value: 63.54808116265952
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.110568039578
- type: cos_sim_ap
value: 70.28927714315245
- type: cos_sim_f1
value: 65.03893361488716
- type: cos_sim_precision
value: 65.06469500924214
- type: cos_sim_recall
value: 65.0131926121372
- type: dot_accuracy
value: 85.110568039578
- type: dot_ap
value: 70.28928082939848
- type: dot_f1
value: 65.03893361488716
- type: dot_precision
value: 65.06469500924214
- type: dot_recall
value: 65.0131926121372
- type: euclidean_accuracy
value: 85.110568039578
- type: euclidean_ap
value: 70.28928621260852
- type: euclidean_f1
value: 65.03893361488716
- type: euclidean_precision
value: 65.06469500924214
- type: euclidean_recall
value: 65.0131926121372
- type: manhattan_accuracy
value: 85.02115992132086
- type: manhattan_ap
value: 70.05813255171925
- type: manhattan_f1
value: 64.59658311510164
- type: manhattan_precision
value: 61.24379285883188
- type: manhattan_recall
value: 68.33773087071239
- type: max_accuracy
value: 85.110568039578
- type: max_ap
value: 70.28928621260852
- type: max_f1
value: 65.03893361488716
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.99949547871309
- type: cos_sim_ap
value: 85.82819569154559
- type: cos_sim_f1
value: 78.37315338318439
- type: cos_sim_precision
value: 74.46454564358494
- type: cos_sim_recall
value: 82.71481367416075
- type: dot_accuracy
value: 88.99949547871309
- type: dot_ap
value: 85.82820043407936
- type: dot_f1
value: 78.37315338318439
- type: dot_precision
value: 74.46454564358494
- type: dot_recall
value: 82.71481367416075
- type: euclidean_accuracy
value: 88.99949547871309
- type: euclidean_ap
value: 85.82819622588083
- type: euclidean_f1
value: 78.37315338318439
- type: euclidean_precision
value: 74.46454564358494
- type: euclidean_recall
value: 82.71481367416075
- type: manhattan_accuracy
value: 88.98009081383165
- type: manhattan_ap
value: 85.77393389750326
- type: manhattan_f1
value: 78.38852097130243
- type: manhattan_precision
value: 75.06341600901916
- type: manhattan_recall
value: 82.0218663381583
- type: max_accuracy
value: 88.99949547871309
- type: max_ap
value: 85.82820043407936
- type: max_f1
value: 78.38852097130243
---
| [
"BIOSSES",
"SCIFACT"
] |
chenhaodev/mistral-7b-medwiki-v1 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:other",
"region:us"
] | "2024-02-06T09:26:37Z" | 2024-02-07T04:05:06+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: mistral-7b-medwiki-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-medwiki-v1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_wikidoc dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Perfromance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.99|± |0.0100|
|professional_medicine| 0|none | 0|acc | 0.57|± |0.0498|
|college_medicine | 0|none | 0|acc | 0.59|± |0.0494|
|clinical_knowledge | 0|none | 0|acc | 0.58|± |0.0496|
|medmcqa |Yaml |none | 0|acc | 0.40|± |0.0492|
|ocn |Yaml |none | 0|acc | 0.61|± |0.0490|
|aocnp |Yaml |none | 0|acc | 0.52|± |0.0502|
### Original Performance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|professional_medicine| 0|none | 0|acc | 0.64|± |0.0482|
|college_medicine | 0|none | 0|acc | 0.65|± |0.0479|
|clinical_knowledge | 0|none | 0|acc | 0.68|± |0.0469|
|medmcqa |Yaml |none | 0|acc | 0.45|± |0.0500|
|ocn |Yaml |none | 0|acc | 0.62|± |0.0488|
|aocnp |Yaml |none | 0|acc | 0.47|± |0.0502|
| [
"PUBMEDQA"
] |
chenhaodev/mistral-7b-medqa-v1 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:other",
"region:us"
] | "2024-02-07T02:28:34Z" | 2024-02-07T03:05:03+00:00 | 0 | 1 | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: mistral-7b-medqa-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-medqa-v1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_medqa dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Performance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medqa-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|ocn |Yaml |none | 0|acc | 0.71|± |0.0456|
|professional_medicine| 0|none | 0|acc | 0.69|± |0.0465|
|college_medicine | 0|none | 0|acc | 0.61|± |0.0490|
|clinical_knowledge | 0|none | 0|acc | 0.63|± |0.0485|
|medmcqa |Yaml |none | 0|acc | 0.41|± |0.0494|
|aocnp |Yaml |none | 0|acc | 0.61|± |0.0490|
### Appendix (original performance before lora-finetune)
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|ocn |Yaml |none | 0|acc | 0.62|± |0.0488|
|professional_medicine| 0|none | 0|acc | 0.64|± |0.0482|
|college_medicine | 0|none | 0|acc | 0.65|± |0.0479|
|clinical_knowledge | 0|none | 0|acc | 0.68|± |0.0469|
|medmcqa |Yaml |none | 0|acc | 0.45|± |0.0500|
|aocnp |Yaml |none | 0|acc | 0.47|± |0.0502|
| [
"MEDQA",
"PUBMEDQA"
] |
chenhaodev/mistral-7b-mmlu-v1 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:other",
"region:us"
] | "2024-02-07T05:03:57Z" | 2024-02-07T05:17:54+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: mistral-7b-mmlu-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-mmlu-v1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_mmmlu dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Performance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-mmlu-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|medmcqa |Yaml |none | 0|acc | 0.47|± |0.0502|
|professional_medicine| 0|none | 0|acc | 0.79|± |0.0409|
|college_medicine | 0|none | 0|acc | 0.72|± |0.0451|
|clinical_knowledge | 0|none | 0|acc | 0.72|± |0.0451|
|aocnp |Yaml |none | 0|acc | 0.56|± |0.0499|
|ocn |Yaml |none | 0|acc | 0.66|± |0.0476|
| [
"PUBMEDQA"
] |
chenhaodev/mistral-7b-ocn-v1 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:other",
"region:us"
] | "2024-02-07T06:17:58Z" | 2024-02-07T06:49:53+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: mistral-7b-ocn-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-ocn-v1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc_instruct dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Performance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|medmcqa |Yaml |none | 0|acc | 0.36|± |0.0482|
|professional_medicine| 0|none | 0|acc | 0.57|± |0.0498|
|college_medicine | 0|none | 0|acc | 0.54|± |0.0501|
|clinical_knowledge | 0|none | 0|acc | 0.62|± |0.0488|
|aocnp |Yaml |none | 0|acc | 0.44|± |0.0499|
|ocn |Yaml |none | 0|acc | 0.54|± |0.0501|
| [
"PUBMEDQA"
] |
chenhaodev/mistral-7b-ocn-v2 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:other",
"region:us"
] | "2024-02-07T07:07:17Z" | 2024-02-07T07:22:09+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: mistral-7b-ocn-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-ocn-v2
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc_medqa_instruct dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Performance
hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.98|± |0.0141|
|medmcqa |Yaml |none | 0|acc | 0.40|± |0.0492|
|professional_medicine| 0|none | 0|acc | 0.69|± |0.0465|
|college_medicine | 0|none | 0|acc | 0.53|± |0.0502|
|clinical_knowledge | 0|none | 0|acc | 0.59|± |0.0494|
|ocn |Yaml |none | 0|acc | 0.80|± |0.0402|
|aocnp |Yaml |none | 0|acc | 0.63|± |0.0485|
| [
"MEDQA",
"PUBMEDQA"
] |
chenhaodev/solar-10b-ocn-v1 | chenhaodev | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:upstage/SOLAR-10.7B-v1.0",
"base_model:adapter:upstage/SOLAR-10.7B-v1.0",
"license:other",
"region:us"
] | "2024-02-07T09:12:23Z" | 2024-02-07T10:01:49+00:00 | 0 | 1 | ---
base_model: upstage/SOLAR-10.7B-v1.0
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: solar-10b-ocn-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# solar-10b-ocn-v1
This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the oncc_medqa_instruct dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training script
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py --stage sft --do_train True --model_name_or_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning_type lora --quantization_bit 4 --flash_attn True --dataset_dir data --dataset oncc_medqa_instruct --cutoff_len 1024 --learning_rate 0.0005 --num_train_epochs 1.0 --max_samples 5000 --per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 10 --save_steps 100 --warmup_steps 10 --neftune_noise_alpha 0.5 --lora_rank 8 --lora_dropout 0.2 --lora_target wqkv --output_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot_loss True
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
### Performance
Test script:
lm_eval --model hf --model_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu_clinical_knowledge,mmlu_college_medicine,mmlu_professional_medicine --device cuda:0 --limit 100
hf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric |Value| |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa | 1|none | 0|acc | 0.95|± |0.0219|
|medmcqa |Yaml |none | 0|acc | 0.42|± |0.0496|
|professional_medicine| 0|none | 0|acc | 0.72|± |0.0451|
|college_medicine | 0|none | 0|acc | 0.67|± |0.0473|
|clinical_knowledge | 0|none | 0|acc | 0.64|± |0.0482|
|ocn |Yaml |none | 0|acc | 0.83|± |0.0378|
|aocnp |Yaml |none | 0|acc | 0.72|± |0.0451|
| [
"MEDQA",
"PUBMEDQA"
] |
reginaldcoghlan/qa | reginaldcoghlan | null | [
"region:us"
] | "2024-02-07T10:16:17Z" | 2024-02-07T10:19:12+00:00 | 0 | 0 | ---
{}
---
In today's digital landscape, the reliability, functionality, and performance of software are paramount to business success. At https://inoxoft.com/service/qa-consulting/, we specialize in revolutionizing your approach to testing, ensuring your products meet exemplary quality standards every step of the way. Our QA consulting services are designed to enhance efficiency, elevate user experience, and propel your business toward greater heights.
As an ISO 27001 certified company and esteemed Microsoft Gold Partner, Google Cloud Partner, ISTQB Silver Partner, and recognized member of Clutch Firms that Deliver and Pangea, we bring unparalleled expertise to every project. Proud members of the Lviv IT Cluster, we are committed to setting industry standards and exceeding client expectations.
Our comprehensive suite of Quality Assurance consulting services includes:
Test Engineering:
Our seasoned software QA consultants craft and implement robust testing frameworks tailored to your project's unique requirements. From identifying and addressing defects to verifying system performance, we cover all functional and non-functional aspects with precision.
Test Management:
Ensure seamless planning, execution, and delivery of QA activities throughout your project lifecycle. Our specialists align testing processes with your company goals, objectives, and quality standards, monitoring progress, and addressing issues proactively.
Test Governance & Compliance:
Navigating industries with stringent regulations such as healthcare, finance, and government, we define policies, procedures, and guidelines to ensure compliance. Our quality control measures mitigate risks and ensure timely addressing of compliance-related challenges.
QA Audit and Improvement:
We analyze your existing QA processes to identify areas for improvement, streamlining workflows, and enhancing efficiency. Leveraging automation and continuous integration practices, we optimize your testing processes for maximum efficacy.
Pre-certification QA:
Prepare your software products for certification and compliance with industry standards and regulations. Our comprehensive assessments, gap analyses, and mock audits ensure your solution meets the necessary criteria. | [
"CRAFT"
] |
croissantllm/CroissantLLMChat-v0.1-GGML | croissantllm | text-generation | [
"legal",
"code",
"text-generation-inference",
"art",
"text-generation",
"fr",
"en",
"dataset:croissantllm/croissant_dataset",
"dataset:croissantllm/CroissantLLM-2201-sft",
"dataset:cerebras/SlimPajama-627B",
"dataset:uonlp/CulturaX",
"dataset:pg19",
"dataset:bigcode/starcoderdata",
"arxiv:2402.00786",
"license:mit",
"region:us"
] | "2024-02-09T14:49:10Z" | 2024-04-29T12:11:49+00:00 | 0 | 0 | ---
datasets:
- croissantllm/croissant_dataset
- croissantllm/CroissantLLM-2201-sft
- cerebras/SlimPajama-627B
- uonlp/CulturaX
- pg19
- bigcode/starcoderdata
language:
- fr
- en
license: mit
pipeline_tag: text-generation
tags:
- legal
- code
- text-generation-inference
- art
---
# CroissantLLMChat - GGML (190k steps + Chat)
This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase.
https://arxiv.org/abs/2402.00786
For best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below:
```python
chat = [
{"role": "user", "content": "Que puis-je faire à Marseille en hiver?"},
]
chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
corresponding to:
```python
chat_input = """<|im_start|>user
{USER QUERY}<|im_end|>
<|im_start|>assistant\n"""
```
## Abstract
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.
To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.
This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.
## Citation
Our work can be cited as:
```bash
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Usage
This model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template.
#### With generate
This might require a stopping criteria on <|im_end|> token.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "croissantllm/CroissantLLMChat-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
generation_args = {
"max_new_tokens": 256,
"do_sample": True,
"temperature": 0.3,
"top_p": 0.90,
"top_k": 40,
"repetition_penalty": 1.05,
"eos_token_id": [tokenizer.eos_token_id, 32000],
}
chat = [
{"role": "user", "content": "Qui est le président francais actuel ?"},
]
chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat_input, return_tensors="pt").to(model.device)
tokens = model.generate(**inputs, **generation_args)
print(tokenizer.decode(tokens[0]))
# print tokens individually
print([(tokenizer.decode([tok]), tok) for tok in tokens[0].tolist()])
```
## Model limitations
Evaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks.
#### Knowledge Cutoff
The model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning.
#### Multilingual performance.
CroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well.
#### Hallucinations.
CroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments. | [
"CRAFT"
] |
croissantllm/CroissantLLMBase-GGML | croissantllm | text-generation | [
"legal",
"code",
"text-generation-inference",
"art",
"text-generation",
"fr",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:uonlp/CulturaX",
"dataset:pg19",
"dataset:bigcode/starcoderdata",
"dataset:croissantllm/croissant_dataset",
"arxiv:2402.00786",
"license:mit",
"region:us"
] | "2024-02-09T15:08:10Z" | 2024-04-29T12:13:02+00:00 | 0 | 0 | ---
datasets:
- cerebras/SlimPajama-627B
- uonlp/CulturaX
- pg19
- bigcode/starcoderdata
- croissantllm/croissant_dataset
language:
- fr
- en
license: mit
pipeline_tag: text-generation
tags:
- legal
- code
- text-generation-inference
- art
---
# CroissantLLM - Base GGML (190k steps, Final version)
This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens.
To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1.
https://arxiv.org/abs/2402.00786
## Abstract
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.
To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.
This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.
## Citation
Our work can be cited as:
```bash
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Usage
This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "croissantllm/CroissantLLMBase"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant.\nHe is heading to the market. -> Il va au marché.\nWe are running on the beach. ->", return_tensors="pt").to(model.device)
tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.3)
print(tokenizer.decode(tokens[0]))
# remove bos token
inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device)
tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60)
print(tokenizer.decode(tokens[0]))
``` | [
"CRAFT"
] |
buio/Fauno-Italian-LLM-7B | buio | null | [
"large language model",
"italian large language model",
"baize",
"llama ",
"italian",
"it",
"en",
"dataset:andreabac3/MedQuaAD-Italian-Fauno-Baize",
"dataset:andreabac3/StackOverflow-Italian-Fauno-Baize",
"dataset:andreabac3/Quora-Italian-Fauno-Baize",
"dataset:teelinsan/camoscio_cleaned",
"license:gpl-3.0",
"region:us"
] | "2024-02-13T17:18:36Z" | 2024-02-13T17:24:48+00:00 | 0 | 0 | ---
datasets:
- andreabac3/MedQuaAD-Italian-Fauno-Baize
- andreabac3/StackOverflow-Italian-Fauno-Baize
- andreabac3/Quora-Italian-Fauno-Baize
- teelinsan/camoscio_cleaned
language:
- it
- en
license: gpl-3.0
tags:
- large language model
- italian large language model
- baize
- 'llama '
- italian
---
# Fauno - Italian LLM
Get ready to meet Fauno - the Italian language model crafted by the [RSTLess Research Group](https://rstless-lab.netlify.app/) from the Sapienza University of Rome.
The talented research team behind Fauno includes [Andrea Bacciu](https://andreabac3.github.io/), [Dr. Giovanni Trappolini](https://sites.google.com/view/giovannitrappolini), [Andrea Santilli](https://www.santilli.xyz/), and [Professor Fabrizio Silvestri](https://sites.google.com/diag.uniroma1.it/fabriziosilvestri/home).
Fauno represents a cutting-edge development in open-source Italian Large Language Modeling. It's trained on extensive Italian synthetic datasets, encompassing a wide range of fields such as medical data 🩺, technical content from Stack Overflow 💻, Quora discussions 💬, and Alpaca data 🦙 translated into Italian.
Hence, our model is able to answer to your questions in Italian 🙋, fix your buggy code 🐛 and understand a minimum of medical literature 💊.
## The 🇮🇹 open-source version of chatGPT!
Discover the capabilities of Fauno and experience the evolution of Italian language models for yourself.
### Why Fauno?
We started with a model called Baize, named after a legendary creature from Chinese literature. Continuing along this thematic line, we developed our Italian model based on Baize and named it Fauno, inspired by an iconic figure from Roman mythology. This choice underlines the link between the two models, while maintaining a distinctive identity rooted in Italian culture.
# Did you know that you can run Fauno on Colab base?
Follow this link to access a Colab notebook with our 7B version! <a target="_blank" href="https://colab.research.google.com/drive/1AepJVWS-qU910zyq-Zi7wWFQ5tthVzUe">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
## 🔎 Model's details
Fauno is a fine-tuned version of the LoRa weights of [Baize](https://github.com/project-baize/baize-chatbot), that is an improved version of [LLama](https://github.com/facebookresearch/llama).
We translated and cleaned the data of Baize, and then we fine-tuned the 7b model using a single RTX A6000 (48GB of VRAM) with 19 hours for one epoch.
- 13B: https://huggingface.co/andreabac3/Fauno-Italian-LLM-13B
Fauno 30B and 65B are coming soon!
## Model initialization
```python
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
from peft import PeftModel
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map="auto",
)
model = PeftModel.from_pretrained(model, "andreabac3/Fauno-Italian-LLM-7B")
model.eval()
```
## Inference
```python
def evaluate(question: str) -> str:
prompt = f"The conversation between human and AI assistant.\n[|Human|] {question}.\n[|AI|] "
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=256
)
output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1]
return output
your_question: str = "Qual'è il significato della vita?"
print(evaluate(your_question))
```
### Output
```
Il senso della vita è una domanda che molte persone hanno cercato di rispondere per secoli.
Alla fine, il senso della vita è soggettivo e varia da persona a persona.
Alcune persone credono che il senso della vita sia trovare la felicità, mentre altre credono che sia raggiungere i propri obiettivi o aiutare gli altri.
Alla fine, il senso della vita è determinato dall'individuo e dai loro valori e credenze.
In definitiva, il senso della vita è qualcosa che ognuno deve trovare da solo.
Non c'è una risposta giusta o sbagliata, poiché ogni persona ha le proprie convinzioni e credenze.
La ricerca del senso della vita può essere un viaggio lungo e difficile, ma vale la pena perseguire.
```
# 📖 Cite our work
To use our translated dataset and model weights in your research, remember to cite our work.
```bibtex
@misc{fauno,
author = {Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Fabrizio Silvestri},
title = {Fauno: The Italian Large Language Model that will leave you senza parole!},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/andreabac3/Fauno-Italian-LLM}},
}
```
## 🔑 License
This project is a derivative of Baize, and we adhere to the licensing constraints imposed by both Baize's creators and the authors of LLama.
## ⚠️ Hallucinations
It is important to remark that current generation models are prone to the problem of hallucinations. So we advise you not to take their answers seriously.
## 👏 Acknowledgement
- LLama - Meta AI: https://github.com/facebookresearch/llama
- Baize: https://github.com/project-baize/baize-chatbot
- Standford Alpaca: https://github.com/tatsu-lab/stanford_alpaca
- Camoscio: https://github.com/teelinsan/camoscio
#### Image Credits
- llama image: https://next14.com/en/nextnews-7-march-a-new-language-model-for-meta-bing-ai-on-windows-and-the-first-tokenized-real-estate-sales/
- Fauno logo: https://www.flaticon.com/free-icon/faun_7931635?term=faun&page=1&position=1&origin=tag&related_id=7931635 | [
"MEDICAL DATA"
] |
GnanaPrasath/ocr_tamil | GnanaPrasath | image-to-text | [
"ocr",
"optical character recognition",
"text recognition",
"tamil",
"image-to-text",
"ta",
"en",
"license:mit",
"region:us"
] | "2024-02-14T12:38:41Z" | 2024-02-14T12:49:55+00:00 | 0 | 7 | ---
language:
- ta
- en
license: mit
metrics:
- accuracy
pipeline_tag: image-to-text
tags:
- ocr
- optical character recognition
- text recognition
- tamil
---
<h1 align="center"> OCR Tamil - Easy, Accurate and Simple to use Tamil OCR - (ஒளி எழுத்துணரி)</h1>
<p align="center">❤️️❤️️Please star✨ it if you like❤️️❤️️</p>
<p align="center">
<a href="LICENSE">
<img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/MIT.svg" alt="LICENSE">
</a>
<a href="https://huggingface.co/spaces/GnanaPrasath/ocr_tamil">
<img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/huggingface.svg" alt="HuggingSpace">
</a>
<a href="https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing">
<img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/colab.svg" alt="colab">
</a>
</p>
<div align="center">
<p>
<a href="https://github.com/gnana70/tamil_ocr">
<img width="50%" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/logo_1.gif">
</a>
</p>
</div>
OCR Tamil can help you extract text from signboard, nameplates, storefronts etc., from Natural Scenes with high accuracy. This version of OCR is much more robust to tilted text compared to the Tesseract, Paddle OCR and Easy OCR as they are primarily built to work on the documents texts and not on natural scenes. This model is work in progress, feel free to contribute!!!
## Languages Supported 🔛
**➡️ English**
**➡️ Tamil (தமிழ்)**
## Accuracy 🎯
✔️ English > 98%
✔️ Tamil > 95%
## Comparison between Tesseract OCR and OCR Tamil ⚖️
Input Image | OCR TAMIL 🏆 | Tesseract | EasyOCR |
|:--------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------:|
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/4.jpg"> | வாழ்கவளமுடன்✅ | க் க்கஸாரகளள௮ஊகஎளமுடன் ❌ | வாழக வளமுடன்❌|
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/11.jpg"> | தமிழ்வாழ்க✅ | **NO OUTPUT** ❌ | தமிழ்வாழ்க✅ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/2.jpg"> | கோபி ✅ | **NO OUTPUT** ❌ | ப99❌ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/6.jpg"> | தாம்பரம் ✅ | **NO OUTPUT** ❌ | தாம்பரம❌ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1.jpg"> | நெடுஞ்சாலைத் ✅ | **NO OUTPUT** ❌ |நெடுஞ்சாலைத் ✅ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/5.jpg"> | அண்ணாசாலை ✅ | **NO OUTPUT** ❌ | ல@I9❌ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/10.jpg"> | ரெடிமேடஸ் ❌ |**NO OUTPUT** ❌ | ரெடிமேடஸ் ❌ |
**Obtained Tesseract and EasyOCR results using the [Colab notebook](https://colab.research.google.com/drive/1ylZm6afur85Pe6I10N2_tzuBFl2VIxkW?usp=sharing) with Tamil and english as language**
## How to Install and Use OCR Tamil 👨🏼💻
### Quick links🌐
📔 Detailed explanation on [Medium article](https://gnana70.medium.com/ocr-tamil-easy-accurate-and-simple-to-use-tamil-ocr-b03b98697f7b).
✍️ Experiment in [Colab notebook](https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing)
🤗 Test it in [Huggingface spaces](https://huggingface.co/spaces/GnanaPrasath/ocr_tamil)
### Pip install instructions🐍
In your command line, run the following command ```pip install ocr_tamil```
If you are using jupyter notebook , install like ```!pip install ocr_tamil```
### Python Usage - Single image inference
**Text Recognition only**
```python
from ocr_tamil.ocr import OCR
image_path = r"test_images\1.jpg" # insert your own path here
ocr = OCR()
text_list = ocr.predict(image_path)
print(text_list[0])
## OUTPUT : நெடுஞ்சாலைத்
```
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1_180.jpg">
**Text Detect + Recognition**
```python
from ocr_tamil.ocr import OCR
image_path = r"test_images\0.jpg" # insert your own image path here
ocr = OCR(detect=True)
texts = ocr.predict(image_path)
print(text_list[0])
## OUTPUT : கொடைக்கானல் Kodaikanal
```
<img width="400" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/0.jpg">
### Batch inference mode 💻
**Text Recognition only**
```python
from ocr_tamil.ocr import OCR
image_path = [r"test_images\1.jpg",r"test_images\2.jpg"] # insert your own image paths here
ocr = OCR()
text_list = ocr.predict(image_path)
for text in text_list:
print(text)
## OUTPUT : நெடுஞ்சாலைத்
## OUTPUT : கோபி
```
**Text Detect + Recognition**
```python
from ocr_tamil.ocr import OCR
image_path = [r"test_images\0.jpg",r"test_images\tamil_sentence.jpg"] # insert your own image paths here
ocr = OCR(detect=True)
text_list = ocr.predict(image_path)
for text in text_list:
print(text)
## OUTPUT : கொடைக்கானல் Kodaikanal
## OUTPUT : செரியர் யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்க்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு
```
**Tested using Python 3.10 on Windows & Linux (Ubuntu 22.04) Machines**
## Applications⚡
1. ADAS system navigation based on the signboards + maps (hybrid approach) 🚁
2. License plate recognition 🚘
## Limitations⛔
1. Unable to read the text if they are present in rotated forms
<p align="left">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/9.jpg">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/8.jpg">
</p>
2. Currently supports Only English and Tamil Language
3. Document Text reading capability is limited. Auto identification of Paragraph, line are not supported along with Text detection model inability to detect and crop the Tamil text leads to accuracy decrease (**WORKAROUND** Can use your own text detection model along with OCR tamil text recognition model)
<p align="center">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence.jpg">
</p>
<p align="center">
<span>Cropped Text from Text detection Model</span>
</p>
<p align="center">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence_crop.jpg">
</p>
<p align="center">
Character **இ** missing due to text detection model error
</p>
**?**யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்கக்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு
## Acknowledgements 👏
**Text detection** - [CRAFT TEXT DECTECTION](https://github.com/clovaai/CRAFT-pytorch)
**Text recognition** - [PARSEQ](https://github.com/baudm/parseq)
```bibtex
@InProceedings{bautista2022parseq,
title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
author={Bautista, Darwin and Atienza, Rowel},
booktitle={European Conference on Computer Vision},
pages={178--196},
month={10},
year={2022},
publisher={Springer Nature Switzerland},
address={Cham},
doi={10.1007/978-3-031-19815-1_11},
url={https://doi.org/10.1007/978-3-031-19815-1_11}
}
```
```bibtex
@inproceedings{baek2019character,
title={Character Region Awareness for Text Detection},
author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={9365--9374},
year={2019}
}
```
## Citation
```bibtex
@InProceedings{GnanaPrasath,
title={Tamil OCR},
author={Gnana Prasath D},
month={01},
year={2024},
url={https://github.com/gnana70/tamil_ocr}
}
```
 | [
"CRAFT"
] |
ostapeno/library-phi_2-v3-10-flan-clusters-fromlucas | ostapeno | null | [
"region:us"
] | "2024-02-14T15:37:44Z" | 2024-07-06T12:58:50+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| phi2_joint_lora_embed_clustersc8_2e_3epoch | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora |
| phi2_joint_lora_embed_clustersc3_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,wmt16_translate_ro_en_1_0_0,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,gem_common_gen_1_1_0,duorc_SelfRC_build_story_around_qa,app_reviews_generate_review,wiki_bio_what_content,wiki_bio_who,gem_e2e_nlg_1_1_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora |
| phi2_joint_lora_embed_clustersc0_2e_3epoch | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_mix,ropes_background_situation_middle | lora |
| phi2_joint_lora_embed_clustersc2_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,super_glue_record_1_0_2,wiki_hop_original_generate_object,adversarial_qa_droberta_tell_what_it_is,dbpedia_14_given_a_choice_of_categories_,wiki_hop_original_choose_best_object_affirmative_3,quac_1_0_0,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_choose_best_object_affirmative_1,adversarial_qa_dbert_answer_the_following_q,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,squad_v2_0_3_0_0,wiki_hop_original_generate_subject,wiki_bio_guess_person,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,race_high_Write_a_multi_choice_question_options_given_,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora |
| phi2_joint_lora_embed_clustersc6_2e_3epoch | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,cot_sensemaking,super_glue_wic_1_0_2,cos_e_v1_11_rationale,anli_r3_0_1_0,dream_generate_last_utterance,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cot_creak,stream_aqua,snli_1_1_0,cos_e_v1_11_i_think,glue_qqp_2_0_0,cos_e_v1_11_explain_why_human,anli_r2_0_1_0,anli_r1_0_1_0,glue_stsb_2_0_0,cos_e_v1_11_aligned_with_common_sense,glue_mnli_2_0_0,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora |
| phi2_joint_lora_embed_clustersc7_2e_3epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,app_reviews_convert_to_star_rating,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,kilt_tasks_hotpotqa_final_exam,sciq_Multiple_Choice,wiqa_does_the_supposed_perturbation_have_an_effect,cos_e_v1_11_question_description_option_text,wiki_qa_Is_This_True_,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cos_e_v1_11_question_option_description_id,wiqa_effect_with_string_answer,qasc_qa_with_separated_facts_5,dream_baseline,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,qasc_is_correct_1,cos_e_v1_11_description_question_option_id,social_i_qa_Check_if_a_random_answer_is_valid_or_not,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,qasc_is_correct_2,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,sciq_Multiple_Choice_Question_First | lora |
| phi2_joint_lora_embed_clustersc1_2e_3epoch | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_gsm8k,trec_1_0_0,yelp_polarity_reviews_0_2_0,lambada_1_0_0,glue_cola_2_0_0,ag_news_subset_1_0_0,gem_dart_1_1_0,math_dataset_algebra__linear_1d_1_0_0,cnn_dailymail_3_4_0,wiki_hop_original_explain_relation,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,gem_wiki_lingua_english_en_1_1_0,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,gem_web_nlg_en_1_1_0,word_segment,race_high_Write_a_multi_choice_question_for_the_following_article,wmt16_translate_de_en_1_0_0,cot_ecqa,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,para_crawl_enes,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0 | lora |
| phi2_joint_lora_embed_clustersc9_2e_3epoch | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0,web_questions_whats_the_answer,web_questions_question_answer,dbpedia_14_pick_one_category_for_the_following_text,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,adversarial_qa_droberta_based_on,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,kilt_tasks_hotpotqa_formulate,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,squad_v1_1_3_0_0 | lora |
| phi2_joint_lora_embed_clustersc4_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,app_reviews_categorize_rating_using_review,race_middle_Is_this_the_right_answer,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,unified_qa_science_inst,race_high_Is_this_the_right_answer,cot_strategyqa,cot_ecqa_ii,quarel_do_not_use,wiki_qa_exercise,wiki_qa_automatic_system,cot_creak_ii,quarel_heres_a_story,quarel_choose_between,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,super_glue_copa_1_0_2,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,quarel_logic_test,stream_aqua_ii | lora |
| phi2_joint_lora_embed_clustersc5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora |
Last updated on: 2024-02-14 16:05:40+00:00
Cloned from pclucas14/library-phi_2-v3-10-flan-clusters | [
"SCIQ"
] |
ostapeno/library-phi_2-v3_fromsordonia | ostapeno | null | [
"region:us"
] | "2024-02-14T17:40:34Z" | 2024-06-22T15:33:42+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 263
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| glue_sst2_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| adversarial_qa_droberta_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| adversarial_qa_dbidaf_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| app_reviews_convert_to_star_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| race_high_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| super_glue_rte_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| true_case | phi-2 | sordonia/flan-10k-flat/true_case | lora |
| wiqa_what_might_be_the_first_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| quail_description_context_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| quail_context_question_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| stream_qed | phi-2 | sordonia/flan-10k-flat/stream_qed | lora |
| huggingface_xsum | phi-2 | sordonia/flan-10k-flat/huggingface_xsum | lora |
| cos_e_v1_11_question_option_description_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| wiqa_what_is_the_final_step_of_the_following_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_new_situation_answer | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| wiki_qa_found_on_google | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| cot_esnli | phi-2 | sordonia/flan-10k-flat/cot_esnli | lora |
| social_i_qa_Show_choices_and_generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| cot_gsm8k | phi-2 | sordonia/flan-10k-flat/cot_gsm8k | lora |
| app_reviews_categorize_rating_using_review | phi-2 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| cot_sensemaking | phi-2 | sordonia/flan-10k-flat/cot_sensemaking | lora |
| trec_1_0_0 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_wic_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_prompt_bottom_no_hint | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| quartz_answer_question_based_on | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| super_glue_record_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| yelp_polarity_reviews_0_2_0 | phi-2 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| race_middle_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| quoref_Context_Contains_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| cos_e_v1_11_rationale | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| natural_questions_open_1_0_0 | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| ropes_plain_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| web_questions_whats_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| anli_r3_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| duorc_SelfRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| quoref_Find_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| duorc_ParaphraseRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| sciq_Direct_Question_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| qasc_qa_with_separated_facts_3 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| lambada_1_0_0 | phi-2 | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| quartz_given_the_fact_answer_the_q | phi-2 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| super_glue_cb_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| quartz_answer_question_below | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| duorc_ParaphraseRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| wmt16_translate_ro_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| dream_generate_last_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| wiki_qa_Topic_Prediction_Answer_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| kilt_tasks_hotpotqa_final_exam | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| glue_cola_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| race_high_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quail_context_description_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| ag_news_subset_1_0_0 | phi-2 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| paws_wiki_1_1_0 | phi-2 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| sciq_Multiple_Choice | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| wiki_qa_Direct_Answer_to_Question | phi-2 | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| gem_dart_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| cos_e_v1_11_generate_explanation_given_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| wiki_hop_original_generate_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| race_high_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| wiqa_what_might_be_the_last_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| wiki_bio_key_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| quoref_Found_Context_Online | phi-2 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| super_glue_wsc_fixed_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | phi-2 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| adversarial_qa_droberta_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| cos_e_v1_11_question_description_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| gem_common_gen_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| quoref_Read_And_Extract_ | phi-2 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| cot_creak | phi-2 | sordonia/flan-10k-flat/cot_creak | lora |
| cot_gsm8k_ii | phi-2 | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| duorc_ParaphraseRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| hellaswag_1_1_0 | phi-2 | sordonia/flan-10k-flat/hellaswag_1_1_0 | lora |
| wiki_qa_Is_This_True_ | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| math_dataset_algebra__linear_1d_1_0_0 | phi-2 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| unified_qa_science_inst | phi-2 | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quartz_use_info_from_question_paragraph | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| web_questions_question_answer | phi-2 | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| duorc_ParaphraseRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| stream_aqua | phi-2 | sordonia/flan-10k-flat/stream_aqua | lora |
| dbpedia_14_pick_one_category_for_the_following_text | phi-2 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| super_glue_multirc_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| dbpedia_14_given_a_choice_of_categories_ | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| sciq_Direct_Question | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| kilt_tasks_hotpotqa_combining_facts | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quoref_What_Is_The_Answer | phi-2 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| web_questions_short_general_knowledge_q | phi-2 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| qasc_qa_with_separated_facts_2 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | phi-2 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| cnn_dailymail_3_4_0 | phi-2 | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| duorc_ParaphraseRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| race_middle_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| kilt_tasks_hotpotqa_straighforward_qa | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| duorc_SelfRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| adversarial_qa_dbidaf_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| snli_1_1_0 | phi-2 | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| app_reviews_convert_to_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| quail_context_question_description_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| cos_e_v1_11_i_think | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| quoref_Guess_Title_For_Context | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| quac_1_0_0 | phi-2 | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| cos_e_v1_11_question_option_description_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| quoref_Answer_Test | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| duorc_SelfRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| wiki_hop_original_explain_relation | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| ropes_new_situation_background_answer | phi-2 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| race_high_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| quail_description_context_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| cot_strategyqa | phi-2 | sordonia/flan-10k-flat/cot_strategyqa | lora |
| ropes_given_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| quail_context_question_answer_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| cot_ecqa_ii | phi-2 | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| ropes_prompt_bottom_hint_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| gem_wiki_lingua_english_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| glue_qqp_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| ai2_arc_ARC_Challenge_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora |
| fix_punct | phi-2 | sordonia/flan-10k-flat/fix_punct | lora |
| wiqa_effect_with_string_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| adversarial_qa_droberta_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| imdb_reviews_plain_text_1_0_0 | phi-2 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| race_high_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| race_middle_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| quarel_do_not_use | phi-2 | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| duorc_SelfRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| qasc_qa_with_separated_facts_5 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| wiki_qa_exercise | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| duorc_ParaphraseRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| web_questions_get_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| duorc_ParaphraseRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| dream_baseline | phi-2 | sordonia/flan-10k-flat/dream_baseline | lora |
| adversarial_qa_dbert_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| gigaword_1_2_0 | phi-2 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| ropes_prompt_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| quail_context_question_answer_description_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| duorc_SelfRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| kilt_tasks_hotpotqa_complex_question | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| quartz_having_read_above_passage | phi-2 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| quail_context_description_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| cos_e_v1_11_question_description_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| ropes_read_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| gem_web_nlg_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| adversarial_qa_droberta_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| qasc_qa_with_separated_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| wiki_qa_automatic_system | phi-2 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_plain_bottom_hint | phi-2 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| duorc_SelfRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| duorc_ParaphraseRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| cos_e_v1_11_explain_why_human | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| word_segment | phi-2 | sordonia/flan-10k-flat/word_segment | lora |
| cot_creak_ii | phi-2 | sordonia/flan-10k-flat/cot_creak_ii | lora |
| anli_r2_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| cos_e_v1_11_description_question_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| quarel_heres_a_story | phi-2 | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| qasc_qa_with_combined_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| app_reviews_generate_review | phi-2 | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_bio_what_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| qasc_is_correct_1 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| quoref_Answer_Question_Given_Context | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| squad_v2_0_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| web_questions_potential_correct_answer | phi-2 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| trivia_qa_rc_1_1_0 | phi-2 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| wmt16_translate_de_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| cos_e_v1_11_description_question_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_hop_original_generate_subject | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| ropes_plain_no_background | phi-2 | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| quarel_choose_between | phi-2 | sordonia/flan-10k-flat/quarel_choose_between | lora |
| stream_qed_ii | phi-2 | sordonia/flan-10k-flat/stream_qed_ii | lora |
| wiki_bio_guess_person | phi-2 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| anli_r1_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| quail_context_description_question_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| cot_ecqa | phi-2 | sordonia/flan-10k-flat/cot_ecqa | lora |
| quail_context_question_description_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| wiki_bio_who | phi-2 | sordonia/flan-10k-flat/wiki_bio_who | lora |
| wiki_qa_Topic_Prediction_Question_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| glue_stsb_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| cos_e_v1_11_aligned_with_common_sense | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| aeslc_1_0_0 | phi-2 | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| dream_generate_first_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| wmt16_translate_fi_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| adversarial_qa_dbidaf_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| dream_answer_to_dialogue | phi-2 | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| glue_qnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| adversarial_qa_droberta_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| cot_sensemaking_ii | phi-2 | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| adversarial_qa_dbert_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| glue_mnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| quail_description_context_question_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| super_glue_copa_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | phi-2 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| social_i_qa_Generate_the_question_from_the_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| social_i_qa_Show_choices_and_generate_index | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| kilt_tasks_hotpotqa_formulate | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| gem_e2e_nlg_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| para_crawl_enes | phi-2 | sordonia/flan-10k-flat/para_crawl_enes | lora |
| ai2_arc_ARC_Easy_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Easy_1_0_0 | lora |
| duorc_SelfRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| sciq_Multiple_Choice_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| race_high_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| race_middle_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| social_i_qa_I_was_wondering | phi-2 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| adversarial_qa_dbert_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| quoref_Guess_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| quartz_use_info_from_paragraph_question | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| quoref_Answer_Friend_Question | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| qasc_is_correct_2 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wmt14_translate_fr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_testing_students | phi-2 | sordonia/flan-10k-flat/quarel_testing_students | lora |
| piqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/piqa_1_0_0 | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| qasc_qa_with_separated_facts_4 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| duorc_SelfRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| cosmos_qa_1_0_0 | phi-2 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| cot_esnli_ii | phi-2 | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quail_no_prompt_id | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| wmt16_translate_tr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| wiki_qa_Decide_good_answer | phi-2 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| wiki_qa_Jeopardy_style | phi-2 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| adversarial_qa_dbert_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| winogrande_1_1_0 | phi-2 | sordonia/flan-10k-flat/winogrande_1_1_0 | lora |
| bool_q_1_0_0 | phi-2 | sordonia/flan-10k-flat/bool_q_1_0_0 | lora |
| duorc_SelfRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| wiki_qa_Generate_Question_from_Topic | phi-2 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| wiki_hop_original_generate_subject_and_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| adversarial_qa_dbidaf_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiqa_what_is_the_missing_first_step | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| quartz_read_passage_below_choose | phi-2 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| definite_pronoun_resolution_1_1_0 | phi-2 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| quail_no_prompt_text | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| wiqa_effect_with_label_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| drop_2_0_0 | phi-2 | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| race_middle_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| glue_wnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| wiki_bio_comprehension | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| glue_mrpc_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cot_qasc | phi-2 | sordonia/flan-10k-flat/cot_qasc | lora |
| adversarial_qa_dbert_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| openbookqa_0_1_0 | phi-2 | sordonia/flan-10k-flat/openbookqa_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | phi-2 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| coqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| quartz_paragraph_question_plain_concat | phi-2 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| adversarial_qa_dbidaf_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| ropes_prompt_mix | phi-2 | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| social_i_qa_Generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| cot_strategyqa_ii | phi-2 | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| quarel_logic_test | phi-2 | sordonia/flan-10k-flat/quarel_logic_test | lora |
| duorc_ParaphraseRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| stream_aqua_ii | phi-2 | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| multi_news_1_0_0 | phi-2 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| ropes_background_situation_middle | phi-2 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| sciq_Multiple_Choice_Question_First | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| squad_v1_1_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
Last updated on: 2024-02-14 17:41:44+00:00
Clone from sordonia/library-phi_2-v3 | [
"SCIQ"
] |
tmohoric-ewc/safer-skin | tmohoric-ewc | tabular-regression | [
"sklearn",
"skops",
"tabular-regression",
"license:mit",
"region:us"
] | "2024-02-15T23:19:43Z" | 2024-02-15T23:21:29+00:00 | 0 | 0 | ---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- tabular-regression
model_format: pickle
model_file: MLR-model.pkl
widget:
- structuredData:
CAS:
- 696-71-9
- 94-02-0
- 15128-82-2
CID:
- 12766.0
- 7170.0
- 27057.0
CanonicalSMILES:
- canonical: OC1CCCCCCC1
original: C1CCCC(CCC1)O
- canonical: CCOC(=O)CC(=O)c1ccccc1
original: CCOC(=O)CC(=O)C1=CC=CC=C1
- canonical: O=[N+]([O-])c1ncccc1O
original: C1=CC(=C(N=C1)[N+](=O)[O-])O
Cor1-C420 Adduct (M+H):
- no Adduct
- no Adduct
- no Adduct
Cor1-C420 Depletion 24 h (%):
- 1.0
- 1.0
- 1.0
Cor1-C420 Dimer (%):
- 2.0
- 5.0
- 4.0
Cor1-C420 Kmax (1/mM/min):
- 6.979399898264935e-06
- 6.979399898264935e-06
- 6.979399898264935e-06
DPRA Cysteine depletion (%):
- .nan
- 11.2
- .nan
DPRA Lysine depletion (%):
- .nan
- 0.9
- .nan
InChI:
- InChI=1S/C8H16O/c9-8-6-4-2-1-3-5-7-8/h8-9H,1-7H2
- InChI=1S/C11H12O3/c1-2-14-11(13)8-10(12)9-6-4-3-5-7-9/h3-7H,2,8H2,1H3
- InChI=1S/C5H4N2O3/c8-4-2-1-3-6-5(4)7(9)10/h1-3,8H
InChIKey:
- FHADSMKORVFYOS-UHFFFAOYSA-N
- GKKZMYDNDDMXSE-UHFFFAOYSA-N
- QBPDSKPWYWIHGA-UHFFFAOYSA-N
IsomericSMILES:
- canonical: OC1CCCCCCC1
original: C1CCCC(CCC1)O
- canonical: CCOC(=O)CC(=O)c1ccccc1
original: CCOC(=O)CC(=O)C1=CC=CC=C1
- canonical: O=[N+]([O-])c1ncccc1O
original: C1=CC(=C(N=C1)[N+](=O)[O-])O
KeratinoSens EC1.5 (uM):
- 249.6822169
- 62.9764329
- 4000.0
KeratinoSens EC3 (uM):
- 4000.0
- 689.0
- 4000.0
KeratinoSens IC50 (uM):
- 4000.0
- 4000.0
- 4000.0
KeratinoSens Imax:
- 2.830997136
- 3.299878249
- 1.036847118
KeratinoSens Log EC1.5 (uM):
- 2.3973876117256947
- 1.7991780577657597
- 3.6020599913279625
KeratinoSens Log IC50 (uM):
- 3.6020599913279625
- 3.6020599913279625
- 3.6020599913279625
LLNA EC3 (%):
- 100.0
- 100.0
- 100.0
LLNA Log EC3 (%):
- 2.0
- 2.0
- 2.0
MW:
- 128.21
- 192.21
- 140.1
OPERA Boiling point (°C):
- 186.863
- 276.068
- 323.069
OPERA Henry constant (atm/m3):
- 7.84426e-06
- 5.86618e-07
- 9.47507e-08
OPERA Log D at pH 5.5:
- 2.36
- 1.87
- -0.01
OPERA Log D at pH 7.4:
- 2.36
- 1.87
- -1.69
OPERA Melting point (°C):
- 25.1423
- 49.3271
- 128.292
OPERA Octanol-air partition coefficient Log Koa:
- 6.08747
- 6.56126
- 6.36287
OPERA Octanol-water partition coefficient LogP:
- 2.3597
- 1.86704
- 0.398541
OPERA Vapour pressure (mm Hg):
- 0.0839894
- 0.000406705
- 0.00472604
OPERA Water solubility (mol/L):
- 0.0510404
- 0.01476
- 0.0416421
OPERA pKaa:
- 10.68
- .nan
- 5.31
OPERA pKab:
- .nan
- .nan
- .nan
SMILES:
- canonical: OC1CCCCCCC1
original: OC1CCCCCCC1
- canonical: CCOC(=O)CC(=O)c1ccccc1
original: CCOC(=O)CC(=O)c1ccccc1
- canonical: O=[N+]([O-])c1ncccc1O
original: OC1=CC=CN=C1[N+]([O-])=O
TIMES Log Vapour pressure (Pa):
- 0.8564932564458658
- -0.2851674875666674
- -0.9385475209128068
Vapour pressure (Pa):
- 7.1861
- 0.5186
- 0.1152
cLogP:
- 2.285000000003492
- 1.206000000005588
- 0.5590000000020154
hCLAT CV75 (ug/mL):
- .nan
- 571.0951916
- .nan
hCLAT Call:
- .nan
- 0.0
- .nan
hCLAT EC150 (ug/mL):
- .nan
- .nan
- .nan
hCLAT EC200 (ug/mL):
- .nan
- .nan
- .nan
hCLAT MIT (ug/mL):
- .nan
- .nan
- .nan
kDPRA Call: []
kDPRA Log rate (1/s/M):
- .nan
- .nan
- .nan
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
[More Information Needed]
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|------------------|---------|
| copy_X | True |
| fit_intercept | True |
| n_jobs | |
| positive | False |
</details>
### Model Plot
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div>
## Evaluation Results
[More Information Needed]
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# model_card_authors
Tomaz Mohoric
# limitations
This model is intended for educational purposes.
# model_description
This is a multiple linear regression model on a skin sensitisation dataset.
| [
"CAS"
] |
tacotaco122/as | tacotaco122 | null | [
"region:us"
] | "2024-02-19T01:50:57Z" | 2024-02-19T01:51:16+00:00 | 0 | 0 | ---
{}
---
Hello, here to inquire about a position at your business, hope your week has been fantastic. Working with customers is fulfilling aside from the most basic response. I've worked at a lot of warehouses and I enjoy working with people more than anything, tired of being stuck in a warehouse. People are pretty uplifting, even the ones I may not see eye to with. Still, always the few out the day that show a nice gesture and have good intentions for the world, some are hilarious. At least one person out of the week does positivity towards you, or you witness good gestures, humor is subjective. Is a great skill or trait you learn the more people you talk to is an amazing trait to learn and craft.
| [
"CRAFT"
] |
yy0514/llama2-7b-chat-qlora-lek-train-for-medqa-2-epochs | yy0514 | null | [
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | "2024-02-19T13:28:38Z" | 2024-02-19T14:30:51+00:00 | 0 | 0 | ---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: llama2-7b-chat-qlora-lek-train-for-medqa-2-epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2-7b-chat-qlora-lek-train-for-medqa-2-epochs
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
| [
"MEDQA"
] |
BlueSkyThompson/Opi | BlueSkyThompson | null | [
"license:unknown",
"region:us"
] | "2024-02-20T07:16:57Z" | 2024-02-20T07:38:50+00:00 | 0 | 1 | ---
license: unknown
---
hello. Opi is my first projet please bear with me as i am learning to write python as well
as the essentials to LLMs. i Currenttly suffer from a range of mental illness and am a student game designer.
my Goals for Opi and all following are build a model that is to build something i can build and reflect with
my interests are in music and philosophy as well as esports and game theory. | [
"BEAR"
] |
DrishtiSharma/llama-7b-chat-hf-medqa-packing-true-padding-left | DrishtiSharma | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | "2024-02-21T11:07:45Z" | 2024-02-21T11:08:04+00:00 | 0 | 0 | ---
base_model: NousResearch/Llama-2-7b-chat-hf
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-chat-hf-medqa-packing-true-padding-left
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-chat-hf-medqa-packing-true-padding-left
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.2.dev0
- Tokenizers 0.15.2 | [
"MEDQA"
] |
RoversX/GPT_SOVITS_LeiJun_V1 | RoversX | null | [
"region:us"
] | "2024-02-24T02:47:10Z" | 2024-10-07T12:05:09+00:00 | 0 | 0 | ---
{}
---
**Disclaimer**
This speech model and the content it generates are for personal learning and research purposes only. We do not guarantee the copyright of the content generated by this model, and users should bear the relevant risks during use. Please note that it is strictly prohibited to use this model for commercial purposes or any illegal purposes. By using this model, you agree to abide by the above terms and bear all consequences that may arise from the use of this model. We are not responsible for any loss or damage caused by the use of this model. | [
"BEAR"
] |
vyshnavids03/my-pet-dog | vyshnavids03 | text-to-image | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | "2024-02-28T12:05:01Z" | 2024-02-28T12:06:37+00:00 | 0 | 0 | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by vyshnavids03 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4MK21CS059
Sample pictures of this concept:
| [
"BEAR"
] |
vyshnavids03/my-pet-dog-xzg | vyshnavids03 | text-to-image | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | "2024-02-28T12:21:14Z" | 2024-02-28T12:25:21+00:00 | 0 | 0 | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-XZG Dreambooth model trained by vyshnavids03 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4MK21CS059
Sample pictures of this concept:
| [
"BEAR"
] |
frizai/Pulse-v1 | frizai | null | [
"multi-modal",
"all-in-one",
"chatbot",
"gpt-4",
"gpt-3.5 turbo",
"dall-e",
"whisper",
"meta-llama",
"image",
"3d",
"audio",
"en",
"doi:10.57967/hf/2222",
"license:apache-2.0",
"region:us"
] | "2024-03-02T09:03:33Z" | 2024-03-02T09:10:05+00:00 | 0 | 1 | ---
language:
- en
license: apache-2.0
tags:
- multi-modal
- all-in-one
- chatbot
- gpt-4
- gpt-3.5 turbo
- dall-e
- whisper
- meta-llama
- image
- 3d
- audio
---
# Pulse AI
PulseAI, an innovative product by FrizAI, stands at the forefront of auto-generative AI technology. Leveraging the power of quantum computing and advanced machine learning techniques, PulseAI specializes in creating diverse forms of digital content. From generating intricate text compositions to developing sophisticated web applications, PulseAI taps into the immense potential of image-based prompts to revolutionize content creation.
## Overview
Pulse-AI is a dynamic Flask-based web application that integrates OpenAI's cutting-edge models. Aimed at delivering a seamless user experience, Pulse-AI provides an intuitive interface for both novices and experts to explore the capabilities of AI in image and text generation, as well as code development.
## Core Features
- **Image Generation**: Utilize OpenAI's state-of-the-art Image API to transform ideas into vivid visual representations.
- **Text Generation**: Harness the power of advanced language models for creating compelling and contextually relevant textual content.
- **Code Generation**: Leverage AI to generate efficient and effective code snippets, enhancing productivity in software development.
# Installation and Setup
Embark on your journey with Pulse-AI by following these setup instructions:
1. **Clone the Repository**
```bash
git clone https://github.com/Will-Langhart/Pulse-AI.git
cd Pulse-AI
```
2. **Initialize the Virtual Environment**
```bash
python3 -m venv venv
source venv/bin/activate
```
3. **Dependency Installation**
```bash
pip install -r requirements.txt
```
4. **Configure Environment Variables**
Create a `.env` file at the project root with your OpenAI API key:
```makefile
OPENAI_API_KEY=<your_api_key_here>
```
5. **Launch the Application**
```bash
flask run
```
## Virtual Environment
```bash
deactivate # If you're currently in the virtual environment
rm -rf venv
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
# Package Downolads
Dotnev
```bash
pip install python-dotenv
```
OpenAI
```bash
pip install openai
```
Flask-SQLAlchemy
```bash
pip install flask-sqlalchemy
```
Verify Installation
```bash
pip freeze
```
# GitHub Repository Changes and Updates
1. Fetch GitHub Repository Status
```bash
git status
```
2. Fetch the latest changes
```bash
git fetch origin
```
3. Merge changes into local branch
```bash
git merge origin/main
```
4. Merge head force
```bash
git push origin HEAD:main
```
## Usage Instructions
Access the world of AI-powered content creation by navigating to `http://127.0.0.1:5000`. The platform offers:
- **Image Generation**: Input your creative prompts and watch as AI brings them to life.
- **Textual Content Creation**: Explore AI's ability to craft narratives, articles, and more.
- **Code Synthesis**: Generate code snippets to accelerate your development process.
## Contribution Guidelines
Join the Pulse-AI community and contribute to the evolution of AI-driven content creation:
1. Fork the repository and create a new feature branch.
2. Make your contributions and commit them with clear, descriptive messages.
3. Push your changes and initiate a pull request for review.
## License
Pulse-AI is under the MIT License. For detailed information, refer to the `LICENSE` file.
## Contact and Further Information
- **Contact**: [Your Name](mailto:[email protected])
- **Twitter**: [@YourTwitter](https://twitter.com/YourTwitter)
- **Project Link**: [PulseAI on GitHub](https://github.com/Will-Langhart/Pulse-AI)
---
PulseAI is a testament to FrizAI's commitment to advancing AI technology, making it accessible and transformative for various industries and creative endeavors. | [
"CRAFT"
] |
flashingtt/imagenet-r | flashingtt | null | [
"en",
"arxiv:1409.0575",
"arxiv:1912.07726",
"arxiv:1811.12231",
"arxiv:2109.13228",
"license:other",
"region:us"
] | "2024-03-04T13:44:10Z" | 2024-03-05T07:10:31+00:00 | 0 | 0 | ---
language:
- en
license:
- other
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license_details: imagenet-agreement
multilinguality:
- monolingual
paperswithcode_id: imagenet-1k-1
pretty_name: ImageNet
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ImageNet
Terms of Access:
[RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet
database (the "Database") at Princeton University and Stanford University. In exchange
for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational
purposes.
2. Princeton University, Stanford University and Hugging Face make no representations
or warranties regarding the Database, including but not limited to warranties of
non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and
shall defend and indemnify the ImageNet team, Princeton University, Stanford University
and Hugging Face, including their employees, Trustees, officers and agents, against
any and all claims arising from Researcher''s use of the Database, including but
not limited to Researcher''s use of any copies of copyrighted images that he or
she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the
Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University, Stanford University and Hugging Face reserve the right
to terminate Researcher''s access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher''s employer
shall also be bound by these terms and conditions, and Researcher hereby represents
that he or she is fully authorized to enter into this agreement on behalf of such
employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement.'
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: tench, Tinca tinca
1: goldfish, Carassius auratus
2: great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
3: tiger shark, Galeocerdo cuvieri
4: hammerhead, hammerhead shark
5: electric ray, crampfish, numbfish, torpedo
6: stingray
7: cock
8: hen
9: ostrich, Struthio camelus
10: brambling, Fringilla montifringilla
11: goldfinch, Carduelis carduelis
12: house finch, linnet, Carpodacus mexicanus
13: junco, snowbird
14: indigo bunting, indigo finch, indigo bird, Passerina cyanea
15: robin, American robin, Turdus migratorius
16: bulbul
17: jay
18: magpie
19: chickadee
20: water ouzel, dipper
21: kite
22: bald eagle, American eagle, Haliaeetus leucocephalus
23: vulture
24: great grey owl, great gray owl, Strix nebulosa
25: European fire salamander, Salamandra salamandra
26: common newt, Triturus vulgaris
27: eft
28: spotted salamander, Ambystoma maculatum
29: axolotl, mud puppy, Ambystoma mexicanum
30: bullfrog, Rana catesbeiana
31: tree frog, tree-frog
32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
33: loggerhead, loggerhead turtle, Caretta caretta
34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
35: mud turtle
36: terrapin
37: box turtle, box tortoise
38: banded gecko
39: common iguana, iguana, Iguana iguana
40: American chameleon, anole, Anolis carolinensis
41: whiptail, whiptail lizard
42: agama
43: frilled lizard, Chlamydosaurus kingi
44: alligator lizard
45: Gila monster, Heloderma suspectum
46: green lizard, Lacerta viridis
47: African chameleon, Chamaeleo chamaeleon
48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
49: African crocodile, Nile crocodile, Crocodylus niloticus
50: American alligator, Alligator mississipiensis
51: triceratops
52: thunder snake, worm snake, Carphophis amoenus
53: ringneck snake, ring-necked snake, ring snake
54: hognose snake, puff adder, sand viper
55: green snake, grass snake
56: king snake, kingsnake
57: garter snake, grass snake
58: water snake
59: vine snake
60: night snake, Hypsiglena torquata
61: boa constrictor, Constrictor constrictor
62: rock python, rock snake, Python sebae
63: Indian cobra, Naja naja
64: green mamba
65: sea snake
66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
67: diamondback, diamondback rattlesnake, Crotalus adamanteus
68: sidewinder, horned rattlesnake, Crotalus cerastes
69: trilobite
70: harvestman, daddy longlegs, Phalangium opilio
71: scorpion
72: black and gold garden spider, Argiope aurantia
73: barn spider, Araneus cavaticus
74: garden spider, Aranea diademata
75: black widow, Latrodectus mactans
76: tarantula
77: wolf spider, hunting spider
78: tick
79: centipede
80: black grouse
81: ptarmigan
82: ruffed grouse, partridge, Bonasa umbellus
83: prairie chicken, prairie grouse, prairie fowl
84: peacock
85: quail
86: partridge
87: African grey, African gray, Psittacus erithacus
88: macaw
89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
90: lorikeet
91: coucal
92: bee eater
93: hornbill
94: hummingbird
95: jacamar
96: toucan
97: drake
98: red-breasted merganser, Mergus serrator
99: goose
100: black swan, Cygnus atratus
101: tusker
102: echidna, spiny anteater, anteater
103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
104: wallaby, brush kangaroo
105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
106: wombat
107: jellyfish
108: sea anemone, anemone
109: brain coral
110: flatworm, platyhelminth
111: nematode, nematode worm, roundworm
112: conch
113: snail
114: slug
115: sea slug, nudibranch
116: chiton, coat-of-mail shell, sea cradle, polyplacophore
117: chambered nautilus, pearly nautilus, nautilus
118: Dungeness crab, Cancer magister
119: rock crab, Cancer irroratus
120: fiddler crab
121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
122: American lobster, Northern lobster, Maine lobster, Homarus americanus
123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
124: crayfish, crawfish, crawdad, crawdaddy
125: hermit crab
126: isopod
127: white stork, Ciconia ciconia
128: black stork, Ciconia nigra
129: spoonbill
130: flamingo
131: little blue heron, Egretta caerulea
132: American egret, great white heron, Egretta albus
133: bittern
134: crane
135: limpkin, Aramus pictus
136: European gallinule, Porphyrio porphyrio
137: American coot, marsh hen, mud hen, water hen, Fulica americana
138: bustard
139: ruddy turnstone, Arenaria interpres
140: red-backed sandpiper, dunlin, Erolia alpina
141: redshank, Tringa totanus
142: dowitcher
143: oystercatcher, oyster catcher
144: pelican
145: king penguin, Aptenodytes patagonica
146: albatross, mollymawk
147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca
149: dugong, Dugong dugon
150: sea lion
151: Chihuahua
152: Japanese spaniel
153: Maltese dog, Maltese terrier, Maltese
154: Pekinese, Pekingese, Peke
155: Shih-Tzu
156: Blenheim spaniel
157: papillon
158: toy terrier
159: Rhodesian ridgeback
160: Afghan hound, Afghan
161: basset, basset hound
162: beagle
163: bloodhound, sleuthhound
164: bluetick
165: black-and-tan coonhound
166: Walker hound, Walker foxhound
167: English foxhound
168: redbone
169: borzoi, Russian wolfhound
170: Irish wolfhound
171: Italian greyhound
172: whippet
173: Ibizan hound, Ibizan Podenco
174: Norwegian elkhound, elkhound
175: otterhound, otter hound
176: Saluki, gazelle hound
177: Scottish deerhound, deerhound
178: Weimaraner
179: Staffordshire bullterrier, Staffordshire bull terrier
180: American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
181: Bedlington terrier
182: Border terrier
183: Kerry blue terrier
184: Irish terrier
185: Norfolk terrier
186: Norwich terrier
187: Yorkshire terrier
188: wire-haired fox terrier
189: Lakeland terrier
190: Sealyham terrier, Sealyham
191: Airedale, Airedale terrier
192: cairn, cairn terrier
193: Australian terrier
194: Dandie Dinmont, Dandie Dinmont terrier
195: Boston bull, Boston terrier
196: miniature schnauzer
197: giant schnauzer
198: standard schnauzer
199: Scotch terrier, Scottish terrier, Scottie
200: Tibetan terrier, chrysanthemum dog
201: silky terrier, Sydney silky
202: soft-coated wheaten terrier
203: West Highland white terrier
204: Lhasa, Lhasa apso
205: flat-coated retriever
206: curly-coated retriever
207: golden retriever
208: Labrador retriever
209: Chesapeake Bay retriever
210: German short-haired pointer
211: vizsla, Hungarian pointer
212: English setter
213: Irish setter, red setter
214: Gordon setter
215: Brittany spaniel
216: clumber, clumber spaniel
217: English springer, English springer spaniel
218: Welsh springer spaniel
219: cocker spaniel, English cocker spaniel, cocker
220: Sussex spaniel
221: Irish water spaniel
222: kuvasz
223: schipperke
224: groenendael
225: malinois
226: briard
227: kelpie
228: komondor
229: Old English sheepdog, bobtail
230: Shetland sheepdog, Shetland sheep dog, Shetland
231: collie
232: Border collie
233: Bouvier des Flandres, Bouviers des Flandres
234: Rottweiler
235: German shepherd, German shepherd dog, German police dog, alsatian
236: Doberman, Doberman pinscher
237: miniature pinscher
238: Greater Swiss Mountain dog
239: Bernese mountain dog
240: Appenzeller
241: EntleBucher
242: boxer
243: bull mastiff
244: Tibetan mastiff
245: French bulldog
246: Great Dane
247: Saint Bernard, St Bernard
248: Eskimo dog, husky
249: malamute, malemute, Alaskan malamute
250: Siberian husky
251: dalmatian, coach dog, carriage dog
252: affenpinscher, monkey pinscher, monkey dog
253: basenji
254: pug, pug-dog
255: Leonberg
256: Newfoundland, Newfoundland dog
257: Great Pyrenees
258: Samoyed, Samoyede
259: Pomeranian
260: chow, chow chow
261: keeshond
262: Brabancon griffon
263: Pembroke, Pembroke Welsh corgi
264: Cardigan, Cardigan Welsh corgi
265: toy poodle
266: miniature poodle
267: standard poodle
268: Mexican hairless
269: timber wolf, grey wolf, gray wolf, Canis lupus
270: white wolf, Arctic wolf, Canis lupus tundrarum
271: red wolf, maned wolf, Canis rufus, Canis niger
272: coyote, prairie wolf, brush wolf, Canis latrans
273: dingo, warrigal, warragal, Canis dingo
274: dhole, Cuon alpinus
275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
276: hyena, hyaena
277: red fox, Vulpes vulpes
278: kit fox, Vulpes macrotis
279: Arctic fox, white fox, Alopex lagopus
280: grey fox, gray fox, Urocyon cinereoargenteus
281: tabby, tabby cat
282: tiger cat
283: Persian cat
284: Siamese cat, Siamese
285: Egyptian cat
286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
287: lynx, catamount
288: leopard, Panthera pardus
289: snow leopard, ounce, Panthera uncia
290: jaguar, panther, Panthera onca, Felis onca
291: lion, king of beasts, Panthera leo
292: tiger, Panthera tigris
293: cheetah, chetah, Acinonyx jubatus
294: brown bear, bruin, Ursus arctos
295: American black bear, black bear, Ursus americanus, Euarctos americanus
296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
297: sloth bear, Melursus ursinus, Ursus ursinus
298: mongoose
299: meerkat, mierkat
300: tiger beetle
301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
302: ground beetle, carabid beetle
303: long-horned beetle, longicorn, longicorn beetle
304: leaf beetle, chrysomelid
305: dung beetle
306: rhinoceros beetle
307: weevil
308: fly
309: bee
310: ant, emmet, pismire
311: grasshopper, hopper
312: cricket
313: walking stick, walkingstick, stick insect
314: cockroach, roach
315: mantis, mantid
316: cicada, cicala
317: leafhopper
318: lacewing, lacewing fly
319: dragonfly, darning needle, devil's darning needle, sewing needle, snake
feeder, snake doctor, mosquito hawk, skeeter hawk
320: damselfly
321: admiral
322: ringlet, ringlet butterfly
323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
324: cabbage butterfly
325: sulphur butterfly, sulfur butterfly
326: lycaenid, lycaenid butterfly
327: starfish, sea star
328: sea urchin
329: sea cucumber, holothurian
330: wood rabbit, cottontail, cottontail rabbit
331: hare
332: Angora, Angora rabbit
333: hamster
334: porcupine, hedgehog
335: fox squirrel, eastern fox squirrel, Sciurus niger
336: marmot
337: beaver
338: guinea pig, Cavia cobaya
339: sorrel
340: zebra
341: hog, pig, grunter, squealer, Sus scrofa
342: wild boar, boar, Sus scrofa
343: warthog
344: hippopotamus, hippo, river horse, Hippopotamus amphibius
345: ox
346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
347: bison
348: ram, tup
349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
350: ibex, Capra ibex
351: hartebeest
352: impala, Aepyceros melampus
353: gazelle
354: Arabian camel, dromedary, Camelus dromedarius
355: llama
356: weasel
357: mink
358: polecat, fitch, foulmart, foumart, Mustela putorius
359: black-footed ferret, ferret, Mustela nigripes
360: otter
361: skunk, polecat, wood pussy
362: badger
363: armadillo
364: three-toed sloth, ai, Bradypus tridactylus
365: orangutan, orang, orangutang, Pongo pygmaeus
366: gorilla, Gorilla gorilla
367: chimpanzee, chimp, Pan troglodytes
368: gibbon, Hylobates lar
369: siamang, Hylobates syndactylus, Symphalangus syndactylus
370: guenon, guenon monkey
371: patas, hussar monkey, Erythrocebus patas
372: baboon
373: macaque
374: langur
375: colobus, colobus monkey
376: proboscis monkey, Nasalis larvatus
377: marmoset
378: capuchin, ringtail, Cebus capucinus
379: howler monkey, howler
380: titi, titi monkey
381: spider monkey, Ateles geoffroyi
382: squirrel monkey, Saimiri sciureus
383: Madagascar cat, ring-tailed lemur, Lemur catta
384: indri, indris, Indri indri, Indri brevicaudatus
385: Indian elephant, Elephas maximus
386: African elephant, Loxodonta africana
387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
389: barracouta, snoek
390: eel
391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
392: rock beauty, Holocanthus tricolor
393: anemone fish
394: sturgeon
395: gar, garfish, garpike, billfish, Lepisosteus osseus
396: lionfish
397: puffer, pufferfish, blowfish, globefish
398: abacus
399: abaya
400: academic gown, academic robe, judge's robe
401: accordion, piano accordion, squeeze box
402: acoustic guitar
403: aircraft carrier, carrier, flattop, attack aircraft carrier
404: airliner
405: airship, dirigible
406: altar
407: ambulance
408: amphibian, amphibious vehicle
409: analog clock
410: apiary, bee house
411: apron
412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
413: assault rifle, assault gun
414: backpack, back pack, knapsack, packsack, rucksack, haversack
415: bakery, bakeshop, bakehouse
416: balance beam, beam
417: balloon
418: ballpoint, ballpoint pen, ballpen, Biro
419: Band Aid
420: banjo
421: bannister, banister, balustrade, balusters, handrail
422: barbell
423: barber chair
424: barbershop
425: barn
426: barometer
427: barrel, cask
428: barrow, garden cart, lawn cart, wheelbarrow
429: baseball
430: basketball
431: bassinet
432: bassoon
433: bathing cap, swimming cap
434: bath towel
435: bathtub, bathing tub, bath, tub
436: beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
437: beacon, lighthouse, beacon light, pharos
438: beaker
439: bearskin, busby, shako
440: beer bottle
441: beer glass
442: bell cote, bell cot
443: bib
444: bicycle-built-for-two, tandem bicycle, tandem
445: bikini, two-piece
446: binder, ring-binder
447: binoculars, field glasses, opera glasses
448: birdhouse
449: boathouse
450: bobsled, bobsleigh, bob
451: bolo tie, bolo, bola tie, bola
452: bonnet, poke bonnet
453: bookcase
454: bookshop, bookstore, bookstall
455: bottlecap
456: bow
457: bow tie, bow-tie, bowtie
458: brass, memorial tablet, plaque
459: brassiere, bra, bandeau
460: breakwater, groin, groyne, mole, bulwark, seawall, jetty
461: breastplate, aegis, egis
462: broom
463: bucket, pail
464: buckle
465: bulletproof vest
466: bullet train, bullet
467: butcher shop, meat market
468: cab, hack, taxi, taxicab
469: caldron, cauldron
470: candle, taper, wax light
471: cannon
472: canoe
473: can opener, tin opener
474: cardigan
475: car mirror
476: carousel, carrousel, merry-go-round, roundabout, whirligig
477: carpenter's kit, tool kit
478: carton
479: car wheel
480: cash machine, cash dispenser, automated teller machine, automatic teller
machine, automated teller, automatic teller, ATM
481: cassette
482: cassette player
483: castle
484: catamaran
485: CD player
486: cello, violoncello
487: cellular telephone, cellular phone, cellphone, cell, mobile phone
488: chain
489: chainlink fence
490: chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
491: chain saw, chainsaw
492: chest
493: chiffonier, commode
494: chime, bell, gong
495: china cabinet, china closet
496: Christmas stocking
497: church, church building
498: cinema, movie theater, movie theatre, movie house, picture palace
499: cleaver, meat cleaver, chopper
500: cliff dwelling
501: cloak
502: clog, geta, patten, sabot
503: cocktail shaker
504: coffee mug
505: coffeepot
506: coil, spiral, volute, whorl, helix
507: combination lock
508: computer keyboard, keypad
509: confectionery, confectionary, candy store
510: container ship, containership, container vessel
511: convertible
512: corkscrew, bottle screw
513: cornet, horn, trumpet, trump
514: cowboy boot
515: cowboy hat, ten-gallon hat
516: cradle
517: crane2
518: crash helmet
519: crate
520: crib, cot
521: Crock Pot
522: croquet ball
523: crutch
524: cuirass
525: dam, dike, dyke
526: desk
527: desktop computer
528: dial telephone, dial phone
529: diaper, nappy, napkin
530: digital clock
531: digital watch
532: dining table, board
533: dishrag, dishcloth
534: dishwasher, dish washer, dishwashing machine
535: disk brake, disc brake
536: dock, dockage, docking facility
537: dogsled, dog sled, dog sleigh
538: dome
539: doormat, welcome mat
540: drilling platform, offshore rig
541: drum, membranophone, tympan
542: drumstick
543: dumbbell
544: Dutch oven
545: electric fan, blower
546: electric guitar
547: electric locomotive
548: entertainment center
549: envelope
550: espresso maker
551: face powder
552: feather boa, boa
553: file, file cabinet, filing cabinet
554: fireboat
555: fire engine, fire truck
556: fire screen, fireguard
557: flagpole, flagstaff
558: flute, transverse flute
559: folding chair
560: football helmet
561: forklift
562: fountain
563: fountain pen
564: four-poster
565: freight car
566: French horn, horn
567: frying pan, frypan, skillet
568: fur coat
569: garbage truck, dustcart
570: gasmask, respirator, gas helmet
571: gas pump, gasoline pump, petrol pump, island dispenser
572: goblet
573: go-kart
574: golf ball
575: golfcart, golf cart
576: gondola
577: gong, tam-tam
578: gown
579: grand piano, grand
580: greenhouse, nursery, glasshouse
581: grille, radiator grille
582: grocery store, grocery, food market, market
583: guillotine
584: hair slide
585: hair spray
586: half track
587: hammer
588: hamper
589: hand blower, blow dryer, blow drier, hair dryer, hair drier
590: hand-held computer, hand-held microcomputer
591: handkerchief, hankie, hanky, hankey
592: hard disc, hard disk, fixed disk
593: harmonica, mouth organ, harp, mouth harp
594: harp
595: harvester, reaper
596: hatchet
597: holster
598: home theater, home theatre
599: honeycomb
600: hook, claw
601: hoopskirt, crinoline
602: horizontal bar, high bar
603: horse cart, horse-cart
604: hourglass
605: iPod
606: iron, smoothing iron
607: jack-o'-lantern
608: jean, blue jean, denim
609: jeep, landrover
610: jersey, T-shirt, tee shirt
611: jigsaw puzzle
612: jinrikisha, ricksha, rickshaw
613: joystick
614: kimono
615: knee pad
616: knot
617: lab coat, laboratory coat
618: ladle
619: lampshade, lamp shade
620: laptop, laptop computer
621: lawn mower, mower
622: lens cap, lens cover
623: letter opener, paper knife, paperknife
624: library
625: lifeboat
626: lighter, light, igniter, ignitor
627: limousine, limo
628: liner, ocean liner
629: lipstick, lip rouge
630: Loafer
631: lotion
632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
633: loupe, jeweler's loupe
634: lumbermill, sawmill
635: magnetic compass
636: mailbag, postbag
637: mailbox, letter box
638: maillot
639: maillot, tank suit
640: manhole cover
641: maraca
642: marimba, xylophone
643: mask
644: matchstick
645: maypole
646: maze, labyrinth
647: measuring cup
648: medicine chest, medicine cabinet
649: megalith, megalithic structure
650: microphone, mike
651: microwave, microwave oven
652: military uniform
653: milk can
654: minibus
655: miniskirt, mini
656: minivan
657: missile
658: mitten
659: mixing bowl
660: mobile home, manufactured home
661: Model T
662: modem
663: monastery
664: monitor
665: moped
666: mortar
667: mortarboard
668: mosque
669: mosquito net
670: motor scooter, scooter
671: mountain bike, all-terrain bike, off-roader
672: mountain tent
673: mouse, computer mouse
674: mousetrap
675: moving van
676: muzzle
677: nail
678: neck brace
679: necklace
680: nipple
681: notebook, notebook computer
682: obelisk
683: oboe, hautboy, hautbois
684: ocarina, sweet potato
685: odometer, hodometer, mileometer, milometer
686: oil filter
687: organ, pipe organ
688: oscilloscope, scope, cathode-ray oscilloscope, CRO
689: overskirt
690: oxcart
691: oxygen mask
692: packet
693: paddle, boat paddle
694: paddlewheel, paddle wheel
695: padlock
696: paintbrush
697: pajama, pyjama, pj's, jammies
698: palace
699: panpipe, pandean pipe, syrinx
700: paper towel
701: parachute, chute
702: parallel bars, bars
703: park bench
704: parking meter
705: passenger car, coach, carriage
706: patio, terrace
707: pay-phone, pay-station
708: pedestal, plinth, footstall
709: pencil box, pencil case
710: pencil sharpener
711: perfume, essence
712: Petri dish
713: photocopier
714: pick, plectrum, plectron
715: pickelhaube
716: picket fence, paling
717: pickup, pickup truck
718: pier
719: piggy bank, penny bank
720: pill bottle
721: pillow
722: ping-pong ball
723: pinwheel
724: pirate, pirate ship
725: pitcher, ewer
726: plane, carpenter's plane, woodworking plane
727: planetarium
728: plastic bag
729: plate rack
730: plow, plough
731: plunger, plumber's helper
732: Polaroid camera, Polaroid Land camera
733: pole
734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
735: poncho
736: pool table, billiard table, snooker table
737: pop bottle, soda bottle
738: pot, flowerpot
739: potter's wheel
740: power drill
741: prayer rug, prayer mat
742: printer
743: prison, prison house
744: projectile, missile
745: projector
746: puck, hockey puck
747: punching bag, punch bag, punching ball, punchball
748: purse
749: quill, quill pen
750: quilt, comforter, comfort, puff
751: racer, race car, racing car
752: racket, racquet
753: radiator
754: radio, wireless
755: radio telescope, radio reflector
756: rain barrel
757: recreational vehicle, RV, R.V.
758: reel
759: reflex camera
760: refrigerator, icebox
761: remote control, remote
762: restaurant, eating house, eating place, eatery
763: revolver, six-gun, six-shooter
764: rifle
765: rocking chair, rocker
766: rotisserie
767: rubber eraser, rubber, pencil eraser
768: rugby ball
769: rule, ruler
770: running shoe
771: safe
772: safety pin
773: saltshaker, salt shaker
774: sandal
775: sarong
776: sax, saxophone
777: scabbard
778: scale, weighing machine
779: school bus
780: schooner
781: scoreboard
782: screen, CRT screen
783: screw
784: screwdriver
785: seat belt, seatbelt
786: sewing machine
787: shield, buckler
788: shoe shop, shoe-shop, shoe store
789: shoji
790: shopping basket
791: shopping cart
792: shovel
793: shower cap
794: shower curtain
795: ski
796: ski mask
797: sleeping bag
798: slide rule, slipstick
799: sliding door
800: slot, one-armed bandit
801: snorkel
802: snowmobile
803: snowplow, snowplough
804: soap dispenser
805: soccer ball
806: sock
807: solar dish, solar collector, solar furnace
808: sombrero
809: soup bowl
810: space bar
811: space heater
812: space shuttle
813: spatula
814: speedboat
815: spider web, spider's web
816: spindle
817: sports car, sport car
818: spotlight, spot
819: stage
820: steam locomotive
821: steel arch bridge
822: steel drum
823: stethoscope
824: stole
825: stone wall
826: stopwatch, stop watch
827: stove
828: strainer
829: streetcar, tram, tramcar, trolley, trolley car
830: stretcher
831: studio couch, day bed
832: stupa, tope
833: submarine, pigboat, sub, U-boat
834: suit, suit of clothes
835: sundial
836: sunglass
837: sunglasses, dark glasses, shades
838: sunscreen, sunblock, sun blocker
839: suspension bridge
840: swab, swob, mop
841: sweatshirt
842: swimming trunks, bathing trunks
843: swing
844: switch, electric switch, electrical switch
845: syringe
846: table lamp
847: tank, army tank, armored combat vehicle, armoured combat vehicle
848: tape player
849: teapot
850: teddy, teddy bear
851: television, television system
852: tennis ball
853: thatch, thatched roof
854: theater curtain, theatre curtain
855: thimble
856: thresher, thrasher, threshing machine
857: throne
858: tile roof
859: toaster
860: tobacco shop, tobacconist shop, tobacconist
861: toilet seat
862: torch
863: totem pole
864: tow truck, tow car, wrecker
865: toyshop
866: tractor
867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
868: tray
869: trench coat
870: tricycle, trike, velocipede
871: trimaran
872: tripod
873: triumphal arch
874: trolleybus, trolley coach, trackless trolley
875: trombone
876: tub, vat
877: turnstile
878: typewriter keyboard
879: umbrella
880: unicycle, monocycle
881: upright, upright piano
882: vacuum, vacuum cleaner
883: vase
884: vault
885: velvet
886: vending machine
887: vestment
888: viaduct
889: violin, fiddle
890: volleyball
891: waffle iron
892: wall clock
893: wallet, billfold, notecase, pocketbook
894: wardrobe, closet, press
895: warplane, military plane
896: washbasin, handbasin, washbowl, lavabo, wash-hand basin
897: washer, automatic washer, washing machine
898: water bottle
899: water jug
900: water tower
901: whiskey jug
902: whistle
903: wig
904: window screen
905: window shade
906: Windsor tie
907: wine bottle
908: wing
909: wok
910: wooden spoon
911: wool, woolen, woollen
912: worm fence, snake fence, snake-rail fence, Virginia fence
913: wreck
914: yawl
915: yurt
916: web site, website, internet site, site
917: comic book
918: crossword puzzle, crossword
919: street sign
920: traffic light, traffic signal, stoplight
921: book jacket, dust cover, dust jacket, dust wrapper
922: menu
923: plate
924: guacamole
925: consomme
926: hot pot, hotpot
927: trifle
928: ice cream, icecream
929: ice lolly, lolly, lollipop, popsicle
930: French loaf
931: bagel, beigel
932: pretzel
933: cheeseburger
934: hotdog, hot dog, red hot
935: mashed potato
936: head cabbage
937: broccoli
938: cauliflower
939: zucchini, courgette
940: spaghetti squash
941: acorn squash
942: butternut squash
943: cucumber, cuke
944: artichoke, globe artichoke
945: bell pepper
946: cardoon
947: mushroom
948: Granny Smith
949: strawberry
950: orange
951: lemon
952: fig
953: pineapple, ananas
954: banana
955: jackfruit, jak, jack
956: custard apple
957: pomegranate
958: hay
959: carbonara
960: chocolate sauce, chocolate syrup
961: dough
962: meat loaf, meatloaf
963: pizza, pizza pie
964: potpie
965: burrito
966: red wine
967: espresso
968: cup
969: eggnog
970: alp
971: bubble
972: cliff, drop, drop-off
973: coral reef
974: geyser
975: lakeside, lakeshore
976: promontory, headland, head, foreland
977: sandbar, sand bar
978: seashore, coast, seacoast, sea-coast
979: valley, vale
980: volcano
981: ballplayer, baseball player
982: groom, bridegroom
983: scuba diver
984: rapeseed
985: daisy
986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
987: corn
988: acorn
989: hip, rose hip, rosehip
990: buckeye, horse chestnut, conker
991: coral fungus
992: agaric
993: gyromitra
994: stinkhorn, carrion fungus
995: earthstar
996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
997: bolete
998: ear, spike, capitulum
999: toilet tissue, toilet paper, bathroom tissue
splits:
- name: test
num_bytes: 13613661561
num_examples: 100000
- name: train
num_bytes: 146956944242
num_examples: 1281167
- name: validation
num_bytes: 6709003386
num_examples: 50000
download_size: 166009941208
dataset_size: 167279609189
---
# Dataset Card for ImageNet
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://image-net.org/index.php
- **Repository:**
- **Paper:** https://arxiv.org/abs/1409.0575
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
- **Point of Contact:** mailto: [email protected]
### Dataset Summary
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.
💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171).
To evaluate the `imagenet-classification` accuracy on the test split, one must first create an account at https://image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:
```
670 778 794 387 650
217 691 564 909 364
737 369 430 531 124
755 930 755 512 152
```
The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz. Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See `imagenet2012_labels.txt`.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
An example looks like below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'label': 23
}
```
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label. -1 for `test` set as the labels are missing.
The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.
<details>
<summary>
Click here to see the full list of ImageNet class labels mapping:
</summary>
|id|Class|
|--|-----|
|0 | tench, Tinca tinca|
|1 | goldfish, Carassius auratus|
|2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
|3 | tiger shark, Galeocerdo cuvieri|
|4 | hammerhead, hammerhead shark|
|5 | electric ray, crampfish, numbfish, torpedo|
|6 | stingray|
|7 | cock|
|8 | hen|
|9 | ostrich, Struthio camelus|
|10 | brambling, Fringilla montifringilla|
|11 | goldfinch, Carduelis carduelis|
|12 | house finch, linnet, Carpodacus mexicanus|
|13 | junco, snowbird|
|14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
|15 | robin, American robin, Turdus migratorius|
|16 | bulbul|
|17 | jay|
|18 | magpie|
|19 | chickadee|
|20 | water ouzel, dipper|
|21 | kite|
|22 | bald eagle, American eagle, Haliaeetus leucocephalus|
|23 | vulture|
|24 | great grey owl, great gray owl, Strix nebulosa|
|25 | European fire salamander, Salamandra salamandra|
|26 | common newt, Triturus vulgaris|
|27 | eft|
|28 | spotted salamander, Ambystoma maculatum|
|29 | axolotl, mud puppy, Ambystoma mexicanum|
|30 | bullfrog, Rana catesbeiana|
|31 | tree frog, tree-frog|
|32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
|33 | loggerhead, loggerhead turtle, Caretta caretta|
|34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
|35 | mud turtle|
|36 | terrapin|
|37 | box turtle, box tortoise|
|38 | banded gecko|
|39 | common iguana, iguana, Iguana iguana|
|40 | American chameleon, anole, Anolis carolinensis|
|41 | whiptail, whiptail lizard|
|42 | agama|
|43 | frilled lizard, Chlamydosaurus kingi|
|44 | alligator lizard|
|45 | Gila monster, Heloderma suspectum|
|46 | green lizard, Lacerta viridis|
|47 | African chameleon, Chamaeleo chamaeleon|
|48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
|49 | African crocodile, Nile crocodile, Crocodylus niloticus|
|50 | American alligator, Alligator mississipiensis|
|51 | triceratops|
|52 | thunder snake, worm snake, Carphophis amoenus|
|53 | ringneck snake, ring-necked snake, ring snake|
|54 | hognose snake, puff adder, sand viper|
|55 | green snake, grass snake|
|56 | king snake, kingsnake|
|57 | garter snake, grass snake|
|58 | water snake|
|59 | vine snake|
|60 | night snake, Hypsiglena torquata|
|61 | boa constrictor, Constrictor constrictor|
|62 | rock python, rock snake, Python sebae|
|63 | Indian cobra, Naja naja|
|64 | green mamba|
|65 | sea snake|
|66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
|67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
|68 | sidewinder, horned rattlesnake, Crotalus cerastes|
|69 | trilobite|
|70 | harvestman, daddy longlegs, Phalangium opilio|
|71 | scorpion|
|72 | black and gold garden spider, Argiope aurantia|
|73 | barn spider, Araneus cavaticus|
|74 | garden spider, Aranea diademata|
|75 | black widow, Latrodectus mactans|
|76 | tarantula|
|77 | wolf spider, hunting spider|
|78 | tick|
|79 | centipede|
|80 | black grouse|
|81 | ptarmigan|
|82 | ruffed grouse, partridge, Bonasa umbellus|
|83 | prairie chicken, prairie grouse, prairie fowl|
|84 | peacock|
|85 | quail|
|86 | partridge|
|87 | African grey, African gray, Psittacus erithacus|
|88 | macaw|
|89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
|90 | lorikeet|
|91 | coucal|
|92 | bee eater|
|93 | hornbill|
|94 | hummingbird|
|95 | jacamar|
|96 | toucan|
|97 | drake|
|98 | red-breasted merganser, Mergus serrator|
|99 | goose|
|100 | black swan, Cygnus atratus|
|101 | tusker|
|102 | echidna, spiny anteater, anteater|
|103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
|104 | wallaby, brush kangaroo|
|105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
|106 | wombat|
|107 | jellyfish|
|108 | sea anemone, anemone|
|109 | brain coral|
|110 | flatworm, platyhelminth|
|111 | nematode, nematode worm, roundworm|
|112 | conch|
|113 | snail|
|114 | slug|
|115 | sea slug, nudibranch|
|116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
|117 | chambered nautilus, pearly nautilus, nautilus|
|118 | Dungeness crab, Cancer magister|
|119 | rock crab, Cancer irroratus|
|120 | fiddler crab|
|121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
|122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
|123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
|124 | crayfish, crawfish, crawdad, crawdaddy|
|125 | hermit crab|
|126 | isopod|
|127 | white stork, Ciconia ciconia|
|128 | black stork, Ciconia nigra|
|129 | spoonbill|
|130 | flamingo|
|131 | little blue heron, Egretta caerulea|
|132 | American egret, great white heron, Egretta albus|
|133 | bittern|
|134 | crane|
|135 | limpkin, Aramus pictus|
|136 | European gallinule, Porphyrio porphyrio|
|137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
|138 | bustard|
|139 | ruddy turnstone, Arenaria interpres|
|140 | red-backed sandpiper, dunlin, Erolia alpina|
|141 | redshank, Tringa totanus|
|142 | dowitcher|
|143 | oystercatcher, oyster catcher|
|144 | pelican|
|145 | king penguin, Aptenodytes patagonica|
|146 | albatross, mollymawk|
|147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
|148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
|149 | dugong, Dugong dugon|
|150 | sea lion|
|151 | Chihuahua|
|152 | Japanese spaniel|
|153 | Maltese dog, Maltese terrier, Maltese|
|154 | Pekinese, Pekingese, Peke|
|155 | Shih-Tzu|
|156 | Blenheim spaniel|
|157 | papillon|
|158 | toy terrier|
|159 | Rhodesian ridgeback|
|160 | Afghan hound, Afghan|
|161 | basset, basset hound|
|162 | beagle|
|163 | bloodhound, sleuthhound|
|164 | bluetick|
|165 | black-and-tan coonhound|
|166 | Walker hound, Walker foxhound|
|167 | English foxhound|
|168 | redbone|
|169 | borzoi, Russian wolfhound|
|170 | Irish wolfhound|
|171 | Italian greyhound|
|172 | whippet|
|173 | Ibizan hound, Ibizan Podenco|
|174 | Norwegian elkhound, elkhound|
|175 | otterhound, otter hound|
|176 | Saluki, gazelle hound|
|177 | Scottish deerhound, deerhound|
|178 | Weimaraner|
|179 | Staffordshire bullterrier, Staffordshire bull terrier|
|180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
|181 | Bedlington terrier|
|182 | Border terrier|
|183 | Kerry blue terrier|
|184 | Irish terrier|
|185 | Norfolk terrier|
|186 | Norwich terrier|
|187 | Yorkshire terrier|
|188 | wire-haired fox terrier|
|189 | Lakeland terrier|
|190 | Sealyham terrier, Sealyham|
|191 | Airedale, Airedale terrier|
|192 | cairn, cairn terrier|
|193 | Australian terrier|
|194 | Dandie Dinmont, Dandie Dinmont terrier|
|195 | Boston bull, Boston terrier|
|196 | miniature schnauzer|
|197 | giant schnauzer|
|198 | standard schnauzer|
|199 | Scotch terrier, Scottish terrier, Scottie|
|200 | Tibetan terrier, chrysanthemum dog|
|201 | silky terrier, Sydney silky|
|202 | soft-coated wheaten terrier|
|203 | West Highland white terrier|
|204 | Lhasa, Lhasa apso|
|205 | flat-coated retriever|
|206 | curly-coated retriever|
|207 | golden retriever|
|208 | Labrador retriever|
|209 | Chesapeake Bay retriever|
|210 | German short-haired pointer|
|211 | vizsla, Hungarian pointer|
|212 | English setter|
|213 | Irish setter, red setter|
|214 | Gordon setter|
|215 | Brittany spaniel|
|216 | clumber, clumber spaniel|
|217 | English springer, English springer spaniel|
|218 | Welsh springer spaniel|
|219 | cocker spaniel, English cocker spaniel, cocker|
|220 | Sussex spaniel|
|221 | Irish water spaniel|
|222 | kuvasz|
|223 | schipperke|
|224 | groenendael|
|225 | malinois|
|226 | briard|
|227 | kelpie|
|228 | komondor|
|229 | Old English sheepdog, bobtail|
|230 | Shetland sheepdog, Shetland sheep dog, Shetland|
|231 | collie|
|232 | Border collie|
|233 | Bouvier des Flandres, Bouviers des Flandres|
|234 | Rottweiler|
|235 | German shepherd, German shepherd dog, German police dog, alsatian|
|236 | Doberman, Doberman pinscher|
|237 | miniature pinscher|
|238 | Greater Swiss Mountain dog|
|239 | Bernese mountain dog|
|240 | Appenzeller|
|241 | EntleBucher|
|242 | boxer|
|243 | bull mastiff|
|244 | Tibetan mastiff|
|245 | French bulldog|
|246 | Great Dane|
|247 | Saint Bernard, St Bernard|
|248 | Eskimo dog, husky|
|249 | malamute, malemute, Alaskan malamute|
|250 | Siberian husky|
|251 | dalmatian, coach dog, carriage dog|
|252 | affenpinscher, monkey pinscher, monkey dog|
|253 | basenji|
|254 | pug, pug-dog|
|255 | Leonberg|
|256 | Newfoundland, Newfoundland dog|
|257 | Great Pyrenees|
|258 | Samoyed, Samoyede|
|259 | Pomeranian|
|260 | chow, chow chow|
|261 | keeshond|
|262 | Brabancon griffon|
|263 | Pembroke, Pembroke Welsh corgi|
|264 | Cardigan, Cardigan Welsh corgi|
|265 | toy poodle|
|266 | miniature poodle|
|267 | standard poodle|
|268 | Mexican hairless|
|269 | timber wolf, grey wolf, gray wolf, Canis lupus|
|270 | white wolf, Arctic wolf, Canis lupus tundrarum|
|271 | red wolf, maned wolf, Canis rufus, Canis niger|
|272 | coyote, prairie wolf, brush wolf, Canis latrans|
|273 | dingo, warrigal, warragal, Canis dingo|
|274 | dhole, Cuon alpinus|
|275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
|276 | hyena, hyaena|
|277 | red fox, Vulpes vulpes|
|278 | kit fox, Vulpes macrotis|
|279 | Arctic fox, white fox, Alopex lagopus|
|280 | grey fox, gray fox, Urocyon cinereoargenteus|
|281 | tabby, tabby cat|
|282 | tiger cat|
|283 | Persian cat|
|284 | Siamese cat, Siamese|
|285 | Egyptian cat|
|286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
|287 | lynx, catamount|
|288 | leopard, Panthera pardus|
|289 | snow leopard, ounce, Panthera uncia|
|290 | jaguar, panther, Panthera onca, Felis onca|
|291 | lion, king of beasts, Panthera leo|
|292 | tiger, Panthera tigris|
|293 | cheetah, chetah, Acinonyx jubatus|
|294 | brown bear, bruin, Ursus arctos|
|295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
|296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
|297 | sloth bear, Melursus ursinus, Ursus ursinus|
|298 | mongoose|
|299 | meerkat, mierkat|
|300 | tiger beetle|
|301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
|302 | ground beetle, carabid beetle|
|303 | long-horned beetle, longicorn, longicorn beetle|
|304 | leaf beetle, chrysomelid|
|305 | dung beetle|
|306 | rhinoceros beetle|
|307 | weevil|
|308 | fly|
|309 | bee|
|310 | ant, emmet, pismire|
|311 | grasshopper, hopper|
|312 | cricket|
|313 | walking stick, walkingstick, stick insect|
|314 | cockroach, roach|
|315 | mantis, mantid|
|316 | cicada, cicala|
|317 | leafhopper|
|318 | lacewing, lacewing fly|
|319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
|320 | damselfly|
|321 | admiral|
|322 | ringlet, ringlet butterfly|
|323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
|324 | cabbage butterfly|
|325 | sulphur butterfly, sulfur butterfly|
|326 | lycaenid, lycaenid butterfly|
|327 | starfish, sea star|
|328 | sea urchin|
|329 | sea cucumber, holothurian|
|330 | wood rabbit, cottontail, cottontail rabbit|
|331 | hare|
|332 | Angora, Angora rabbit|
|333 | hamster|
|334 | porcupine, hedgehog|
|335 | fox squirrel, eastern fox squirrel, Sciurus niger|
|336 | marmot|
|337 | beaver|
|338 | guinea pig, Cavia cobaya|
|339 | sorrel|
|340 | zebra|
|341 | hog, pig, grunter, squealer, Sus scrofa|
|342 | wild boar, boar, Sus scrofa|
|343 | warthog|
|344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
|345 | ox|
|346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
|347 | bison|
|348 | ram, tup|
|349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
|350 | ibex, Capra ibex|
|351 | hartebeest|
|352 | impala, Aepyceros melampus|
|353 | gazelle|
|354 | Arabian camel, dromedary, Camelus dromedarius|
|355 | llama|
|356 | weasel|
|357 | mink|
|358 | polecat, fitch, foulmart, foumart, Mustela putorius|
|359 | black-footed ferret, ferret, Mustela nigripes|
|360 | otter|
|361 | skunk, polecat, wood pussy|
|362 | badger|
|363 | armadillo|
|364 | three-toed sloth, ai, Bradypus tridactylus|
|365 | orangutan, orang, orangutang, Pongo pygmaeus|
|366 | gorilla, Gorilla gorilla|
|367 | chimpanzee, chimp, Pan troglodytes|
|368 | gibbon, Hylobates lar|
|369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
|370 | guenon, guenon monkey|
|371 | patas, hussar monkey, Erythrocebus patas|
|372 | baboon|
|373 | macaque|
|374 | langur|
|375 | colobus, colobus monkey|
|376 | proboscis monkey, Nasalis larvatus|
|377 | marmoset|
|378 | capuchin, ringtail, Cebus capucinus|
|379 | howler monkey, howler|
|380 | titi, titi monkey|
|381 | spider monkey, Ateles geoffroyi|
|382 | squirrel monkey, Saimiri sciureus|
|383 | Madagascar cat, ring-tailed lemur, Lemur catta|
|384 | indri, indris, Indri indri, Indri brevicaudatus|
|385 | Indian elephant, Elephas maximus|
|386 | African elephant, Loxodonta africana|
|387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
|388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
|389 | barracouta, snoek|
|390 | eel|
|391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
|392 | rock beauty, Holocanthus tricolor|
|393 | anemone fish|
|394 | sturgeon|
|395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
|396 | lionfish|
|397 | puffer, pufferfish, blowfish, globefish|
|398 | abacus|
|399 | abaya|
|400 | academic gown, academic robe, judge's robe|
|401 | accordion, piano accordion, squeeze box|
|402 | acoustic guitar|
|403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
|404 | airliner|
|405 | airship, dirigible|
|406 | altar|
|407 | ambulance|
|408 | amphibian, amphibious vehicle|
|409 | analog clock|
|410 | apiary, bee house|
|411 | apron|
|412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
|413 | assault rifle, assault gun|
|414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
|415 | bakery, bakeshop, bakehouse|
|416 | balance beam, beam|
|417 | balloon|
|418 | ballpoint, ballpoint pen, ballpen, Biro|
|419 | Band Aid|
|420 | banjo|
|421 | bannister, banister, balustrade, balusters, handrail|
|422 | barbell|
|423 | barber chair|
|424 | barbershop|
|425 | barn|
|426 | barometer|
|427 | barrel, cask|
|428 | barrow, garden cart, lawn cart, wheelbarrow|
|429 | baseball|
|430 | basketball|
|431 | bassinet|
|432 | bassoon|
|433 | bathing cap, swimming cap|
|434 | bath towel|
|435 | bathtub, bathing tub, bath, tub|
|436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
|437 | beacon, lighthouse, beacon light, pharos|
|438 | beaker|
|439 | bearskin, busby, shako|
|440 | beer bottle|
|441 | beer glass|
|442 | bell cote, bell cot|
|443 | bib|
|444 | bicycle-built-for-two, tandem bicycle, tandem|
|445 | bikini, two-piece|
|446 | binder, ring-binder|
|447 | binoculars, field glasses, opera glasses|
|448 | birdhouse|
|449 | boathouse|
|450 | bobsled, bobsleigh, bob|
|451 | bolo tie, bolo, bola tie, bola|
|452 | bonnet, poke bonnet|
|453 | bookcase|
|454 | bookshop, bookstore, bookstall|
|455 | bottlecap|
|456 | bow|
|457 | bow tie, bow-tie, bowtie|
|458 | brass, memorial tablet, plaque|
|459 | brassiere, bra, bandeau|
|460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
|461 | breastplate, aegis, egis|
|462 | broom|
|463 | bucket, pail|
|464 | buckle|
|465 | bulletproof vest|
|466 | bullet train, bullet|
|467 | butcher shop, meat market|
|468 | cab, hack, taxi, taxicab|
|469 | caldron, cauldron|
|470 | candle, taper, wax light|
|471 | cannon|
|472 | canoe|
|473 | can opener, tin opener|
|474 | cardigan|
|475 | car mirror|
|476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
|477 | carpenter's kit, tool kit|
|478 | carton|
|479 | car wheel|
|480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
|481 | cassette|
|482 | cassette player|
|483 | castle|
|484 | catamaran|
|485 | CD player|
|486 | cello, violoncello|
|487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
|488 | chain|
|489 | chainlink fence|
|490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
|491 | chain saw, chainsaw|
|492 | chest|
|493 | chiffonier, commode|
|494 | chime, bell, gong|
|495 | china cabinet, china closet|
|496 | Christmas stocking|
|497 | church, church building|
|498 | cinema, movie theater, movie theatre, movie house, picture palace|
|499 | cleaver, meat cleaver, chopper|
|500 | cliff dwelling|
|501 | cloak|
|502 | clog, geta, patten, sabot|
|503 | cocktail shaker|
|504 | coffee mug|
|505 | coffeepot|
|506 | coil, spiral, volute, whorl, helix|
|507 | combination lock|
|508 | computer keyboard, keypad|
|509 | confectionery, confectionary, candy store|
|510 | container ship, containership, container vessel|
|511 | convertible|
|512 | corkscrew, bottle screw|
|513 | cornet, horn, trumpet, trump|
|514 | cowboy boot|
|515 | cowboy hat, ten-gallon hat|
|516 | cradle|
|517 | crane_1|
|518 | crash helmet|
|519 | crate|
|520 | crib, cot|
|521 | Crock Pot|
|522 | croquet ball|
|523 | crutch|
|524 | cuirass|
|525 | dam, dike, dyke|
|526 | desk|
|527 | desktop computer|
|528 | dial telephone, dial phone|
|529 | diaper, nappy, napkin|
|530 | digital clock|
|531 | digital watch|
|532 | dining table, board|
|533 | dishrag, dishcloth|
|534 | dishwasher, dish washer, dishwashing machine|
|535 | disk brake, disc brake|
|536 | dock, dockage, docking facility|
|537 | dogsled, dog sled, dog sleigh|
|538 | dome|
|539 | doormat, welcome mat|
|540 | drilling platform, offshore rig|
|541 | drum, membranophone, tympan|
|542 | drumstick|
|543 | dumbbell|
|544 | Dutch oven|
|545 | electric fan, blower|
|546 | electric guitar|
|547 | electric locomotive|
|548 | entertainment center|
|549 | envelope|
|550 | espresso maker|
|551 | face powder|
|552 | feather boa, boa|
|553 | file, file cabinet, filing cabinet|
|554 | fireboat|
|555 | fire engine, fire truck|
|556 | fire screen, fireguard|
|557 | flagpole, flagstaff|
|558 | flute, transverse flute|
|559 | folding chair|
|560 | football helmet|
|561 | forklift|
|562 | fountain|
|563 | fountain pen|
|564 | four-poster|
|565 | freight car|
|566 | French horn, horn|
|567 | frying pan, frypan, skillet|
|568 | fur coat|
|569 | garbage truck, dustcart|
|570 | gasmask, respirator, gas helmet|
|571 | gas pump, gasoline pump, petrol pump, island dispenser|
|572 | goblet|
|573 | go-kart|
|574 | golf ball|
|575 | golfcart, golf cart|
|576 | gondola|
|577 | gong, tam-tam|
|578 | gown|
|579 | grand piano, grand|
|580 | greenhouse, nursery, glasshouse|
|581 | grille, radiator grille|
|582 | grocery store, grocery, food market, market|
|583 | guillotine|
|584 | hair slide|
|585 | hair spray|
|586 | half track|
|587 | hammer|
|588 | hamper|
|589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
|590 | hand-held computer, hand-held microcomputer|
|591 | handkerchief, hankie, hanky, hankey|
|592 | hard disc, hard disk, fixed disk|
|593 | harmonica, mouth organ, harp, mouth harp|
|594 | harp|
|595 | harvester, reaper|
|596 | hatchet|
|597 | holster|
|598 | home theater, home theatre|
|599 | honeycomb|
|600 | hook, claw|
|601 | hoopskirt, crinoline|
|602 | horizontal bar, high bar|
|603 | horse cart, horse-cart|
|604 | hourglass|
|605 | iPod|
|606 | iron, smoothing iron|
|607 | jack-o'-lantern|
|608 | jean, blue jean, denim|
|609 | jeep, landrover|
|610 | jersey, T-shirt, tee shirt|
|611 | jigsaw puzzle|
|612 | jinrikisha, ricksha, rickshaw|
|613 | joystick|
|614 | kimono|
|615 | knee pad|
|616 | knot|
|617 | lab coat, laboratory coat|
|618 | ladle|
|619 | lampshade, lamp shade|
|620 | laptop, laptop computer|
|621 | lawn mower, mower|
|622 | lens cap, lens cover|
|623 | letter opener, paper knife, paperknife|
|624 | library|
|625 | lifeboat|
|626 | lighter, light, igniter, ignitor|
|627 | limousine, limo|
|628 | liner, ocean liner|
|629 | lipstick, lip rouge|
|630 | Loafer|
|631 | lotion|
|632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
|633 | loupe, jeweler's loupe|
|634 | lumbermill, sawmill|
|635 | magnetic compass|
|636 | mailbag, postbag|
|637 | mailbox, letter box|
|638 | maillot|
|639 | maillot, tank suit|
|640 | manhole cover|
|641 | maraca|
|642 | marimba, xylophone|
|643 | mask|
|644 | matchstick|
|645 | maypole|
|646 | maze, labyrinth|
|647 | measuring cup|
|648 | medicine chest, medicine cabinet|
|649 | megalith, megalithic structure|
|650 | microphone, mike|
|651 | microwave, microwave oven|
|652 | military uniform|
|653 | milk can|
|654 | minibus|
|655 | miniskirt, mini|
|656 | minivan|
|657 | missile|
|658 | mitten|
|659 | mixing bowl|
|660 | mobile home, manufactured home|
|661 | Model T|
|662 | modem|
|663 | monastery|
|664 | monitor|
|665 | moped|
|666 | mortar|
|667 | mortarboard|
|668 | mosque|
|669 | mosquito net|
|670 | motor scooter, scooter|
|671 | mountain bike, all-terrain bike, off-roader|
|672 | mountain tent|
|673 | mouse, computer mouse|
|674 | mousetrap|
|675 | moving van|
|676 | muzzle|
|677 | nail|
|678 | neck brace|
|679 | necklace|
|680 | nipple|
|681 | notebook, notebook computer|
|682 | obelisk|
|683 | oboe, hautboy, hautbois|
|684 | ocarina, sweet potato|
|685 | odometer, hodometer, mileometer, milometer|
|686 | oil filter|
|687 | organ, pipe organ|
|688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
|689 | overskirt|
|690 | oxcart|
|691 | oxygen mask|
|692 | packet|
|693 | paddle, boat paddle|
|694 | paddlewheel, paddle wheel|
|695 | padlock|
|696 | paintbrush|
|697 | pajama, pyjama, pj's, jammies|
|698 | palace|
|699 | panpipe, pandean pipe, syrinx|
|700 | paper towel|
|701 | parachute, chute|
|702 | parallel bars, bars|
|703 | park bench|
|704 | parking meter|
|705 | passenger car, coach, carriage|
|706 | patio, terrace|
|707 | pay-phone, pay-station|
|708 | pedestal, plinth, footstall|
|709 | pencil box, pencil case|
|710 | pencil sharpener|
|711 | perfume, essence|
|712 | Petri dish|
|713 | photocopier|
|714 | pick, plectrum, plectron|
|715 | pickelhaube|
|716 | picket fence, paling|
|717 | pickup, pickup truck|
|718 | pier|
|719 | piggy bank, penny bank|
|720 | pill bottle|
|721 | pillow|
|722 | ping-pong ball|
|723 | pinwheel|
|724 | pirate, pirate ship|
|725 | pitcher, ewer|
|726 | plane, carpenter's plane, woodworking plane|
|727 | planetarium|
|728 | plastic bag|
|729 | plate rack|
|730 | plow, plough|
|731 | plunger, plumber's helper|
|732 | Polaroid camera, Polaroid Land camera|
|733 | pole|
|734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
|735 | poncho|
|736 | pool table, billiard table, snooker table|
|737 | pop bottle, soda bottle|
|738 | pot, flowerpot|
|739 | potter's wheel|
|740 | power drill|
|741 | prayer rug, prayer mat|
|742 | printer|
|743 | prison, prison house|
|744 | projectile, missile|
|745 | projector|
|746 | puck, hockey puck|
|747 | punching bag, punch bag, punching ball, punchball|
|748 | purse|
|749 | quill, quill pen|
|750 | quilt, comforter, comfort, puff|
|751 | racer, race car, racing car|
|752 | racket, racquet|
|753 | radiator|
|754 | radio, wireless|
|755 | radio telescope, radio reflector|
|756 | rain barrel|
|757 | recreational vehicle, RV, R.V.|
|758 | reel|
|759 | reflex camera|
|760 | refrigerator, icebox|
|761 | remote control, remote|
|762 | restaurant, eating house, eating place, eatery|
|763 | revolver, six-gun, six-shooter|
|764 | rifle|
|765 | rocking chair, rocker|
|766 | rotisserie|
|767 | rubber eraser, rubber, pencil eraser|
|768 | rugby ball|
|769 | rule, ruler|
|770 | running shoe|
|771 | safe|
|772 | safety pin|
|773 | saltshaker, salt shaker|
|774 | sandal|
|775 | sarong|
|776 | sax, saxophone|
|777 | scabbard|
|778 | scale, weighing machine|
|779 | school bus|
|780 | schooner|
|781 | scoreboard|
|782 | screen, CRT screen|
|783 | screw|
|784 | screwdriver|
|785 | seat belt, seatbelt|
|786 | sewing machine|
|787 | shield, buckler|
|788 | shoe shop, shoe-shop, shoe store|
|789 | shoji|
|790 | shopping basket|
|791 | shopping cart|
|792 | shovel|
|793 | shower cap|
|794 | shower curtain|
|795 | ski|
|796 | ski mask|
|797 | sleeping bag|
|798 | slide rule, slipstick|
|799 | sliding door|
|800 | slot, one-armed bandit|
|801 | snorkel|
|802 | snowmobile|
|803 | snowplow, snowplough|
|804 | soap dispenser|
|805 | soccer ball|
|806 | sock|
|807 | solar dish, solar collector, solar furnace|
|808 | sombrero|
|809 | soup bowl|
|810 | space bar|
|811 | space heater|
|812 | space shuttle|
|813 | spatula|
|814 | speedboat|
|815 | spider web, spider's web|
|816 | spindle|
|817 | sports car, sport car|
|818 | spotlight, spot|
|819 | stage|
|820 | steam locomotive|
|821 | steel arch bridge|
|822 | steel drum|
|823 | stethoscope|
|824 | stole|
|825 | stone wall|
|826 | stopwatch, stop watch|
|827 | stove|
|828 | strainer|
|829 | streetcar, tram, tramcar, trolley, trolley car|
|830 | stretcher|
|831 | studio couch, day bed|
|832 | stupa, tope|
|833 | submarine, pigboat, sub, U-boat|
|834 | suit, suit of clothes|
|835 | sundial|
|836 | sunglass|
|837 | sunglasses, dark glasses, shades|
|838 | sunscreen, sunblock, sun blocker|
|839 | suspension bridge|
|840 | swab, swob, mop|
|841 | sweatshirt|
|842 | swimming trunks, bathing trunks|
|843 | swing|
|844 | switch, electric switch, electrical switch|
|845 | syringe|
|846 | table lamp|
|847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
|848 | tape player|
|849 | teapot|
|850 | teddy, teddy bear|
|851 | television, television system|
|852 | tennis ball|
|853 | thatch, thatched roof|
|854 | theater curtain, theatre curtain|
|855 | thimble|
|856 | thresher, thrasher, threshing machine|
|857 | throne|
|858 | tile roof|
|859 | toaster|
|860 | tobacco shop, tobacconist shop, tobacconist|
|861 | toilet seat|
|862 | torch|
|863 | totem pole|
|864 | tow truck, tow car, wrecker|
|865 | toyshop|
|866 | tractor|
|867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
|868 | tray|
|869 | trench coat|
|870 | tricycle, trike, velocipede|
|871 | trimaran|
|872 | tripod|
|873 | triumphal arch|
|874 | trolleybus, trolley coach, trackless trolley|
|875 | trombone|
|876 | tub, vat|
|877 | turnstile|
|878 | typewriter keyboard|
|879 | umbrella|
|880 | unicycle, monocycle|
|881 | upright, upright piano|
|882 | vacuum, vacuum cleaner|
|883 | vase|
|884 | vault|
|885 | velvet|
|886 | vending machine|
|887 | vestment|
|888 | viaduct|
|889 | violin, fiddle|
|890 | volleyball|
|891 | waffle iron|
|892 | wall clock|
|893 | wallet, billfold, notecase, pocketbook|
|894 | wardrobe, closet, press|
|895 | warplane, military plane|
|896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
|897 | washer, automatic washer, washing machine|
|898 | water bottle|
|899 | water jug|
|900 | water tower|
|901 | whiskey jug|
|902 | whistle|
|903 | wig|
|904 | window screen|
|905 | window shade|
|906 | Windsor tie|
|907 | wine bottle|
|908 | wing|
|909 | wok|
|910 | wooden spoon|
|911 | wool, woolen, woollen|
|912 | worm fence, snake fence, snake-rail fence, Virginia fence|
|913 | wreck|
|914 | yawl|
|915 | yurt|
|916 | web site, website, internet site, site|
|917 | comic book|
|918 | crossword puzzle, crossword|
|919 | street sign|
|920 | traffic light, traffic signal, stoplight|
|921 | book jacket, dust cover, dust jacket, dust wrapper|
|922 | menu|
|923 | plate|
|924 | guacamole|
|925 | consomme|
|926 | hot pot, hotpot|
|927 | trifle|
|928 | ice cream, icecream|
|929 | ice lolly, lolly, lollipop, popsicle|
|930 | French loaf|
|931 | bagel, beigel|
|932 | pretzel|
|933 | cheeseburger|
|934 | hotdog, hot dog, red hot|
|935 | mashed potato|
|936 | head cabbage|
|937 | broccoli|
|938 | cauliflower|
|939 | zucchini, courgette|
|940 | spaghetti squash|
|941 | acorn squash|
|942 | butternut squash|
|943 | cucumber, cuke|
|944 | artichoke, globe artichoke|
|945 | bell pepper|
|946 | cardoon|
|947 | mushroom|
|948 | Granny Smith|
|949 | strawberry|
|950 | orange|
|951 | lemon|
|952 | fig|
|953 | pineapple, ananas|
|954 | banana|
|955 | jackfruit, jak, jack|
|956 | custard apple|
|957 | pomegranate|
|958 | hay|
|959 | carbonara|
|960 | chocolate sauce, chocolate syrup|
|961 | dough|
|962 | meat loaf, meatloaf|
|963 | pizza, pizza pie|
|964 | potpie|
|965 | burrito|
|966 | red wine|
|967 | espresso|
|968 | cup|
|969 | eggnog|
|970 | alp|
|971 | bubble|
|972 | cliff, drop, drop-off|
|973 | coral reef|
|974 | geyser|
|975 | lakeside, lakeshore|
|976 | promontory, headland, head, foreland|
|977 | sandbar, sand bar|
|978 | seashore, coast, seacoast, sea-coast|
|979 | valley, vale|
|980 | volcano|
|981 | ballplayer, baseball player|
|982 | groom, bridegroom|
|983 | scuba diver|
|984 | rapeseed|
|985 | daisy|
|986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
|987 | corn|
|988 | acorn|
|989 | hip, rose hip, rosehip|
|990 | buckeye, horse chestnut, conker|
|991 | coral fungus|
|992 | agaric|
|993 | gyromitra|
|994 | stinkhorn, carrion fungus|
|995 | earthstar|
|996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
|997 | bolete|
|998 | ear, spike, capitulum|
|999 | toilet tissue, toilet paper, bathroom tissue|
</details>
### Data Splits
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000 |100000 |
## Dataset Creation
### Curation Rationale
The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.
### Source Data
#### Initial Data Collection and Normalization
Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.
#### Who are the source language producers?
WordNet synsets further quality controlled by human annotators. The images are from Flickr.
### Annotations
#### Annotation process
The annotation process of collecting ImageNet for image classification task is a three step process.
1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
1. Collecting the candidate image for these object categories using a search engine.
1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.
See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet.
#### Who are the annotators?
Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details.
### Personal and Sensitive Information
The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet.
## Considerations for Using the Data
### Social Impact of Dataset
The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset.
### Discussion of Biases
1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/)
1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet.
1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet.
### Other Known Limitations
1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848).
## Additional Information
### Dataset Curators
Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):
- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei
### Licensing Information
In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.
### Citation Information
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. | [
"BEAR"
] |
SilasK/mistral-7b-medqa-version3 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-09T15:23:58Z" | 2024-03-09T15:29:54+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: mistral-7b-medqa-version3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-medqa-version3
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1e-08
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
SilasK/llama-7b-medqa_version_3 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-09T15:53:00Z" | 2024-03-09T15:57:58+00:00 | 0 | 0 | ---
base_model: huggyllama/llama-7b
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-medqa_version_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-medqa_version_3
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1e-11
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
SilasK/llama-7b-medqa_version_5 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-11T18:30:44Z" | 2024-03-12T05:21:27+00:00 | 0 | 0 | ---
base_model: huggyllama/llama-7b
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-medqa_version_5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-medqa_version_5
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
SilasK/llama-7b-medqa_version_6 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-12T17:17:57Z" | 2024-03-13T08:31:59+00:00 | 0 | 0 | ---
base_model: huggyllama/llama-7b
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-medqa_version_6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-medqa_version_6
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
ostapeno/library-mistral7B_flan_5ep_sharedA | ostapeno | null | [
"region:us"
] | "2024-03-12T23:16:50Z" | 2024-03-15T17:41:32+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 256
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| true_case | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/true_case | lora |
| cot_strategyqa_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| quarel_do_not_use | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| word_segment | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| stream_aqua_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| duorc_ParaphraseRC_movie_director | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| math_dataset_algebra__linear_1d_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cot_gsm8k_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| social_i_qa_Show_choices_and_generate_index | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dream_generate_last_utterance | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| quail_context_question_description_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| cot_creak_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_creak_ii | lora |
| ropes_background_new_situation_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| cot_esnli | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_esnli | lora |
| anli_r3_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| adversarial_qa_droberta_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| cos_e_v1_11_i_think | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| gem_wiki_lingua_english_en_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| glue_qqp_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| wiki_qa_Topic_Prediction_Answer_Only | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| cot_creak | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_creak | lora |
| trivia_qa_rc_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| duorc_SelfRC_title_generation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| wiqa_what_is_the_final_step_of_the_following_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| glue_mnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| quail_context_question_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| adversarial_qa_dbidaf_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| adversarial_qa_droberta_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| cos_e_v1_11_question_description_option_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| wiki_hop_original_generate_subject_and_object | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| para_crawl_enes | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/para_crawl_enes | lora |
| ropes_background_situation_middle | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| adversarial_qa_dbert_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| ropes_prompt_beginning | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| cot_strategyqa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_strategyqa | lora |
| duorc_ParaphraseRC_question_answering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| adversarial_qa_dbert_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_hop_original_generate_subject | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| race_high_Select_the_best_answer_generate_span_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| cot_gsm8k | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_gsm8k | lora |
| glue_mrpc_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cos_e_v1_11_explain_why_human | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| app_reviews_categorize_rating_using_review | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| adversarial_qa_dbert_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| quail_context_question_answer_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| qasc_qa_with_combined_facts_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| quoref_What_Is_The_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| glue_stsb_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| qasc_qa_with_separated_facts_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| web_questions_short_general_knowledge_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_testing_students | lora |
| cnn_dailymail_3_4_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_complex_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| huggingface_xsum | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/huggingface_xsum | lora |
| cot_sensemaking_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| glue_sst2_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| dbpedia_14_pick_one_category_for_the_following_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| qasc_is_correct_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wiki_qa_Generate_Question_from_Topic | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_combining_facts | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quartz_given_the_fact_answer_the_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| wmt16_translate_ro_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| cot_sensemaking | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_sensemaking | lora |
| wiki_bio_what_content | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| quail_context_question_description_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| quartz_answer_question_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| fix_punct | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/fix_punct | lora |
| qasc_is_correct_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| cos_e_v1_11_question_description_option_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| wmt16_translate_fi_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| wiki_qa_Is_This_True_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| web_questions_question_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| quartz_use_info_from_paragraph_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_choose_between | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_choose_between | lora |
| adversarial_qa_droberta_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| cot_ecqa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_ecqa | lora |
| web_questions_get_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_movie_director | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| dream_answer_to_dialogue | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| ropes_plain_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| cos_e_v1_11_rationale | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| duorc_ParaphraseRC_build_story_around_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| kilt_tasks_hotpotqa_straighforward_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| wiki_hop_original_explain_relation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| natural_questions_open_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| anli_r1_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| duorc_SelfRC_answer_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| wmt16_translate_de_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| duorc_ParaphraseRC_extract_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| unified_qa_science_inst | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| yelp_polarity_reviews_0_2_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| quarel_logic_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| wiki_qa_automatic_system | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| duorc_SelfRC_build_story_around_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| quartz_having_read_above_passage | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| glue_cola_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| wiqa_effect_with_string_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| duorc_ParaphraseRC_answer_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| ag_news_subset_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| dbpedia_14_given_a_choice_of_categories_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| dream_baseline | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_baseline | lora |
| qasc_qa_with_separated_facts_4 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| qasc_qa_with_separated_facts_5 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| duorc_ParaphraseRC_decide_worth_it | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| duorc_SelfRC_question_answering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| definite_pronoun_resolution_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| super_glue_rte_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| stream_qed | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_qed | lora |
| app_reviews_convert_to_rating | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiqa_what_might_be_the_first_step_of_the_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| social_i_qa_Show_choices_and_generate_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| cos_e_v1_11_generate_explanation_given_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| quartz_use_info_from_question_paragraph | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| anli_r2_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| duorc_ParaphraseRC_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quarel_heres_a_story | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| cos_e_v1_11_aligned_with_common_sense | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| duorc_SelfRC_extract_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| race_middle_Select_the_best_answer_no_instructions_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| web_questions_potential_correct_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_qasc | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_qasc | lora |
| adversarial_qa_dbert_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| paws_wiki_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| quail_context_description_question_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| kilt_tasks_hotpotqa_final_exam | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| trec_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_effect_with_label_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| snli_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| cot_ecqa_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| quail_context_question_answer_description_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| gigaword_1_2_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| cos_e_v1_11_question_option_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| glue_qnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| duorc_SelfRC_generate_question_by_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| kilt_tasks_hotpotqa_formulate | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| quartz_paragraph_question_plain_concat | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| adversarial_qa_dbert_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| ropes_prompt_bottom_hint_beginning | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| adversarial_qa_droberta_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| dream_generate_first_utterance | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| duorc_SelfRC_decide_worth_it | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| quail_context_description_question_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| race_high_Is_this_the_right_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| ropes_read_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| glue_wnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| wiki_qa_Direct_Answer_to_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| web_questions_whats_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| quail_no_prompt_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| duorc_ParaphraseRC_generate_question_by_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| cos_e_v1_11_question_option_description_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| super_glue_copa_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| app_reviews_generate_review | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| app_reviews_convert_to_star_rating | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| gem_web_nlg_en_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| quoref_Context_Contains_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| gem_e2e_nlg_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| quoref_Answer_Question_Given_Context | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| duorc_SelfRC_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| race_high_Select_the_best_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| stream_qed_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_qed_ii | lora |
| cos_e_v1_11_description_question_option_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_bio_comprehension | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| duorc_ParaphraseRC_title_generation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_no_prompt_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| adversarial_qa_dbidaf_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| quoref_Found_Context_Online | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| race_middle_Taking_a_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| gem_common_gen_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| race_high_Taking_a_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| gem_dart_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| wiki_bio_guess_person | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| cosmos_qa_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| lambada_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| ropes_given_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| adversarial_qa_droberta_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| adversarial_qa_dbidaf_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| adversarial_qa_dbidaf_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| cot_esnli_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quail_description_context_question_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| wiki_bio_who | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_who | lora |
Last updated on: 2024-03-15 17:41:32+00:00
| [
"SCIQ"
] |
SilasK/llama-7b-medqa_version_7 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-13T12:49:38Z" | 2024-03-13T23:17:13+00:00 | 0 | 0 | ---
base_model: huggyllama/llama-7b
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-medqa_version_7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-medqa_version_7
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
ShivamChadha/whisper-medical-data | ShivamChadha | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-small.en",
"base_model:adapter:openai/whisper-small.en",
"license:apache-2.0",
"region:us"
] | "2024-03-14T16:45:57Z" | 2024-03-14T16:45:59+00:00 | 0 | 0 | ---
base_model: openai/whisper-small.en
library_name: peft
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-medical-data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medical-data
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5610
- Wer: 29.2608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.2874 | 16.67 | 100 | 1.5610 | 29.2608 |
### Framework versions
- PEFT 0.9.1.dev0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"MEDICAL DATA"
] |
fakufaku/diffsep | fakufaku | null | [
"license:mit",
"region:us"
] | "2024-03-15T07:29:07Z" | 2024-03-15T07:37:59+00:00 | 0 | 0 | ---
license: mit
---
Diffusion-based Generative Speech Source Separation
This repository contains the checkpoints for the diffusion based speech
separation model from the paper Diffusion-based Generative Speech Source
Separation presented at ICASSP 2023.
The code to run the model is available on [github](https://github.com/fakufaku/diffusion-separation).
### Abstract
We propose DiffSep, a new single channel source separation method based on
score-matching of a stochastic differential equation (SDE). We craft a tailored
continuous time diffusion-mixing process starting from the separated sources
and converging to a Gaussian distribution centered on their mixture. This
formulation lets us apply the machinery of score-based generative modelling.
First, we train a neural network to approximate the score function of the
marginal probabilities or the diffusion-mixing process. Then, we use it to
solve the reverse time SDE that progressively separates the sources starting
from their mixture. We propose a modified training strategy to handle model
mismatch and source permutation ambiguity. Experiments on the WSJ0 2mix dataset
demonstrate the potential of the method. Furthermore, the method is also
suitable for speech enhancement and shows performance competitive with prior
work on the VoiceBank-DEMAND dataset.
ID: `2022-10-23_01-37-07_experiment-model-large-multigpu_model.optimizer.lr-0.0002_model.sde.d_lambda-2.0_model.sde.sigma_min-0.05_epoch-979_si_sdr-11.271_N-30_snr-0.5_corrstep-1_denoise-True_schedule-None`
| [
"CRAFT"
] |
SilasK/llama-7b-medqa_version_9 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-15T09:55:40Z" | 2024-03-17T01:17:49+00:00 | 0 | 0 | ---
base_model: huggyllama/llama-7b
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-medqa_version_9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-medqa_version_9
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.2
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
noguchis/viewdiff_train_teddybear | noguchis | null | [
"diffusers",
"safetensors",
"license:cc-by-nc-sa-4.0",
"diffusers:CustomStableDiffusionPipeline",
"region:us"
] | "2024-03-18T10:11:31Z" | 2024-03-18T11:49:40+00:00 | 0 | 3 | ---
license: cc-by-nc-sa-4.0
---
This model I have uploaded is checkpoint 1061000 for Teddy Bear (CO3D) trained using [ViewDiff](https://github.com/facebookresearch/ViewDiff).
Please see my article for details --> [Trying ViewDiff with WSL2...but](https://note.com/ngc_shj/n/n50f96770c0bf) | [
"BEAR"
] |
mpetitguillaume/cryptoGPT-1.0-7B-lt | mpetitguillaume | text-generation | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | "2024-03-19T08:55:39Z" | 2024-03-20T16:49:46+00:00 | 0 | 3 | ---
{}
---
# CryptoGPT-1.0-7B-lt - Sentiment analysis model for financial news dedicated to crypto-assets
This project introduces our lightest AI model designed for real-time market sentiment analysis on financial news dedicated to cryptoassets. Leveraging the latest advances in natural language processing (NLP), our model classifies financial texts into specific classes and evaluates their sentiment, providing invaluable insights into market trends.
### 1. Background and Problem Statement
The cryptoasset market is known for its volatility, driven by various factors including statements by influential figures, political decisions, rumors, etc. Traditional financial models often fail to accurately interpret the impact of such events. In response to this challenge and inspired by the introduction of BloombergGPT, our project aims to democratize access to cutting-edge NLP for financial analysis dedicated to the crypto-asset market. Our research focused on developing a more accessible Large Language Model (LLM) capable of analyzing tweets, recent news, and market trends with limited resources.
### 2. Annotation Methodology
##### 2.1. Annotation Objective
Our methodology is built around the annotation of financial texts into 21 financial classes specific to crypto-assets, and having a significant impact on this market, such as “Regulation and legislation”, “Market sentiment”, “ESG impact”, etc. The objective is twofold: to enable very precise categorization and to provide a comprehensive analysis of the sentiment reflected by the technical nuances of financial markets, particularly in the area of crypto-assets.
##### 2.2. Annotation Method
We have used one of our automatic annotation technologies as part of our annotation and categorization process to ensure greater reliability compared to human annotation. Our input dataset of more than 15 million tokens has allowed us to develop high-performance market sentiment analysis models dedicated to crypto-assets. The cryptoGPT-1.0-7B-lt model that we present to you in this repository is fine-tuned on a much smaller input dataset of around 3.3 million tokens.
#### 3. Fine-Tuning Strategy
Our fine-tuning process uses broad language models with QLoRA for efficient adaptation. We have optimized the training phase of our models to run on a small infrastructure, ensuring significant resource and time savings without compromising model performance.
### 4. Installation
To set up the environment for our model, follow these steps:
```bash
!pip install --upgrade pip
!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git
!pip install -q huggingface_hub
```
Alternatively, you can create your requiremnts.txt and install all required packages like below:
```python
bitsandbytes
git+https://github.com/huggingface/transformers.git
git+https://github.com/huggingface/peft.git
git+https://github.com/huggingface/accelerate.git
huggingface_hub
tokenizers==0.15.2
```
```bash
!pip install --upgrade pip
!pip install -r requirements.txt
```
### 5. Usage
The model can be used to analyze financial texts ideally dedicated to the crypto-asset market, thereby providing accurate technical analysis of sentiment in various categories relevant to the crypto-asset market.
### 6. Python Example Code
Here is a simple example of Python code to illustrate a basic use of the model for sentiment analysis:
```python
import torch
from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from huggingface_hub import login
import gc
gc.collect()
torch.cuda.empty_cache()
HF_TOKEN = "HF_TOKEN"
login(HF_TOKEN)
MODEL_NAME = "mpetitguillaume/cryptoGPT-1.0-7B-lt"
def setup_device():
"""Configures and returns the primary device for model computations (GPU if available)."""
return torch.device("cuda")
def login_to_hf_hub(token):
"""Authenticates with the Hugging Face Hub using a provided token."""
login(token=token)
def load_model_and_tokenizer(model_name, bnb_config):
"""
Loads the specified model and tokenizer from Hugging Face, applying quantization
configurations if provided. Also sets the tokenizer's pad token to its eos token.
Args:
model_name (str): Name of the model to load.
bnb_config (BitsAndBytesConfig): Configuration for model quantization.
Returns:
tuple: The loaded model and tokenizer.
"""
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token # Harmonize pad and eos tokens
return model, tokenizer
def create_bnb_config():
"""Creates a BitsAndBytes configuration optimized for model performance."""
return BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
def create_prompt_formats(news):
"""
Creates a formatted prompt template for a prompt in the instruction dataset
:param sample: Prompt or sample from the instruction dataset
"""
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
INSTRUCTION_KEY = "### Instruction:"
INPUT_KEY = "### Input:"
RESPONSE_KEY = "### Response:"
prompt = """You need to relate news to some of the following 21 categories, provide a brief explanation, conduct sentiment analysis within each category, and offer an overall sentiment analysis, especially focusing on the financial markets, including cryptocurrencies. Determine whether it has a strongly positive, moderately positive, strongly negative, moderately negative, or negligible impact on market trends.
Categories:
1. Regulation and Legislation (keywords: government, SEC, regulator, law, regulation, legislation.)
2. Adoption and Usage (keywords: adoption, usage, business, institution, partnership.)
3. Geopolitical Events (keywords: geopolitics, conflict, election, economic policy.)
4. Technology and Infrastructure (keywords: technology, infrastructure, updates, protocols, security.)
5. Financial Market Performances (keywords: stock market, bond market, currencies, indices.)
6. Market Sentiment (keywords: sentiment, confidence, opinion, investors.)
7. Competition Between Cryptocurrencies (keywords: competition, fork, updates, new projects, cryptocurrencies.)
8. Partnerships and Collaborations (keywords: partnership, collaboration, business, institution.)
9. Initial Coin Offerings (keywords: ICO, token sales, fundraising, crowdfunding.)
10. Media Coverage (keywords: media, media coverage, reporting, articles, news.)
11. Exchange Listings (keywords: exchange platforms, listing, liquidity.)
12. Exchange Delistings (keywords: exchange platforms, delisting, liquidity.)
13. Exchange Volume and Liquidity (keywords: volume, liquidity, exchange, trading.)
14. Market Manipulation and Fraud (keywords: manipulation, fraud, deception, investigation.)
15. Influential Players' Interventions (keywords: influence, statements, personalities, entrepreneurs, analysts.)
16. Expert Analysis and Forecasts (keywords: analysis, forecasts, experts, projections, predictions.)
17. Integration with Financial Services (keywords: integration, financial services, banking, payments.)
18. Macroeconomic Indicators (keywords: macroeconomics, inflation, interest rates, economic growth.)
19. Cryptocurrency Events and Conferences (keywords: events, conferences, summits, forums, exhibitions related to cryptocurrencies.)
20. Rumors and Speculations (keywords: rumors, speculations, buzz, leaks, unconfirmed information.)
21. Impact ESG (ESG Impact) (keywords: environment, social, governance, sustainability, responsibility, ethics, impact, carbon footprint, energy consumption, mining, electronic waste, working conditions, transparency, corporate governance, diversity, inclusion, human rights, climate change.)
If you don't know the category, response "OTHERS"."""
blurb = f"{INTRO_BLURB}"
instruction = f"{INSTRUCTION_KEY}\n{prompt}"
input_context = f"{INPUT_KEY}\n{news}" if news else None
response = f"{RESPONSE_KEY}\n"
parts = [part for part in [blurb, instruction, input_context, response] if part]
formatted_prompt = "\n\n".join(parts)
return formatted_prompt
def generate_response(model, tokenizer, news):
"""
Generates a text response for a given input news snippet using the model and tokenizer.
Args:
model (AutoModelForCausalLM): The model for generating responses.
tokenizer (AutoTokenizer): The tokenizer for processing input and output texts.
news (str): The news snippet to respond to.
Returns:
str: The generated text response.
"""
generation_config = GenerationConfig(
max_new_tokens=300,
do_sample=True,
top_p=0.1,
temperature=0.01,
pad_token_id=tokenizer.eos_token_id,
)
input_tensor = tokenizer(create_prompt_formats(news), return_tensors="pt", truncation=True)
device = setup_device()
with torch.inference_mode():
outputs = model.generate(
input_ids=input_tensor["input_ids"].to("cuda"),
attention_mask=input_tensor["attention_mask"],
generation_config=generation_config,
)
result = tokenizer.batch_decode(
outputs.detach().cpu().numpy(), skip_special_tokens=True
)
return result[0].split('### Response:')[1].split('###')[0]
bnb_config = create_bnb_config()
model, tokenizer = load_model_and_tokenizer(MODEL_NAME, bnb_config)
```
Here is an example output for this code:
```bash
0. Financial Market Performances (mention of Jinzhou Bank's financial situation)
Neutral - The article suggests that Jinzhou Bank's financial situation was in good shape, with low bad-debt level and only a small percentage of personal-business loans having gone sour.
1. Market Manipulation and Fraud (mention of the potential involvement of a billionaire, Li Hejun, in the bank's problems)
Negative - The article suggests that the billionaire, Li Hejun, may be behind the bank's distress, indicating potential market manipulation or fraud.
2. Geopolitical Events (mention of China's banks and its economic policies)
Neutral - The article discusses China's banks and their financial situation, but it doesn't provide any clear geopolitical analysis or opinion.
Sentiment Analysis regarding the Cryptocurrency Market:
Somewhat Negative Impact on the Market: The article suggests that Jinzhou Bank's financial situation may be in trouble, and the billionaire Li Hejun might be involved. This could lead to rumors and speculations about the stability of China's banks and the economy. However, the article doesn't provide any clear sentiment analysis or opinion on the matter...
```
### 7. Evaluation Results
Our model evaluation was based on manual expert evaluation. As part of the evaluation of this very lightweight model, we selected a set of 50 financial articles dedicated to crypto-assets, representative of various categories, rich in content and representative of several market trends. Six models were tested: refined LLaMa-2-7B, refined Mistral-7B, LLaMa-2-7B, LLaMa-2-13B, Mistral-7B, GPT-3.5 Turbo.
Each model was rated on a scale of 0 to 4, where 4 indicates optimal performance and 0 means unusable results. According to the data our model based on fine-tuned Mistral-7B showed superior performance to GPT-3.5.
Our tests on our larger models demonstrated performance well above GPT-4 and the largest known wide language models.
| Models | GPT-3.5 | **CryptoGPT-1.0-7B-lt** | Mistral-7B | LLaMa-2-7B | LLaMa-2-13B |
|--- |:-: |:-: |:-: |--: |--: |
| Average score | 2.9 | **3.12** | 0.48 | 0.38 | 0.68 |
| Score 4 | 14 | **15** | 0 | 0 | 0 |
| Score 4 & 3 | 35 | **41** | 0 | 0 | 0 |
### 8. Reporting Issues
Please report any software "bug," or other problems with the models through one of the following means:
- Reporting issues with the model: [[email protected]](mailto:[email protected])
### 9. License
This project is licensed under the MIT License - see the LICENSE file for details.
### 10. Contact
For any questions or to contribute to the project, please contact us at [[email protected]](mailto:[email protected]).
| [
"BLURB"
] |
SilasK/mistral-7b-medqa-version4 | SilasK | null | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | "2024-03-19T10:54:21Z" | 2024-03-19T16:21:01+00:00 | 0 | 0 | ---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: mistral-7b-medqa-version4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-medqa-version4
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 20
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"MEDQA"
] |
WeMake/V41 | WeMake | any-to-any | [
"not-for-all-audiences",
"any-to-any",
"en",
"dataset:WeMake/Intelligent-Content-Understanding",
"region:us"
] | "2024-03-20T03:42:16Z" | 2025-03-01T14:51:14+00:00 | 0 | 1 | ---
datasets:
- WeMake/Intelligent-Content-Understanding
language:
- en
pipeline_tag: any-to-any
tags:
- not-for-all-audiences
---
# V41
**V41** is WeMake's flagship multimodal AI model, designed to bridge the gap between different data formats and modalities. As an any-to-any model, V41 excels at tasks requiring the transformation, understanding, and generation of content across various formats including text, images, audio, and structured data.
Built on WeMake's proprietary Intelligent Content Understanding (ICU) methodology, V41 represents a significant leap forward in creating AI that truly understands context, emotional nuances, and complex relationships between different types of information.
## Key Features
- **Multimodal Versatility:** Seamlessly processes and generates across text, image, audio, and structured data formats
- **Emotional Intelligence:** Incorporates advanced emotional understanding capabilities, enabling more human-like interactions
- **Context-Aware Processing:** Maintains context across different modalities and extended interactions
- **Ethical Alignment:** Designed with WeMake's commitment to ethical AI, prioritizing human values and well-being
- **Adaptive Learning:** Continuously improves through our AI-Human Alignment Process
- **Enterprise-Grade Security:** Implements robust security measures in compliance with European data protection standards
## Applications
V41 powers a wide range of applications across industries:
- **Productivity Enhancement:** Core of the V41 Platform with Clarity and Val Intelligence Workers
- **Content Creation:** Generates cohesive multi-format content for marketing, education, and entertainment
- **Data Analysis:** Transforms complex datasets into accessible visualizations and insights
- **Customer Experience:** Powers conversational interfaces that understand emotional context and user needs
- **Healthcare Support:** Assists in medical data interpretation and patient communication
- **Educational Tools:** Creates interactive learning experiences across multiple formats
## Technical Architecture
V41 is built on a transformer-based architecture with specialized encoders and decoders for each supported modality. The model features:
- **Advanced Cross-Attention Mechanisms:** Enabling seamless information flow between modalities
- **Contextual Memory System:** Maintaining coherence across extended interactions
- **Ethical Guardrails:** Built-in parameters to ensure outputs align with WeMake's ethical principles
- **Optimized Performance:** Balancing computational efficiency with high-quality outputs
## Model Variants
V41 comes in several specialized variants:
- **V41 Core:** The foundation model supporting all modalities
- **Llama-3-8B-V41-Instruct-1048k:** Text-optimized version with 1M token context length
- **VX-Unholy-13B:** Specialized creative variant built on Unholy-v2-13B
- **V41-Vision:** Image-focused variant with enhanced visual understanding
## Responsible Use
V41 is designed for beneficial applications that enhance human capabilities while respecting privacy and ethical boundaries. Users are expected to adhere to WeMake's ethics policy and applicable regulations when deploying V41-based applications.
## Training Methodology
V41 was trained using WeMake's proprietary ICU (Intelligent Content Understanding) dataset and methodology, which focuses on building an internal knowledge map that enables the model to make connections between concepts across different modalities.
This training approach emphasizes:
- **Deep Contextual Understanding:** Beyond surface-level pattern recognition
- **Ethical Considerations:** Minimizing harmful biases and promoting inclusive outputs
- **Emotional Intelligence:** Recognizing and appropriately responding to emotional cues
- **Human-AI Alignment:** Ensuring the model's outputs align with human values and expectations
## License
V41 is released under a custom license that allows for research and commercial applications while prohibiting harmful or unethical use cases. Please contact WeMake for specific licensing details.
## Acknowledgments
V41 builds upon the collective efforts of the AI research community while incorporating WeMake's innovations in emotional intelligence and ethical AI development. We extend our gratitude to all contributors to the field whose work has made V41 possible.
## Contact
For inquiries about V41, please contact us at [[email protected]](mailto:[email protected]).
Join us in building a future where AI amplifies human potential while respecting our values and well-being.
💙 **The WeMake Team**
[🤝 Ethics Policy](https://wemake.cx/legal/ethics/) [🛡️ Privacy Policy](https://wemake.cx/legal/privacy) [📇 Imprint](https://wemake.cx/legal/imprint)
| [
"MEDICAL DATA"
] |
ostapeno/library-stablelm_flan_5ep_part2 | ostapeno | null | [
"region:us"
] | "2024-03-20T15:19:52Z" | 2024-03-22T21:23:53+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 156
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| true_case | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/true_case | lora |
| quarel_do_not_use | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| word_segment | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| stream_aqua_ii | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| social_i_qa_Show_choices_and_generate_index | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| quail_context_question_description_answer_id | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| ropes_background_new_situation_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| wiki_bio_who | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_bio_who | lora |
| gem_wiki_lingua_english_en_1_1_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| wiki_qa_Topic_Prediction_Answer_Only | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| trivia_qa_rc_1_1_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| wiqa_what_is_the_final_step_of_the_following_process | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| quail_context_question_description_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| wiki_hop_original_generate_subject_and_object | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| ropes_background_situation_middle | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| race_high_Select_the_best_answer_generate_span_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| race_high_Write_a_multi_choice_question_options_given_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| quail_context_question_answer_description_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| quoref_What_Is_The_Answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| web_questions_short_general_knowledge_q | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quarel_testing_students | lora |
| cnn_dailymail_3_4_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| huggingface_xsum | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/huggingface_xsum | lora |
| wiki_qa_Generate_Question_from_Topic | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| quartz_given_the_fact_answer_the_q | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| wmt16_translate_ro_en_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| wiki_bio_what_content | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| quail_context_question_description_answer_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| quartz_answer_question_based_on | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| wmt16_translate_fi_en_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| wiki_qa_Is_This_True_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| web_questions_question_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| quartz_use_info_from_paragraph_question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_choose_between | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quarel_choose_between | lora |
| web_questions_get_the_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| dream_answer_to_dialogue | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| ropes_plain_background_situation | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| duorc_ParaphraseRC_build_story_around_qa | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| wiki_hop_original_explain_relation | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| quoref_Given_Context_Answer_Question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| wmt16_translate_de_en_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| unified_qa_science_inst | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| yelp_polarity_reviews_0_2_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| quarel_logic_test | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| wiki_qa_automatic_system | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| duorc_SelfRC_build_story_around_qa | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| quartz_having_read_above_passage | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| wiqa_effect_with_string_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| qasc_qa_with_separated_facts_4 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| qasc_qa_with_separated_facts_5 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| super_glue_rte_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_what_might_be_the_first_step_of_the_process | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| social_i_qa_Show_choices_and_generate_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| quartz_use_info_from_question_paragraph | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| quarel_heres_a_story | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| race_middle_Select_the_best_answer_no_instructions_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| web_questions_potential_correct_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| quail_context_description_question_answer_id | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| trec_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_effect_with_label_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| snli_1_1_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| quail_context_question_answer_description_id | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| quartz_paragraph_question_plain_concat | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| ropes_prompt_bottom_hint_beginning | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| quail_context_description_question_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| race_high_Is_this_the_right_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| ropes_read_background_situation | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| wiki_qa_Direct_Answer_to_Question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| web_questions_whats_the_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| quail_no_prompt_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| super_glue_copa_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quoref_Context_Contains_Answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| quoref_Answer_Question_Given_Context | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| race_high_Select_the_best_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| stream_qed_ii | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/stream_qed_ii | lora |
| wiki_bio_comprehension | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| quail_no_prompt_id | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| quoref_Found_Context_Online | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| race_middle_Taking_a_test | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| race_high_Taking_a_test | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| ropes_given_background_situation | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| quail_description_context_question_answer_text | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| stream_qed | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/stream_qed | lora |
Last updated on: 2024-03-22 21:23:52+00:00
| [
"SCIQ"
] |
ostapeno/library-mistral7B_flan_5ep_higher_lr | ostapeno | null | [
"region:us"
] | "2024-03-23T19:08:25Z" | 2024-04-05T18:13:32+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 256
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| race_middle_Is_this_the_right_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| true_case | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/true_case | lora |
| cot_strategyqa_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| quarel_do_not_use | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| word_segment | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/word_segment | lora |
| sciq_Multiple_Choice_Question_First | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| wiki_qa_Decide_good_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| stream_aqua_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| sciq_Direct_Question_Closed_Book_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| social_i_qa_Generate_the_question_from_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| duorc_ParaphraseRC_movie_director | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| math_dataset_algebra__linear_1d_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cot_gsm8k_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| social_i_qa_Show_choices_and_generate_index | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| wiqa_what_is_the_missing_first_step | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| dream_generate_last_utterance | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| quail_context_question_description_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| cot_creak_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_creak_ii | lora |
| ropes_background_new_situation_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| quoref_Guess_Title_For_Context | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| imdb_reviews_plain_text_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| cot_esnli | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_esnli | lora |
| anli_r3_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| adversarial_qa_droberta_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| wiki_bio_who | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_who | lora |
| cos_e_v1_11_i_think | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| gem_wiki_lingua_english_en_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| ropes_prompt_bottom_no_hint | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| glue_qqp_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_description_question_option_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| wiki_qa_Topic_Prediction_Answer_Only | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| cot_creak | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_creak | lora |
| trivia_qa_rc_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| duorc_SelfRC_title_generation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| wiqa_what_is_the_final_step_of_the_following_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| glue_mnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| quail_context_question_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| quoref_Guess_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| adversarial_qa_dbidaf_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| adversarial_qa_droberta_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| cos_e_v1_11_question_description_option_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| wiki_hop_original_generate_subject_and_object | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| para_crawl_enes | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/para_crawl_enes | lora |
| ropes_background_situation_middle | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| adversarial_qa_dbert_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| ropes_prompt_beginning | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| cot_strategyqa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_strategyqa | lora |
| duorc_ParaphraseRC_question_answering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| adversarial_qa_dbert_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| wiki_hop_original_generate_subject | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| race_high_Select_the_best_answer_generate_span_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| cot_gsm8k | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_gsm8k | lora |
| glue_mrpc_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| cos_e_v1_11_explain_why_human | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| app_reviews_categorize_rating_using_review | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| adversarial_qa_dbert_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| quail_context_question_answer_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| qasc_qa_with_combined_facts_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| quoref_What_Is_The_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| quail_context_description_question_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| glue_stsb_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| qasc_qa_with_separated_facts_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| web_questions_short_general_knowledge_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| quarel_testing_students | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_testing_students | lora |
| cnn_dailymail_3_4_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| wiki_bio_key_content | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| sciq_Multiple_Choice | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_complex_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| huggingface_xsum | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/huggingface_xsum | lora |
| cot_sensemaking_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| glue_sst2_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| dbpedia_14_pick_one_category_for_the_following_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| qasc_is_correct_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wiki_qa_Generate_Question_from_Topic | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_combining_facts | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| quartz_given_the_fact_answer_the_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| wmt16_translate_ro_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| cot_sensemaking | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_sensemaking | lora |
| wiki_bio_what_content | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| sciq_Direct_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| quail_context_question_description_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| ropes_prompt_mix | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| quartz_answer_question_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| fix_punct | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/fix_punct | lora |
| qasc_is_correct_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| cos_e_v1_11_question_description_option_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| wmt16_translate_fi_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| super_glue_multirc_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| wiki_qa_Is_This_True_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| multi_news_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| web_questions_question_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| quartz_use_info_from_paragraph_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wmt14_translate_fr_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| quarel_choose_between | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_choose_between | lora |
| adversarial_qa_droberta_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| cot_ecqa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_ecqa | lora |
| web_questions_get_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_movie_director | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| dream_answer_to_dialogue | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| ropes_plain_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| cos_e_v1_11_rationale | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| duorc_ParaphraseRC_build_story_around_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| social_i_qa_I_was_wondering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_qa_exercise | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| kilt_tasks_hotpotqa_straighforward_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| wiki_hop_original_explain_relation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| natural_questions_open_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| anli_r1_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| quoref_Given_Context_Answer_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| duorc_SelfRC_answer_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| wmt16_translate_de_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| wiki_hop_original_generate_object | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| duorc_ParaphraseRC_extract_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| unified_qa_science_inst | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quail_description_context_question_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| quarel_logic_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_logic_test | lora |
| squad_v1_1_3_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| wiki_qa_Jeopardy_style | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| wiki_qa_automatic_system | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| ropes_new_situation_background_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| super_glue_wsc_fixed_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| wmt16_translate_tr_en_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| duorc_SelfRC_build_story_around_qa | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| quoref_Answer_Friend_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| quartz_having_read_above_passage | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| glue_cola_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| wiqa_effect_with_string_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| duorc_ParaphraseRC_answer_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| ag_news_subset_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| dbpedia_14_given_a_choice_of_categories_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| dream_baseline | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_baseline | lora |
| qasc_qa_with_separated_facts_4 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| quartz_read_passage_below_choose | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| ropes_plain_no_background | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| qasc_qa_with_separated_facts_5 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| quoref_Read_And_Extract_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| duorc_ParaphraseRC_decide_worth_it | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| duorc_SelfRC_question_answering | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| definite_pronoun_resolution_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| super_glue_rte_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| social_i_qa_Generate_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| stream_qed | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_qed | lora |
| app_reviews_convert_to_rating | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiqa_what_might_be_the_first_step_of_the_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| social_i_qa_Show_choices_and_generate_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| cos_e_v1_11_generate_explanation_given_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| quartz_use_info_from_question_paragraph | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| anli_r2_0_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| duorc_ParaphraseRC_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| quarel_heres_a_story | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| cos_e_v1_11_aligned_with_common_sense | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| duorc_SelfRC_extract_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| race_middle_Select_the_best_answer_no_instructions_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| web_questions_potential_correct_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| qasc_qa_with_separated_facts_3 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_qasc | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_qasc | lora |
| adversarial_qa_dbert_tell_what_it_is | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| paws_wiki_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| quail_context_description_question_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| coqa_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| kilt_tasks_hotpotqa_final_exam | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| trec_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| super_glue_cb_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| qasc_qa_with_separated_facts_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| wiqa_effect_with_label_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| snli_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| cot_ecqa_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| quail_context_question_answer_description_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| gigaword_1_2_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| cos_e_v1_11_question_option_description_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| glue_qnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| duorc_SelfRC_generate_question_by_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| kilt_tasks_hotpotqa_formulate | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| quartz_paragraph_question_plain_concat | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| adversarial_qa_dbert_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| ropes_prompt_bottom_hint_beginning | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| adversarial_qa_droberta_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| dream_generate_first_utterance | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| duorc_SelfRC_decide_worth_it | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| quail_context_description_question_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| race_high_Is_this_the_right_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| drop_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| ropes_read_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| glue_wnli_2_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| wiki_qa_Direct_Answer_to_Question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| web_questions_whats_the_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiki_qa_found_on_google | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| quail_no_prompt_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| duorc_ParaphraseRC_generate_question_by_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| cos_e_v1_11_question_option_description_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| super_glue_copa_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| app_reviews_generate_review | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| app_reviews_convert_to_star_rating | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| gem_web_nlg_en_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| quoref_Context_Contains_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| gem_e2e_nlg_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| quoref_Answer_Question_Given_Context | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quartz_answer_question_below | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| duorc_SelfRC_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| race_high_Select_the_best_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| stream_qed_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_qed_ii | lora |
| cos_e_v1_11_description_question_option_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| wiki_bio_comprehension | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| duorc_ParaphraseRC_title_generation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_no_prompt_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| adversarial_qa_dbidaf_answer_the_following_q | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| quoref_Found_Context_Online | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| race_middle_Select_the_best_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| race_middle_Taking_a_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| quoref_Answer_Test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| gem_common_gen_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| race_high_Taking_a_test | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| quail_description_context_question_answer_id | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| gem_dart_1_1_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| wiki_bio_guess_person | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| race_high_Select_the_best_answer_no_instructions_ | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| quac_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| cosmos_qa_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| dream_read_the_following_conversation_and_answer_the_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| wiki_qa_Topic_Prediction_Question_Only | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| lambada_1_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| ropes_given_background_situation | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| adversarial_qa_droberta_generate_question | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| adversarial_qa_dbidaf_question_context_answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| adversarial_qa_dbidaf_based_on | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| super_glue_wic_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| ropes_plain_bottom_hint | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| quoref_Find_Answer | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| cot_esnli_ii | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quail_description_context_question_answer_text | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| squad_v2_0_3_0_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| stream_aqua | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/stream_aqua | lora |
| super_glue_record_1_0_2 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| yelp_polarity_reviews_0_2_0 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
Last updated on: 2024-03-25 21:05:58+00:00
| [
"SCIQ"
] |
ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity | ginkgogo | text-classification | [
"setfit",
"safetensors",
"mpnet",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"model-index",
"region:us"
] | "2024-03-23T23:19:03Z" | 2024-03-24T23:47:26+00:00 | 0 | 0 | ---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: ordered, great atmosphere, excellent service:FINALLY tried Mizza and wasn't
disappointed. Loved (almost) everything we ordered, great atmosphere, excellent
service, and the perfect setting for a lovely bday Sunday. The burrata & heirloom
tomatoes app was scrumptious, the salmon pasta, very flavorful and the salmon
perfectly cooked, I liked the toppings of the veggie pizza but wasn't a super
fan of the crust (doesn't mean I won't come back and try another pizza on their
menu ) and the cannoli was good although that dessert in general isn't my fave
(it was my bf's bday so had to get what he wanted ). The flourless chocolate cake
and limoncello cake are what I'll try next time. Had a great time and will be
back. Gave it 4 stars just cuz I wasn't that excited about the pizza and that's
something they're supposed to so well. Would recommend the restaurant though!
- text: "is because the food was decent,:Three reasons why it gets three stars:\n\n\
1. The crab cakes were good and is a definitely must try!\n2. The shrimp scampi\
\ was actually amazing in the sauce that it comes with, so that's another must\
\ try!\n3. The real reason why it is getting three stars is because service is\
\ everything in ANY restaurant you go to. Service started off great, waitress\
\ was attentive, but once we paid the bill and left a 20% tip, my guests and I,\
\ which was only three of us, stayed at the table to finish our drinks and we're\
\ looking at funny videos from a trip we went to. Point is the waitress rudely\
\ told my friend to lower the volume on his phone, yet other guests were just\
\ as loud and we were sitting OUTSIDE...where it is already a loud environment!\
\ \n\nI really want to give it 4 stars, but if I give 4 stars it changes it to,\
\ \"Yay! I'm a fan\", but I am not. The only reason why it's not getting 1 star,\
\ is because the food was decent, the view is nice and also the manager was extremely\
\ empathetic to the situation and it wasn't her fault at all that her waitress\
\ was obviously having an off day. I have never met a manager that attentive and\
\ she was incredible at handling and diffusing the situation. I cannot thank her\
\ enough for salvaging the rest of our evening for how poor the waitress treated\
\ paying customers."
- text: and the perfect setting for a lovely:FINALLY tried Mizza and wasn't disappointed.
Loved (almost) everything we ordered, great atmosphere, excellent service, and
the perfect setting for a lovely bday Sunday. The burrata & heirloom tomatoes
app was scrumptious, the salmon pasta, very flavorful and the salmon perfectly
cooked, I liked the toppings of the veggie pizza but wasn't a super fan of the
crust (doesn't mean I won't come back and try another pizza on their menu ) and
the cannoli was good although that dessert in general isn't my fave (it was my
bf's bday so had to get what he wanted ). The flourless chocolate cake and limoncello
cake are what I'll try next time. Had a great time and will be back. Gave it 4
stars just cuz I wasn't that excited about the pizza and that's something they're
supposed to so well. Would recommend the restaurant though!
- text: ) and the service is friendly and:I'm not sure what what I would do if I'd
never discovered Nikka, since it's the definitely the most authentic ramen one
can get in the area. Prices are standard for ramen (especially in SB) and the
service is friendly and efficient. Not only is Nikka's ramen amazing, their variety
of appetizers is also great. I've yet to try one that I don't like. Definitely
come here if you're looking to satisfy your ramen craving!
- text: Overall an excellent experience and the friendly:I got a to-go order for empanadas
on the lunch menu and it was fantastic. The dish was incredibly flavorful and
the Kombucha the owner recommended was amazing. Overall an excellent experience
and the friendly owner, waiters, and waitresses are just the cherry on top. I
would highly recommend any vegetarians to try out this spot!
inference: false
model-index:
- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.65
name: Accuracy
---
# SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_sm
- **SetFitABSA Aspect Model:** [ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect)
- **SetFitABSA Polarity Model:** [ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity](https://huggingface.co/ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'into more American food, added burgers:They made it into more American food, added burgers and ribs and got rid of the tequila selection. We were so bummed. Used to be one of our favorite places to go for good Mexican food. The owner said the new direction was to appeal to more tourists.'</li></ul> |
| positive | <ul><li>"was great and seating was comfortable.:Such a cute little spot for desserts! I'm so glad we had time on our short visit to Santa Barbara to grab a slice of cake from here. My husband and I each got our own to slice to share of course. He said we didn't come all this way just to get one so we chose a slice of the berry cake and chocolate decadence. The berry cake was nice and fluffy without being too sweet. The acidity from the fruits balanced the sweetest of the cake wonderfully. If you're up for something rich then the chocolate decadence will not disappoint. Service was great and seating was comfortable. Order your sweet treats at the counter then a number will be given to you. Pick a table and get ready to enjoy because your sweets will be brought out to your table when ready."</li><li>'Soon, our food was delivered,:One brisk Saturday morning after asking workers during a stop for tylenol from the Hotel California Boutique the best breakfast place, they recommended Goat Tree. We crossed the busy street and greeted the hostess. The very kind young lady walked us to our table on the sunny patio. We skimmed the menu and decided on the chicken and waffle and a chocolate croissant. The wait was quite short and we spent it discussing the beautiful surrounding area. Soon, our food was delivered, and let me tell you, it was beautiful. On top of that, it was scrumptious. The fried chicken was perfect and tender. The waffle had the perfect balance of crunch and fluff. And how dare I forget the exquisite honey. Now this honey was the best I have ever tasted. It was topped with chia and pumpkin seeds. My daughter asked for her croissant warmed, and once again it was marvelous. After paying, I told our waitress how amazing the honey was. Next thing we knew, she brought out two large to go cups full of it! \n\nAbsolutely loved this place and everything about it. 100% recommend! I strongly award them 5 stars!'</li><li>". \n\nThe service was impeccable,:Man! I was so drunk when I ate here last weekend. \n\nI came up from LA to celebrate my boyfriend's best friend's graduation. So after the commencement ceremony a bunch of us went to a friend's house and had Vodka tonics. He made them great and I was drunk. So we went to dinner at this beautiful hotel overlooking the beach. \n\nThe best friend's parents bought us (about 20 people) dinner at Rodney's Steakhouse. There was a pre-set menu with different choices for us. \n\nFor the appetizer, I ordered the Sea Scallops. These were the best damn sea scallops I've ever had. They literally melted in my mouth and were so delicious. They came in a white wine and garlic butter onion tartlet with truffle vinaigrette.\n\nPlease keep in mind that the wine kept flowing and continued to get very giggly and drunk. So fun!\n\nFor the main course, I ordered the Roasted Dover Sole Fillet which had lump crab meat stuffing and lemon butter. It was good but it wasn't great. \n\nFor dessert I had the creme brulee which was strange tasting. I would have much rather had the chocolate mousse. \n\nThe service was impeccable, the bathrooms were very nice and clean and I met a lot of great people. Or so I think so. =)"</li></ul> |
| mixed | <ul><li>"is because the food was inedible.:I rarely ever give anything less than a 2 star and if I do, it is because the food was inedible. Literally, we paid so much for our entrees and tried to force ourselves to eat it because we hate to waste food but we couldn't even do that. Maybe we ordered the wrong dish. We had the ramen and the risotto- and we've had these type of dishes many times before. In fact, it's one of our favorite dishes normally. But the ramen was sooo disappointing. It was just watered down soy sauce. Imagine how salty that is. I've never had anything like this and was completely shocked. And the risotto was so undercooked. I am okay with al dente but I am saying this was borderline raw, hard, and was hard to digest. BUT- the BONE MARROW was AMAZING. Do order this. And maybe only this. It was perfectly balanced and savory and aromatic and presented beautifully. That was the only good tapas that we ordered and why I will give it a 2 star instead of a 1 star. Also, we spoke to the manager about our food and we discounted us and was really nice about it. I felt pretty bad but I also thought they should know- maybe we just came on a bad day? I am just really surprised that this place had such a high rating. I took my mom here for her birthday and we left this restaurant hungry and disappointed"</li></ul> |
| neutral | <ul><li>"limited amount of seating for the long:If you're ever missing LA street tacos, Lilly's is the closest you're gonna get. Without taking that into consideration, Lilly's is without a doubt one of the cheapest places to get a filling and delicious meal along the Pacific Coast, and you will love it.\n\nThe $1.80 tacos come out before you're even done ordering, which is wild considering that while they have a bunch of meats already prepped, there's still a constant rotation of beef, pork, and chicken on the grill. All tacos come double-wrapped, and if you eat in, on a Styrofoam plate that might betray the county's love for eco-friendly packaging, but it certainly cuts down on the costs.\n\nThe salsa bar is never consistent; while all the fixings are always well-stocked, how spicy each salsa is changes from day to day. Sometimes, it'll be the dark brown one that'll cause you to start crying; other days, it's the green one that packs a punch. Taste test each one before you squirt it on. Old Yelp reviews suggest their grilled onions and jalapenos used to be free, but it costs $1 for a small plate of them nowadays.\n\nBeing next to the 101 isn't ideal, nor is the limited amount of seating for the long line that builds up outside the store. But these are the kinds of things you get saddled with, not quite the stuff you can choose or imagine happening when you first start out.\n\nYou'll incur a $0.50 charge for credit cards if you spend less than $5 here. You're going to want so many tacos that you won't even think about it."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.65 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect",
"ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:----|
| Word count | 25 | 123.9048 | 272 |
| Label | Training Sample Count |
|:---------|:----------------------|
| mixed | 1 |
| negative | 1 |
| neutral | 1 |
| positive | 18 |
### Training Hyperparameters
- batch_size: (50, 50)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.1429 | 1 | 0.2034 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.0
- spaCy: 3.7.4
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | [
"CHIA"
] |
dubious-bear/test | dubious-bear | null | [
"en",
"region:us"
] | "2024-03-24T02:29:56Z" | 2024-03-24T03:19:03+00:00 | 0 | 0 | ---
language:
- en
extra_gated_heading: You need to share contact information with ... to access this
model
extra_gated_prompt: '
### DUBIOUS BEAR LICENSE
"Agreement" means the terms and conditions for use, reproduction, distribution and modification
of the Dubious Bear materials set forth herein. "Documentation" means the specifications,
manuals and documentation accompanying ...
#### Acceptable use Policy
You agree you will not use, or allow others to use, ...
Please report any violation of this Policy, software “bug,” or other problems that
could lead to a violation of this Policy through one of the following means: * Reporting
issues with the model: Link here * Reporting risky content generated by the model:
Link here * Reporting bugs and security concerns: Link here * Reporting violations
of the Acceptable Use Policy or unlicensed uses: Link here'
extra_gated_fields:
First Name: text
Last Name: text
Organization: text
? By clicking 'Submit' below, I accept the terms of the license and acknowledge
that the information I provide will be collected, stored, processed, and shared
in accordance with XXX
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the ... Privacy Policy.
extra_gated_button_content: Submit
inference: false
---
| [
"BEAR"
] |
sohampatil/msphi2-medical | sohampatil | null | [
"license:mit",
"region:us"
] | "2024-03-24T15:04:44Z" | 2024-03-24T15:16:39+00:00 | 0 | 3 | ---
license: mit
---
---
library_name: peft
base_model: microsoft/phi-2
---
## Model Details
### Model Description
We have developed a finetuned AI model designed to revolutionize how medical reports are generated. This model, built upon the robust foundation of Microsoft's Phi Large Language Model (LLM), leverages the specialized MedMCQA dataset, sourced from the OpenLifeSciences AI initiative on Hugging Face. The integration of these powerful tools enables our model to interpret and analyze complex medical data with unparalleled precision and depth.
### Dataset Overview
MedMCQA Dataset: At the heart of our model's training lies the MedMCQA dataset, a comprehensive collection of over 194,000 multiple-choice questions (MCQs) derived from AIIMS & NEET PG medical entrance exams. This dataset spans an impressive array of 21 medical subjects, covering 2,400 healthcare topics, making it an invaluable resource for developing an AI adept at understanding and generating medical content. Each question in the MedMCQA dataset is meticulously designed to test a model's reasoning across various medical fields, providing not only the questions and correct answers but also detailed explanations and alternative options, enriching the model's learning process.
### Use Cases:
Application in Medical Report Generation
HL7 Medical Reports: Our AI model is fine-tuned to assist healthcare professionals by automating the creation of HL7 medical reports. HL7 (Health Level Seven) is a set of international standards for the transfer of clinical and administrative data between software applications used by various healthcare providers. By harnessing the MedMCQA dataset, our model is trained to understand the nuances and complexities of medical data, enabling it to generate detailed, accurate, and comprehensible medical reports.
Diagnostic Support: Our AI model can analyze patient interactions and relevant health data to provide preliminary diagnostic insights, aiding doctors in their decision-making process.
Medical Education: The model can serve as an advanced tool for medical students and professionals, offering detailed explanations and reasoning, akin to those found in the MedMCQA dataset, to enhance learning and understanding.
Research and Analysis: By generating comprehensive reports, the model can assist medical researchers in compiling and analyzing data, facilitating advancements in medical research. | [
"MEDICAL DATA"
] |
Manbehindthemadness/craft_mlt_25k | Manbehindthemadness | null | [
"license:mit",
"region:us"
] | "2024-04-01T16:37:49Z" | 2024-04-01T17:01:22+00:00 | 0 | 0 | ---
license: mit
---
This is a re-publication of craft_mlt_25k from https://github.com/WindowsKonon1337/CRAFT-pytorch
I have done this to preserve the model and provide an accessable location for future projects to acquire the file(s) | [
"CRAFT"
] |
ostapeno/library-mistral7B_flan_10clsuters_3ep_lr1e-4 | ostapeno | null | [
"region:us"
] | "2024-04-03T03:58:16Z" | 2024-04-16T22:20:28+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 10
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| c6o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text,cos_e_v1_11_question_description_option_text,cos_e_v1_11_question_option_description_id,qasc_qa_with_combined_facts_1,qasc_qa_with_separated_facts_1,qasc_qa_with_separated_facts_2,qasc_qa_with_separated_facts_3,qasc_qa_with_separated_facts_5,quarel_choose_between,quarel_heres_a_story,quarel_logic_test,quarel_testing_students,quartz_answer_question_based_on,quartz_answer_question_below,quartz_given_the_fact_answer_the_q,quartz_having_read_above_passage,quartz_paragraph_question_plain_concat,quartz_read_passage_below_choose,quartz_use_info_from_paragraph_question,quartz_use_info_from_question_paragraph,quoref_Answer_Question_Given_Context,ropes_background_new_situation_answer,ropes_background_situation_middle,ropes_given_background_situation,ropes_new_situation_background_answer,ropes_plain_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_beginning,ropes_prompt_bottom_hint_beginning,ropes_prompt_bottom_no_hint,ropes_prompt_mix,ropes_read_background_situation,sciq_Direct_Question_Closed_Book_,social_i_qa_Show_choices_and_generate_answer,wiqa_does_the_supposed_perturbation_have_an_effect,wiqa_effect_with_label_answer,wiqa_effect_with_string_answer,wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| c0o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question,adversarial_qa_dbidaf_generate_question,adversarial_qa_droberta_generate_question,app_reviews_generate_review,cot_creak,cot_esnli,cot_esnli_ii,dream_generate_first_utterance,dream_generate_last_utterance,duorc_ParaphraseRC_title_generation,duorc_SelfRC_title_generation,fix_punct,gem_common_gen_1_1_0,gem_dart_1_1_0,gigaword_1_2_0,huggingface_xsum,lambada_1_0_0,race_high_Write_a_multi_choice_question_for_the_following_article,race_high_Write_a_multi_choice_question_options_given_,race_middle_Write_a_multi_choice_question_for_the_following_article,race_middle_Write_a_multi_choice_question_options_given_,stream_aqua,stream_qed,wiqa_what_is_the_missing_first_step,wmt16_translate_fi_en_1_0_0,wmt16_translate_ro_en_1_0_0,yelp_polarity_reviews_0_2_0 | lora |
| c8o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/anli_r1_0_1_0,anli_r2_0_1_0,anli_r3_0_1_0,cosmos_qa_1_0_0,cot_ecqa,cot_sensemaking,glue_cola_2_0_0,glue_mrpc_2_0_0,glue_sst2_2_0_0,imdb_reviews_plain_text_1_0_0,para_crawl_enes,super_glue_record_1_0_2,true_case,wmt14_translate_fr_en_1_0_0,wmt16_translate_de_en_1_0_0,word_segment | lora |
| c9o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_choose_best_object_affirmative_3,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_choose_best_object_interrogative_2,wiki_hop_original_explain_relation,wiki_hop_original_generate_object,wiki_hop_original_generate_subject,wiki_hop_original_generate_subject_and_object | lora |
| c2o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review,cos_e_v1_11_question_option_description_text,cot_qasc,cot_strategyqa_ii,dbpedia_14_pick_one_category_for_the_following_text,definite_pronoun_resolution_1_1_0,kilt_tasks_hotpotqa_final_exam,math_dataset_algebra__linear_1d_1_0_0,qasc_qa_with_separated_facts_4,quarel_do_not_use,quoref_Context_Contains_Answer,race_high_Is_this_the_right_answer,race_middle_Is_this_the_right_answer,sciq_Direct_Question,sciq_Multiple_Choice,sciq_Multiple_Choice_Closed_Book_,sciq_Multiple_Choice_Question_First,social_i_qa_Show_choices_and_generate_index,stream_aqua_ii,super_glue_cb_1_0_2,super_glue_copa_1_0_2,unified_qa_science_inst,wiki_qa_Decide_good_answer,wiki_qa_Direct_Answer_to_Question,wiki_qa_Generate_Question_from_Topic,wiki_qa_Jeopardy_style,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Topic_Prediction_Question_Only,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_automatic_system,wiki_qa_exercise,wiki_qa_found_on_google | lora |
| c7o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/aeslc_1_0_0,cnn_dailymail_3_4_0,coqa_1_0_0,cot_gsm8k,dream_answer_to_dialogue,duorc_ParaphraseRC_build_story_around_qa,duorc_SelfRC_build_story_around_qa,gem_e2e_nlg_1_1_0,gem_web_nlg_en_1_1_0,gem_wiki_lingua_english_en_1_1_0,multi_news_1_0_0,wiki_bio_comprehension,wiki_bio_key_content,wiki_bio_what_content,wiki_bio_who,wiqa_what_is_the_final_step_of_the_following_process,wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_might_be_the_last_step_of_the_process,wmt16_translate_tr_en_1_0_0 | lora |
| c4o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question,duorc_ParaphraseRC_decide_worth_it,duorc_ParaphraseRC_extract_answer,duorc_ParaphraseRC_generate_question,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_question_answering,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_SelfRC_extract_answer,duorc_SelfRC_generate_question,duorc_SelfRC_movie_director,duorc_SelfRC_question_answering,quac_1_0_0,quoref_Answer_Friend_Question,quoref_Answer_Test,quoref_Find_Answer,quoref_Found_Context_Online,quoref_Given_Context_Answer_Question,quoref_Guess_Answer,quoref_Guess_Title_For_Context,quoref_Read_And_Extract_,quoref_What_Is_The_Answer | lora |
| c3o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q,adversarial_qa_dbert_based_on,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbert_tell_what_it_is,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_dbidaf_based_on,adversarial_qa_dbidaf_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_droberta_based_on,adversarial_qa_droberta_question_context_answer,adversarial_qa_droberta_tell_what_it_is,cos_e_v1_11_aligned_with_common_sense,cos_e_v1_11_explain_why_human,cos_e_v1_11_generate_explanation_given_text,cos_e_v1_11_i_think,cos_e_v1_11_rationale,drop_2_0_0,duorc_ParaphraseRC_generate_question_by_answer,duorc_SelfRC_generate_question_by_answer,kilt_tasks_hotpotqa_combining_facts,kilt_tasks_hotpotqa_formulate,kilt_tasks_hotpotqa_straighforward_qa,natural_questions_open_1_0_0,trivia_qa_rc_1_1_0,web_questions_get_the_answer,web_questions_potential_correct_answer,web_questions_question_answer,web_questions_short_general_knowledge_q,web_questions_whats_the_answer | lora |
| c1o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/ag_news_subset_1_0_0,app_reviews_convert_to_rating,app_reviews_convert_to_star_rating,cot_creak_ii,cot_ecqa_ii,cot_gsm8k_ii,cot_sensemaking_ii,cot_strategyqa,dbpedia_14_given_a_choice_of_categories_,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,glue_mnli_2_0_0,glue_qnli_2_0_0,glue_qqp_2_0_0,glue_stsb_2_0_0,glue_wnli_2_0_0,kilt_tasks_hotpotqa_complex_question,paws_wiki_1_1_0,qasc_is_correct_1,qasc_is_correct_2,snli_1_1_0,social_i_qa_Check_if_a_random_answer_is_valid_or_not,social_i_qa_Generate_answer,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_I_was_wondering,squad_v1_1_3_0_0,squad_v2_0_3_0_0,stream_qed_ii,super_glue_multirc_1_0_2,super_glue_rte_1_0_2,super_glue_wic_1_0_2,super_glue_wsc_fixed_1_0_2,trec_1_0_0,wiki_bio_guess_person,wiki_qa_Is_This_True_ | lora |
| c5o10 | mistralai/Mistral-7B-v0.1 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id,cos_e_v1_11_question_description_option_id,dream_baseline,dream_read_the_following_conversation_and_answer_the_question,quail_context_description_question_answer_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_answer_description_id,quail_context_question_answer_description_text,quail_context_question_description_answer_id,quail_context_question_description_answer_text,quail_context_question_description_text,quail_description_context_question_answer_id,quail_description_context_question_answer_text,quail_description_context_question_text,quail_no_prompt_id,quail_no_prompt_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,race_high_Select_the_best_answer_generate_span_,race_high_Select_the_best_answer_no_instructions_,race_high_Taking_a_test,race_middle_Read_the_article_and_answer_the_question_no_option_,race_middle_Select_the_best_answer,race_middle_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_no_instructions_,race_middle_Taking_a_test | lora |
Last updated on: 2024-04-06 04:06:45+00:00
| [
"SCIQ"
] |
steventango/GNorm2-docker | steventango | null | [
"region:us"
] | "2024-04-07T19:47:38Z" | 2024-04-25T05:40:12+00:00 | 0 | 0 | ---
{}
---
# GNorm2
***
GNorm2 is a gene name recognition and normalization tool with optimized functions and customizable configuration to the user preferences. The GNorm2 integrates multiple deep learning-based methods and achieves state-of-the-art performance. GNorm2 is freely available to download for stand-alone usage. [Download GNorm2 here](https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/GNorm2/GNorm2.tar.gz)
## Content
- [Dependency package](#package)
- [Introduction of folders](#intro)
- [Running GNorm2](#pipeline)
## Dependency package
<a name="package"></a>
The codes have been tested using Python3.8/3.9 on CentOS and uses the following main dependencies on a CPU and GPU:
- [TensorFlow 2.3.0](https://www.tensorflow.org/)
- [Transformer 4.18.0](https://huggingface.co/docs/transformers/installation)
- [stanza 1.4.0](stanfordnlp.github.io/stanza/)
To install all dependencies automatically using the command:
$ pip install -r requirements.txt
## Introduction of folders
<a name="intro"></a>
- src_python
- GeneNER: the codes for gene recognition
- SpeAss: the codes for species assignment
- src_Java
- GNormPluslib : the codes for gene normalization and species recogntion
- GeneNER_SpeAss_run.py: the script for runing pipeline
- GNormPlus.jar: the upgraded GNormPlus tools for gene normalization
- gnorm_trained_models:pre-trianed models and trained NER/SA models
- bioformer-cased-v1.0: the original bioformer model
- BiomedNLP-PubMedBERT-base-uncased-abstract: the original pubmedbert model
- geneNER
- GeneNER-Bioformer/PubmedBERT-Allset.h5: the Gene NER models trained by all datasets
- GeneNER-Bioformer/PubmedBERT-Trainset.h5: the Gene NER models trained by the training set only
- SpeAss
- SpeAss-Bioformer/PubmedBERT-SG-Allset.h5: the Species Assignment models trained by all datasets
- SpeAss-Bioformer/PubmedBERT-SG-Trainset.h5: the Species Assignment models trained by the trianing set only
- stanza
- downloaded stanza library for offline usage
- vocab: label files for the machine learning models of GeneNER and SpeAss
- Dictionary: The dictionary folder contains all required files for gene normalization
- CRF: CRF++ library (called by GNormPlus.sh)
- Library: Ab3P library
- tmp/tmp_GNR/tmp_SA/tmp_SR folders: temp folder
- input/output folders: input and output folders. BioC (abstract or full text) and PubTator (abstract only) formats are both avaliable.
- GNorm2.sh: the script to run GNorm2
- setup.GN.txt/setup.SR.txt/setup.txt the setup files for GNorm2.
## Running GNorm2
<a name="pipeline"></a>
Please firstly download [GNorm2](https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/GNorm2/GNorm2.tar.gz) to your local.
Below are the well-trained models (i.e., PubmedBERT/Bioformer) for Gene NER and Species Assignment.
Models for Gene NER:
- gnorm_trained_models/geneNER/GeneNER-PubmedBERT.h5
- gnorm_trained_models/geneNER/GeneNER-Bioformer.h5
Models for Species Assignment:
- gnorm_trained_models/SpeAss/SpeAss-PubmedBERT.h5
- gnorm_trained_models/SpeAss/SpeAss-Bioformer.h5
The parameters of the input/output folders:
- INPUT, default="input"
- OUTPUT, default="output"
[BioC-XML](bioc.sourceforge.net) or [PubTator](https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/Format.html) formats are both avaliabel to GNorm2.
1. Run GNorm2
Run Example:
$ ./GNorm2.sh input output
## Acknowledgments
This research was supported by the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health. | [
"GNORMPLUS"
] |
Zainn123/bioresults | Zainn123 | null | [
"region:us"
] | "2024-04-10T09:58:32Z" | 2024-04-10T09:58:45+00:00 | 0 | 0 | ---
{}
---
hf (pretrained=johnsnowlabs/BioLing-7B-Dare), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.5164|± |0.0059|
| | |none | 0|acc_norm|0.4705|± |0.0068|
| - medmcqa |Yaml |none | 0|acc |0.4633|± |0.0077|
| | |none | 0|acc_norm|0.4633|± |0.0077|
| - medqa_4options |Yaml |none | 0|acc |0.4941|± |0.0140|
| | |none | 0|acc_norm|0.4941|± |0.0140|
| - anatomy (mmlu) | 0|none | 0|acc |0.5556|± |0.0429|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.6755|± |0.0288|
| - college_biology (mmlu) | 0|none | 0|acc |0.6319|± |0.0403|
| - college_medicine (mmlu) | 0|none | 0|acc |0.5896|± |0.0375|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.6900|± |0.0465|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.6654|± |0.0287|
| - pubmedqa | 1|none | 0|acc |0.7480|± |0.0194|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.5164|± |0.0059|
| | |none | 0|acc_norm|0.4705|± |0.0068| | [
"MEDQA",
"PUBMEDQA"
] |
openkg/OneKE | openkg | null | [
"arxiv:2402.14710",
"license:cc-by-nc-sa-4.0",
"region:us"
] | "2024-04-11T04:43:48Z" | 2024-04-11T06:05:36+00:00 | 0 | 17 | ---
license: cc-by-nc-sa-4.0
---
<p align="center">
<a href="https://github.com/zjunlp/deepke"> <img src="assets/oneke_logo.png" width="400"/></a>
<p>
<p align="center">
<a href="https://oneke.openkg.cn/">
<img alt="Documentation" src="https://img.shields.io/badge/demo-website-blue">
</a>
<a href="https://pypi.org/project/deepke/#files">
<img alt="PyPI" src="https://img.shields.io/pypi/v/deepke">
</a>
<a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke">
</a>
<a href="http://zjunlp.github.io/DeepKE">
<img alt="Documentation" src="https://img.shields.io/badge/doc-website-red">
</a>
</p>
<h1 align="center">
<p>OneKE: A Bilingual Large Language Model for <br>Knowledge Extraction</p>
</h1>
- [What is OneKE?](#what-is-oneke)
- [How is OneKE trained?](#how-is-oneke-trained)
- [Getting Started with OneKE](#getting-started-with-oneke)
- [Quick Start](#quick-start)
- [Advanced Use of OneKE](#advanced-use-of-oneke)
- [OneKE Instruction Format](#oneke-instruction-format)
- [Conversion of OneKE Instruction Format](#conversion-of-oneke-instruction-format)
- [Customized Schema Description Instructions](#customized-schema-description-instructions)
- [Evaluation](#evaluation)
- [Continue Training](#continue-training)
- [Citation](#citation)
## What is OneKE?
OneKE is a new bilingual knowledge extraction large model developed jointly by Zhejiang University and Ant Group, leveraging their years of accumulation in knowledge graph and natural language processing technology. Launched in 2024, the model employs schema-based polling instruction construction technology and is optimized to enhance the model's generalization capabilities for structured information extraction.
<p align="center" width="100%">
<a href="" target="_blank"><img src="assets/oneke.gif" alt="OneKE" style="width: 100%; min-width: 20px; display: block; margin: auto;"></a>
</p>
## How is OneKE trained?
OneKE mainly focuses on schema-generalizable information extraction. Due to issues such as non-standard formats, noisy data, and lack of diversity in existing extraction instruction data, OneKE adopted techniques such as normalization and cleaning of extraction instructions, difficult negative sample collection, and schema-based batched instruction construction, as shown in the illustration. For more detailed information, refer to the paper "[IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus](https://arxiv.org/abs/2402.14710) [[Github](https://github.com/zjunlp/IEPile)]".
The zero-shot generalization comparison results of OneKE with other large models are as follows:
* `NER-en`: CrossNER_AI, CrossNER_literature, CrossNER_music, CrossNER_politics, CrossNER_science
* `NER-zh`: WEIBONER, boson
* `RE-zh`: COAE2016, IPRE, SKE2020
* `RE-en`: FewRel, Wiki-ZSL
* `EE-en`: CrudeOilNews, WikiEvents, RAMS
* `EE-zh`: FewFC, CCF Law
<p align="center" width="50%">
<a href="" target="_blank"><img src="assets/oneke_results.png" alt="OneKE" style="width: 50%; min-width: 20px; display: block; margin: auto;"></a>
</p>
Here's the translation of the provided text into English:
## Getting Started with OneKE
### Model Donwload
[HuggingFace](https://huggingface.co/zjunlp/OneKE)
### Quick Start
It is recommended to have at least **20GB of VRAM** for training and inferencing.
```python
import torch
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
BitsAndBytesConfig
)
model_path = 'zjunlp/OneKE'
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# 4bit量化OneKE
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
config=config,
device_map="auto",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model.eval()
system_prompt = '<<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n'
sintruct = "{\"instruction\": \"You are an expert in named entity recognition. Please extract entities that match the schema definition from the input. Return an empty list if the entity type does not exist. Please respond in the format of a JSON string.\", \"schema\": [\"person\", \"organization\", \"else\", \"location\"], \"input\": \"284 Robert Allenby ( Australia ) 69 71 71 73 , Miguel Angel Martin ( Spain ) 75 70 71 68 ( Allenby won at first play-off hole )\"}"
sintruct = '[INST] ' + system_prompt + sintruct + '[/INST]'
input_ids = tokenizer.encode(sintruct, return_tensors="pt")
input_length = input_ids.size(1)
generation_output = model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_length=1024, max_new_tokens=512, return_dict_in_generate=True))
generation_output = generation_output.sequences[0]
generation_output = generation_output[input_length:]
output = tokenizer.decode(generation_output, skip_special_tokens=True)
print(output)
```
### Advanced Use of OneKE
### OneKE Instruction Format
The instructions in OneKE are formatted in a dictionary-type string similar to JSON. It consists of three fields:
(1) **`'instruction'`**, which is the task description, specifies in natural language the role the model plays and the task to be completed;
(2) **`'schema'`**, a list of labels to be extracted, clearly indicates the key fields of the information to be extracted, reflecting the user's needs, and is dynamic and changeable;
(3) **`'input'`**, refers to the source text for information extraction.
Below are examples of instructions for various tasks:
<details>
<summary><b>Named Entity Recognition (NER)</b></summary>
```json
{
"instruction": "You are an expert specializing in entity extraction. Please extract entities that comply with the schema definition from the input; return an empty list for non-existent entity types. Please respond in the JSON string format.",
"schema": ["Person Name", "Education", "Position", "Nationality"],
"input": "Mr. Liu Zhijian: Born in 1956, Chinese nationality, no permanent residency abroad, member of the Communist Party, associate degree, senior economist."
}
```
</details>
<details>
<summary><b>Relation Extraction (RE)</b></summary>
```json
{
"instruction": "You are an expert specializing in relation extraction. Please extract relationship triples that comply with the schema definition from the input; return an empty list for non-existent relationships. Please respond in the JSON string format.",
"schema": ["Father", "Husband", "Postal Code", "Mother"],
"input": "Ding Long took out his life savings of $12,000, which without a doubt was a substantial amount at the end of the 19th century, plus Carpentier's donation, they both funded Columbia University's sinology research together."
}
```
</details>
<details>
<summary><b>Knowledge Graph Construction (KGC)</b></summary>
```json
{
"instruction": "You are an expert in structuring knowledge about graph entities. Based on the schema description of the input entity type, extract the corresponding entity instances and their property information from the text; do not output non-existent properties, return a list if there are multiple values for a property, and provide the output in a parseable json format.",
"schema": [
{
"entity_type": "Person",
"attributes": ["Chinese Name", "English Name", "Ancestral Home", "Date of Birth", "Place of Birth", "Occupation", "Alma Mater", "Works", "Awards"]
}
],
"input": "Jay Chou (Jay Chou), born on January 18, 1979, in New Taipei City, Taiwan Province, ancestral home in Yongchun County, Quanzhou City, Fujian Province, Chinese pop singer, musician, actor, director, screenwriter, graduated from Tamkang High School. In 2000, he released his debut album 'Jay'. In 2001, he cemented his style of blending Eastern and Western music with the album 'Fantasy'. In 2002, he held ‘The One’ world tour; the same year, he won the Best Composer award at the 13th Taiwan Golden Melody Awards with the song 'Love Before the Century'."
}
```
</details>
<details>
<summary><b>Event Extraction (EE)</b></summary>
```json
{
"instruction": "You are an expert specializing in event extraction. Please extract events that match the defined schema from the input; return an empty list for non-existent events, NAN for non-existent arguments, and a list if there are multiple values for an argument. Please provide your response in JSON string format.",
"schema": [
{
"event_type": "Finance/Trading - Interest Rate Hike",
"trigger": true,
"arguments": [
"Time"
]
},
{
"event_type": "Finance/Trading - Interest Rate Cut",
"trigger": true,
"arguments": [
"Cut Magnitude"
]
},
{
"event_type": "Finance/Trading - Price Increase",
"trigger": true,
"arguments": [
"Price Raiser"
]
},
{
"event_type": "Finance/Trading - Price Cut",
"trigger": true,
"arguments": [
"Price Cutter",
"Time"
]
}
],
"input": "AI risk control solution provider Vezetech secures tens of millions of dollars in Series C+ funding"
}
```
</details>
<details>
<summary><b>Event Trigger Identification (EET)</b></summary>
```json
{
"instruction": "You are an expert specializing in event trigger identification. Please extract the event types and triggers that match the defined schema from the input; return an empty list if the event type doesn't exist. Please provide your response in JSON string format.",
"schema": ["Organizational Relationship - Dissolve", "Organizational Relationship - Layoff", "Organizational Relationship - Dismiss", "Competition Behavior - Promotion"],
"input": "Nestlé lays off 4,000 employees: When the times leave you behind, they won't even say goodbye!"
}
```
</details>
<details>
<summary><b>Event Argument Extraction (EEA)</b></summary>
```json
{
"instruction": "You are an expert specializing in event argument extraction. Please extract the event arguments and their roles that match the defined schema from the input; return NAN or an empty dictionary for non-existent arguments, and a list if there are multiple values for an argument. Please provide your response in JSON string format.",
"schema": [{"event_type": "Organizational Relationship - Resignation/Departure", "arguments": ["Resigner", "Time", "Former Organization"]}],
"input": "Nestlé lays off 4,000 employees: When the times leave you behind, they won't even say goodbye!"
}
```
</details>
### Conversion of OneKE Instruction Format
**List of Instructions**:
```python
instruction_mapper = {
'NERzh': "你是专门进行实体抽取的专家。请从input中抽取出符合schema定义的实体，不存在的实体类型返回空列表。请按照JSON字符串的格式回答。",
'REzh': "你是专门进行关系抽取的专家。请从input中抽取出符合schema定义的关系三元组，不存在的关系返回空列表。请按照JSON字符串的格式回答。",
'EEzh': "你是专门进行事件提取的专家。请从input中抽取出符合schema定义的事件，不存在的事件返回空列表，不存在的论元返回NAN，如果论元存在多值请返回列表。请按照JSON字符串的格式回答。",
'EETzh': "你是专门进行事件提取的专家。请从input中抽取出符合schema定义的事件类型及事件触发词，不存在的事件返回空列表。请按照JSON字符串的格式回答。",
'EEAzh': "你是专门进行事件论元提取的专家。请从input中抽取出符合schema定义的事件论元及论元角色，不存在的论元返回NAN或空字典，如果论元存在多值请返回列表。请按照JSON字符串的格式回答。",
'KGzh': '你是一个图谱实体知识结构化专家。根据输入实体类型(entity type)的schema描述，从文本中抽取出相应的实体实例和其属性信息，不存在的属性不输出, 属性存在多值就返回列表，并输出为可解析的json格式。',
'NERen': "You are an expert in named entity recognition. Please extract entities that match the schema definition from the input. Return an empty list if the entity type does not exist. Please respond in the format of a JSON string.",
'REen': "You are an expert in relationship extraction. Please extract relationship triples that match the schema definition from the input. Return an empty list for relationships that do not exist. Please respond in the format of a JSON string.",
'EEen': "You are an expert in event extraction. Please extract events from the input that conform to the schema definition. Return an empty list for events that do not exist, and return NAN for arguments that do not exist. If an argument has multiple values, please return a list. Respond in the format of a JSON string.",
'EETen': "You are an expert in event extraction. Please extract event types and event trigger words from the input that conform to the schema definition. Return an empty list for non-existent events. Please respond in the format of a JSON string.",
'EEAen': "You are an expert in event argument extraction. Please extract event arguments and their roles from the input that conform to the schema definition, which already includes event trigger words. If an argument does not exist, return NAN or an empty dictionary. Please respond in the format of a JSON string.",
'KGen': 'You are an expert in structured knowledge systems for graph entities. Based on the schema description of the input entity type, you extract the corresponding entity instances and their attribute information from the text. Attributes that do not exist should not be output. If an attribute has multiple values, a list should be returned. The results should be output in a parsable JSON format.',
}
```
Recommended **Split Numbers** for Each Task:
```python
split_num_mapper = {
'NER':6, 'RE':4, 'EE':4, 'EET':4, 'EEA':4, 'KG':1
}
```
Since predicting all schemas in the label set at once is too challenging and not easily scalable, OneKE uses a batched approach during training. It divides the number of schemas asked in the instructions, querying a fixed number of schemas at a time. Hence, if the label set of a piece of data is too long, it will be split into multiple instructions that the model will address in turns.
**schema格式**:
```python
NER: ["Person Name", "Education", "Position", "Nationality"] # List of strings
RE: ["Father", "Husband", "Postal Code", "Mother"] # List of strings
EE: [{"event_type": "Finance/Trading - Interest Rate Hike", "trigger": True, "arguments": ["Time"]}, {"event_type": "Finance/Trading - Interest Rate Cut", "trigger": True, "arguments": ["Cut Magnitude"]}] # List of dictionaries, "event_type" is a string, "trigger" is a bool, "arguments" is a list
EET: ["Organizational Relationship - Dissolution", "Organizational Relationship - Layoff", "Organizational Relationship - Dismissal", "Competition Behavior - Advancement"] # List of strings
EEA: [{"event_type": "Finance/Trading - Interest Rate Hike", "arguments": ["Time"]}, {"event_type": "Finance/Trading - Interest Rate Cut", "arguments": ["Cut Magnitude"]}] # List of dictionaries, "event_type" is a string, "arguments" is a list
```
Below is a simple Batched Instruction Generation script:
```python
def get_instruction(language, task, schema, input):
sintructs = []
split_num = split_num_mapper[task]
if type(schema) == dict:
sintruct = json.dumps({'instruction':instruction_mapper[task+language], 'schema':schema, 'input':input}, ensure_ascii=False)
sintructs.append(sintruct)
else:
split_schemas = [schema[i:i+split_num] for i in range(0, len(schema), split_num)]
for split_schema in split_schemas:
sintruct = json.dumps({'instruction':instruction_mapper[task+language], 'schema':split_schema, 'input':input}, ensure_ascii=False)
sintructs.append(sintruct)
return sintructs
```
For more detailed data conversion, please refer to [InstructKGC/README_CN.md/2.3 Testing Data Conversion](./InstructKGC/README_CN.md/#23测试数据转换).
Below is an example using the aforementioned simple script:
```python
task = 'NER'
language = 'en'
schema = ['person', 'organization', 'else', 'location']
split_num = split_num_mapper[task]
split_schemas = [schema[i:i+split_num] for i in range(0, len(schema), split_num)]
input = '284 Robert Allenby ( Australia ) 69 71 71 73 , Miguel Angel Martin ( Spain ) 75 70 71 68 ( Allenby won at first play-off hole )'
sintructs = []
for split_schema in split_schemas:
sintruct = json.dumps({'instruction':instruction_mapper[task+language], 'schema':split_schema, 'input':input}, ensure_ascii=False)
sintructs.append(sintruct)
```
> '{"instruction": "You are an expert in named entity recognition. Please extract entities that match the schema definition from the input. Return an empty list if the entity type does not exist. Please respond in the format of a JSON string.", "schema": ["person", "organization", "else", "location"], "input": "284 Robert Allenby ( Australia ) 69 71 71 73 , Miguel Angel Martin ( Spain ) 75 70 71 68 ( Allenby won at first play-off hole )"}'
### Customized Schema Description Instructions
```json
{
"instruction": "You are an expert specializing in entity extraction. Please extract entities that comply with the defined schema from the input; return an empty list for non-existent entity types. Please respond in JSON string format.",
"schema": {
"Position": "The entity type describes the occupation or official position of an individual or group, including specific role names such as 'producer', 'scorekeeper', 'ascetic', 'oil painter'.",
"Attraction": "The entity type of attraction includes buildings, museums, memorials, art galleries, rivers, peaks, etc. Representative entities include the Pentagon, Tate Modern, Zheng Chenggong Memorial Hall, Duxi Palace, Barikasa, Robo River, Gunung Batur, Yugong Yishan LIVE, Xu Beihong Memorial Hall, Madame Tussauds, etc.",
"Company": "Company is an entity type representing any legal entity or business organization. This type of entity can be a catering group, manufacturer, retailer, hotel, bank, design institute, etc. Examples include: 'Shangri-La Hotel Group', 'JVC', 'Shanghai Coolray Professional eSports Peripheral Store', 'K2•Haitang Bay', 'Wuhan Iron and Steel', 'louisvuitton', 'Bank of Scotland', 'Beijing Institute of Architectural Design', '7 Days Inn', 'Vanke Group'.",
"Address": "Address entities refer to entities with geographical location information, representing specific places such as a country, city, region, street, or abstract geographic areas. Examples include: 'the river dock at the southeast tip of downtown Manhattan', 'Tuapse', 'Venice, Italy', 'Huzhou Hot Spring Golf Course', 'North Carolina', 'Beijing-Tianjin region', 'Happy Internet Cafe', 'Yinian Nursing Home', 'Shangtang Town Pudong', 'Inner Mongolia Autonomous Region Chifeng City', etc.",
"Organization": "Organizational entities refer to collective organizations such as companies, shops, clubs, schools, etc. They play a certain role in social and economic activities and have certain personality rights.",
"Movie": "Movie entities include titles of movies in Chinese or English, and sometimes also include names of characters in films."
},
"input": "It is difficult for me to imagine setting up another Haifishing Plaza. When we obtained this project, I just happened to be in Sanya."
}
```
<details>
<summary><b>Relation Extraction (RE) Description Instructions</b></summary>
```json
{
"instruction": "You are an expert specializing in relation extraction. Please extract triples that match the defined schema from the input; return an empty list for non-existent relations. Please respond in JSON string format.",
"schema": {
"Ethnicity": "Ethnicity",
"Alma Mater": "This type of relationship describes the connection between a person and their alma mater; the person is the subject, and the alma mater is the object. By identifying the names of people and schools in the text and analyzing the relationship of graduation between them based on word combinations and contextual information.",
"Lead Actor": "This is a type of relationship that describes the connection between a film or television work and its main actors; the subject is the film or television work and the object is the actor. In a valid 'Lead Actor' relationship, the actor (object) plays an important role in the work (subject).",
"Father": "This type of relationship is used to indicate the kinship between a father and a child, where the father is the birth parent or caregiver of the child. In the triple, the subject of the 'Father' relation type is the child, and the object is the father."
},
"input": "Throughout history, all those who have portrayed the character 'Chu Liuxiang' from Gu Long's novels are recognized as handsome men in the entertainment industry. In 2011, 36-year-old Zhang Zhiyao played Chu Liuxiang in 'The New Adventures of Chu Liuxiang', remaining irresistibly handsome."
}
```
</details>
<details>
<summary><b>Event Extraction (EE) Description Instructions</b></summary>
```json
{
"instruction": "You are an expert specializing in event extraction. Please extract events that match the schema definition from the input; return an empty list for non-existent events, NAN for non-existent arguments, and a list if there are multiple values for an argument. Please respond in JSON string format.",
"schema": {
"Finance/Trading - Listing": {
"Finance/Trading - Listing": "The act of a financial entity being listed on the stock market mainly involves companies, stocks, etc. Positive examples include specific information about a company or stock listing, while negative examples are unrelated to such activities.",
"trigger": true,
"arguments": {
"Financing Amount": "Refers to the total amount of funds raised by a company in a listing event. It sums up the revenue of all share issues and is measured in currency, including but not limited to units like 'billion', 'million', 'dollars', 'RMB', etc.",
"Time": "Describes the specific time of the listing event, which can be a specific date or relative time, and may also include location information and specific days and weeks.",
"Listing Enterprise": "Refers to the company or enterprise that is conducting an IPO or has already been listed on the trading market in a listing event. Examples include: 'Shanghai Henlius Biotech', 'Three Squirrels', 'Baoxin Software', 'Little Bear Electric', 'Jinshang Bank', 'Beyond Meat (BYND)', 'DouYu gaming live-streaming platform', 'fast food empire', and 'autonomous driving lidar manufacturer Velodyne', etc.",
"Location": "The specific location of the financial or trading event, such as a city, building, or room."
}
},
"Organizational Relationship - Resignation/Departure": {
"Organizational Relationship - Resignation/Departure": "The event type 'Organizational Relationship - Resignation/Departure' refers to changes in the relationship between individuals or organizational members and their organization, mainly including 'resignation', 'requesting to resign', 'stepping down', 'leaving the team', 'retirement', 'leaving', etc. Often occurs in scenarios of high-level personnel changes, government officials changes, or athletes transfers. Examples: 'Li Nan announced resignation', 'Yu Xubo resigned from the position of chairman of the board just three months after taking office, Chen Lang succeeded'.",
"trigger": true,
"arguments": {
"Resigner": "Refers to the individual or group who actively or passively leaves their original position or job post in an organizational relationship resignation/departure event. It can be one person or a group of people, such as: 'Finance Minister', '90s born guy from Shaoyang Longhui, Ouyang En and', 'Xiong Xiaoge', '*ST Changsheng two deputy general managers', 'Yang Tao', 'pilot Ma Qiang', 'HE WEI', '5 Baidu executives', 'Youxin Group COO Peng Weilian', 'Jianke Institute securities representative Shu Yanming', etc.",
"Time": "Indicates the specific point in time or period when the resignation/departure event occurred, generally including specific dates, weeks, times, etc., like 'September 19', 'the evening of June 29', 'this Saturday', '10:30 AM on July 9', 'the morning of June 12', 'April 9', 'September 10', 'local time on Sunday', 'September 12', '10 AM on October 15', etc."
}
},
"Finance/Trading - Interest Rate Increase": {
"Finance/Trading - Interest Rate Increase": "This event describes banks or financial institutions raising interest rates to tighten the money supply. The typical trigger word is 'hike'. 'Hike' indicates the occurrence of the Finance/Trading - Interest Rate Increase event.",
"trigger": true,
"arguments": {
"Rate of Increase": "The rate of increase is usually presented as a percentage or basis points, indicating the degree or range of the interest rate hike in the event. Examples include: 'to 5.75%', '25 basis points', 'the benchmark rate from 0.25% up to 0.5%', '25 basis points'.",
"Hiking Institution": "The hiking institution is the financial institution with the authority to determine or implement the interest rate hike policy in a Finance/Trading - Interest Rate Increase event, such as central banks from different countries (e.g., Bank of England, Federal Reserve, European Central Bank) or financial institutions (e.g., Bank of England).",
"Time": "Indicates the specific date or time period when the Finance/Trading - Interest Rate Increase event occurred, such as 'the morning of June 18th', 'January 24th', 'three months later', etc. The specific expression includes time accurate to the minute, such as '11:00 on December 28, 2018', relative time, such as 'yesterday (2nd)', and special time expressions like 'Mid-Autumn Festival'."
}
},
"Organizational Relationship - Contract Termination": {
"Organizational Relationship - Contract Termination": "Situations of contract cancellation or termination usually occur in the business, entertainment, or sports domains. Trigger words include 'leave', 'trade', 'cut', 'contract expiry', 'contract termination', 'sell-off', 'release', 'send out', 'contract break', etc. Positive examples include 'Peng Yuchang terminates his contract' and 'Jiang Mengjie nearly bankrupt after contract termination'. Negative examples are like 'Federer withdrew from the competition'.",
"trigger": true,
"arguments": {
"Party Being Terminated": "In an organizational relationship contract termination event, the role is the party whose agreement or contract relation is being dissolved, and might be an individual or an organization, such as an athlete, film producer, company, etc. For instance, 'seven-time All-Star Joe Johnson', 'the production side of 'A Little Wish'', 'Raptors', 'Samsung', etc."
}
}
},
"input": "News from August 20th, according to Tencent News 'Frontline' report, informed sources stated that in order to control cost expenditure, NIO plans to reduce the number of staff at its U.S. branch, excluding those involved in the autonomous driving business, to about 200. As of August 16th, U.S. time, NIO's Silicon Valley branch had cut 100 employees."
}
```
</details>
<details>
<summary><b>Knowledge Graph Construction (KGC) Description Instructions</b></summary>
```json
{
"instruction": "You are an expert in structuring knowledge about graph entities. Based on the schema description for the input entity type, extract the corresponding entity instances and their attribute information from the text; do not output non-existent attributes, return a list for attributes with multiple values, and provide the output in a parseable JSON format.",
"schema": [
{
"entity_type": "Person",
"attributes": {
"Chinese Name": "The Chinese name of the person",
"English Name": "The English name of the person",
"Ancestral Home": "The ancestral address of the person",
"Date of Birth": "Birthday, birth date",
"Place of Birth": "The place of birth, administrative region",
"Occupation": "The occupation, position, identity of the person",
"Alma Mater": "The middle school, university, college from which the person graduated",
"Works": "Albums, songs, novels, published books, participated film and television works, etc.",
"Awards": "Various awards and honors received by the person"
}
}
],
"input": "Jay Chou (Jay Chou), born on January 18, 1979, in New Taipei City, Taiwan Province, with ancestral home in Yongchun County, Quanzhou City, Fujian Province, is a Chinese pop musician, actor, director, and screenwriter. He graduated from Tamkang High School. In 2000, he released his debut music album 'Jay.' In 2001, he cemented his fusion style of Eastern and Western music with the album 'Fantasy.' In 2002, he held 'The One' world tour; that same year, he won the Best Composer award at the 13th Taiwan Golden Melody Awards for the song 'Love Before the Century.'"
}
```
</details>
## Evaluation
To extract structured content from the output text and to assess it, please refer to [InstructKGC/README_CN.md/7. Evaluation](./InstructKGC/README_CN.md/#🧾-7评估).
## Continue Training
To continue training OneKE, refer to [InstructKGC/4.9 Domain-specific Data Continual Training](./InstructKGC/README_CN.md/#49领域内数据继续训练).
## Citation
If you have used OneKE in your work, please kindly cite the following paper:
```bibtex
@article{DBLP:journals/corr/abs-2402-14710,
author = {Honghao Gui and
Lin Yuan and
Hongbin Ye and
Ningyu Zhang and
Mengshu Sun and
Lei Liang and
Huajun Chen},
title = {IEPile: Unearthing Large-Scale Schema-Based Information Extraction
Corpus},
journal = {CoRR},
volume = {abs/2402.14710},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2402.14710},
doi = {10.48550/ARXIV.2402.14710},
eprinttype = {arXiv},
eprint = {2402.14710},
timestamp = {Tue, 09 Apr 2024 07:32:43 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2402-14710.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
| [
"BEAR"
] |
aistrosight/Neg-CamemBERT-bio | aistrosight | token-classification | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"medical",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | "2024-04-12T12:33:19Z" | 2024-04-30T11:43:44+00:00 | 0 | 0 | ---
language:
- fr
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- medical
widget:
- text: absence de signe d'anomalie des petites voies aériennes, notamment pas de
signe de piégeage.
---
# Neg-CamemBERT-bio: Language model for negation detection in radiological and other clinical texts for the French language.
Neg-CamemBERT-bio is a refined version of the transformer-based [**CamemBERT-bio-base model**](https://huggingface.co/almanach/camembert-bio-base), fine-tuned for the recognition of negations in clinical texts.
Neg-CamemBERT-bio automatically detects both the negation cues and their scope.
A public model is available for negation recognition in biomedical texts, along with two additional models that are kept private since trained with potentially sensitive data from Lyon University Hospital (Hospices Civils de Lyoon, HCL).
— one dedicated to radiology reports and the other designed more broadly for various medical texts.
## Model Details
1- Neg-CamemBERT-bio: Fine-tuning of the CamemBERT-bio-base model for negation recognition in biomedical texts in French.
2- Neg-Radio-CamemBERT-bio: Fine-tuning of the CamemBERT-bio-base model for negation recognition in anonymized texts extracted from French-written thoracic CT scans
provided by the Radiology Department of the Hospices Civils of Lyon.
3- Neg-Medical-CamemBERT-bio: Fine-tuning of the CamemBERT-bio-base model for negation recognition in clinical texts across various medical domains
(Radiology, Biomedical, Medical literature,...) in French.
| Model name | Type | Corpus train | Number of sentences | Negative sentences |
| :----------- | :---- | :---- | :---------------: | :-------------------: |
| `Neg-CamemBERT-bio` | public| ESSAI + CAS | 11 037 | 1 812 |2 009 |
| `Neg-Radio-CamemBERT-bio`| privite| RADIO | 10 798 | 2 321 | 2 762|
| `Neg-Medical-CamemBERT-bio` | privite | RADIO + ESSAI + CAS + QUAERO| 21 956 | 4 244|
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
| **Corpus** | **Details** |**Licence**|
|------------|--------------------------------------------------------------------|--------------|
| RADIO | Corpus extracted from thoracic scan reports provided by the Radiology Department of Lyon University Hospital (Hospices Civils de Lyon, HCL).| Private corpus|
| ESSAI | ESSAI Clinical is a freely-available corpus that contains clinical trial protocols in French language collected from the registry of the National Cancer Institute (data available [here](https://clementdalloux.fr/?page_id=28)). | CC BY-NC-SA 4.0 DEED |
| CAS | CAS is a freely-available corpus in French containing clinical cases as published in scientific, legal, or educational literature (data available [here](https://clementdalloux.fr/?page_id=28)).| CC BY-NC-SA 4.0 DEED |
| QUAERO| QUAERO is freely-available corpus that contains a vast amount of information in the biomedical field and is available in the form of free-text in natural language (data available [here](https://quaerofrenchmed.limsi.fr/)). | GNU Free Documentation License | | 413 M |
The training dataset distinguishes between the beginning, inside, and end of each negation entity using a BIO annotation scheme.
Abbreviation|Description
-|-
O |Outside of a named entity, represents the affirmative part of the sentence.
B-cue |Beginning of the negation cue.
I-cue | Inside of the negation cue.
B-scope |Beginning of the negation scope.
I-scope |Inside of the negation scope.
### Fine-tuning
The CamemBERT-bio-base model, along with its tokenizer, was fine-tuned for the token classification task in order to identify negation cues and their scope.
We used the Adam optimizer with a learning rate of 5e−5, and a batch size of 16.
The maximum length limit was set to 512 tokens.
The model was trained using cross-validation with 10 folds (90% training / 10% validation). For each fold, the model was trained for 25 epochs.
We eventually selected the best model out of the 10 folds.
### Evaluation
To evaluate the performance of the model and quantify the results, we used the following metrics:
precision (P), recall (R), and the F1 score (F1). The scores were measured using the seqeval tool.
### Results
Validation was performed on a 10% sample of sentences from the training set for each model.
| Model's | Validation Dataset | Metrcis Score | Entity 1: cue | Entity 2: scope |
| :----------- | :------:| :---------------:|:---------------: | :---------------: |
| `Neg-CamemBERT-bio`| 10% (ESSAI+CAS) | P | 95.70 ± 0.94| 86.43 ± 1.12|
| | | R | 97.70 ± 0.56| 85.55 ± 1.33|
| | | F1 | 96.68 ± 0.46 |87.46 ± 0.93 |
| `Neg-Radio-CamemBERT-bio` | 10% (RADIO) | P | 99.35 ± 0.24 | 94.19 ± 0.94|
| | | R |99.37 ± 0.31 |94.80 ± 0.84 |
| | | F1 |99.36 ± 0.25 |94.49 ± 0.80 |
| `Neg-Medical-CamemBERT-bio`| 10%(NegRADIO + ESSAI + CAS + QUAERO)| P | 97.75 ± 0.45 |90.48 ± 0.74 |
| | | R |98.67 ± 0.20 |91.34 ± 0.60 |
| | | F1 |98.20 ± 0.21 | 90.90 ± 0.585|
### Model Description
- **Developed by:** Salim SADOUNE, Antoine Richard, François Talbot,Thomas Guyet, Loic Boussel and Hugues Berry
- **Model type:** NER
- **Language(s) (NLP):** French
- **License:** MIT
- **Finetuned from model:** See the [**CamemBERT-bio-base model**](https://huggingface.co/almanach/camembert-bio-base) for more information on this model.
### Direct Use
You can use the public model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("aistrosight/Neg-CamemBERT-bio")
model = AutoModelForTokenClassification.from_pretrained("aistrosight/Neg-CamemBERT-bio")
NegNamedEntityRecogniser = pipeline(task="token-classification", model=model , tokenizer = tokenizer, aggregation_strategy="simple")
text = ["absence de signe d'anomalie des petites voies aériennes, notamment pas de signe de piégeage."]
sample =NegNamedEntityRecogniser(text)
print(sample)
```
You can visualize the cue and the scope of the negation with the library spacy in a Jupyter notebook.
```python
import spacy
def visualize(sample, text):
colors = {'scope_neg': "#61ffab", "cue_neg": "#ff6961"}
options = {"ents": ['scope_neg', 'cue_neg'], "colors": colors}
for i in range(len(sample)):
entities = []
for ents in sample[i]:
entities.append({"end": ents["end"], "label": ents["entity_group"], "start": ents["start"]})
displacy.render({"ents": entities,"text": text[i]}, style="ent", manual=True,options=options, jupyter=True)
visualize(sample,text)
```
#### Limitations and bias
The capacity of the Neg-CamemBERT-bio model is constrained due to the limited size of its training set, which includes a restricted number of examples for certain negation indicators
*("sauf", "jamais", "hormis",...)* that appear less frequently. This limitation poses challenges for generalizing to other cases.
The tokenizer, not specifically designed for radiology, can lead to confusion for the Neg-Radio-CamemBERT-bio model when predicting the scope.
It is important to note that both the Radio corpus and the ESSAI + CAS corpus have been annotated by different annotators on purpose,
which may introduce confusion into the ability of the Neg-Medical-CamemBERT-bio model to predict this scope accurately. | [
"CAS",
"ESSAI",
"QUAERO"
] |
zhan1993/private_library_phi2_epoch_0 | zhan1993 | null | [
"region:us"
] | "2024-04-15T12:10:10Z" | 2024-04-19T14:39:15+00:00 | 0 | 0 | ---
{}
---
Number of experts present in the library: 263
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| sciq_Multiple_Choice | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora |
| wiki_hop_original_choose_best_object_interrogative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_1 | lora |
| squad_v2_0_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora |
| wiki_qa_exercise | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise | lora |
| race_high_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora |
| adversarial_qa_dbert_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora |
| quoref_Found_Context_Online | phi-2 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora |
| web_questions_get_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora |
| duorc_SelfRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| quarel_testing_students | phi-2 | sordonia/flan-10k-flat/quarel_testing_students | lora |
| qasc_qa_with_separated_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora |
| wiki_qa_Is_This_True_ | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora |
| race_high_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora |
| cot_gsm8k_ii | phi-2 | sordonia/flan-10k-flat/cot_gsm8k_ii | lora |
| gem_wiki_lingua_english_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora |
| unified_qa_science_inst | phi-2 | sordonia/flan-10k-flat/unified_qa_science_inst | lora |
| quartz_use_info_from_paragraph_question | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| wiki_hop_original_generate_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora |
| quoref_What_Is_The_Answer | phi-2 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora |
| adversarial_qa_droberta_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora |
| wiki_bio_comprehension | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora |
| adversarial_qa_dbidaf_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora |
| wiki_bio_what_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_what_content | lora |
| web_questions_whats_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora |
| wiqa_what_is_the_missing_first_step | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora |
| adversarial_qa_droberta_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora |
| ropes_plain_bottom_hint | phi-2 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora |
| kilt_tasks_hotpotqa_combining_facts | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| cos_e_v1_11_aligned_with_common_sense | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora |
| gem_web_nlg_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora |
| web_questions_potential_correct_answer | phi-2 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiki_qa_found_on_google | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora |
| duorc_ParaphraseRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_extract_answer | lora |
| wmt16_translate_de_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora |
| quail_no_prompt_id | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_id | lora |
| quoref_Guess_Title_For_Context | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora |
| duorc_SelfRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora |
| ropes_prompt_mix | phi-2 | sordonia/flan-10k-flat/ropes_prompt_mix | lora |
| adversarial_qa_droberta_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora |
| quail_context_question_answer_description_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora |
| gem_common_gen_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora |
| duorc_ParaphraseRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora |
| super_glue_cb_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora |
| cnn_dailymail_3_4_0 | phi-2 | sordonia/flan-10k-flat/cnn_dailymail_3_4_0 | lora |
| race_high_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| winogrande_1_1_0 | phi-2 | sordonia/flan-10k-flat/winogrande_1_1_0 | lora |
| duorc_SelfRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora |
| trec_1_0_0 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0 | lora |
| yelp_polarity_reviews_0_2_0 | phi-2 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| race_high_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora |
| para_crawl_enes | phi-2 | sordonia/flan-10k-flat/para_crawl_enes | lora |
| qasc_is_correct_1 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora |
| app_reviews_generate_review | phi-2 | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| ropes_read_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_read_background_situation | lora |
| dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora |
| stream_aqua | phi-2 | sordonia/flan-10k-flat/stream_aqua | lora |
| drop_2_0_0 | phi-2 | sordonia/flan-10k-flat/drop_2_0_0 | lora |
| wiki_hop_original_choose_best_object_affirmative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora |
| adversarial_qa_dbidaf_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora |
| social_i_qa_Generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora |
| stream_aqua_ii | phi-2 | sordonia/flan-10k-flat/stream_aqua_ii | lora |
| glue_sst2_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora |
| cot_esnli | phi-2 | sordonia/flan-10k-flat/cot_esnli | lora |
| race_high_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora |
| duorc_SelfRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora |
| cot_esnli_ii | phi-2 | sordonia/flan-10k-flat/cot_esnli_ii | lora |
| quail_no_prompt_text | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_text | lora |
| ropes_given_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_given_background_situation | lora |
| quarel_logic_test | phi-2 | sordonia/flan-10k-flat/quarel_logic_test | lora |
| adversarial_qa_dbidaf_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_based_on | lora |
| super_glue_copa_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora |
| cos_e_v1_11_i_think | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora |
| quail_context_question_description_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora |
| math_dataset_algebra__linear_1d_1_0_0 | phi-2 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| cosmos_qa_1_0_0 | phi-2 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora |
| wiqa_effect_with_label_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora |
| app_reviews_convert_to_star_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora |
| qasc_qa_with_separated_facts_2 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora |
| race_middle_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora |
| quartz_having_read_above_passage | phi-2 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora |
| glue_qqp_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| cos_e_v1_11_question_description_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora |
| cos_e_v1_11_question_option_description_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora |
| imdb_reviews_plain_text_1_0_0 | phi-2 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| natural_questions_open_1_0_0 | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora |
| wiqa_effect_with_string_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora |
| cos_e_v1_11_rationale | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora |
| race_middle_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora |
| wiki_bio_guess_person | phi-2 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| hellaswag_1_1_0 | phi-2 | sordonia/flan-10k-flat/hellaswag_1_1_0 | lora |
| wiqa_does_the_supposed_perturbation_have_an_effect | phi-2 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora |
| trivia_qa_rc_1_1_0 | phi-2 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| lambada_1_0_0 | phi-2 | sordonia/flan-10k-flat/lambada_1_0_0 | lora |
| quoref_Read_And_Extract_ | phi-2 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora |
| quail_context_description_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora |
| quail_context_description_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora |
| duorc_SelfRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora |
| cot_sensemaking_ii | phi-2 | sordonia/flan-10k-flat/cot_sensemaking_ii | lora |
| fix_punct | phi-2 | sordonia/flan-10k-flat/fix_punct | lora |
| squad_v1_1_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora |
| coqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/coqa_1_0_0 | lora |
| glue_qnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora |
| wiki_qa_Jeopardy_style | phi-2 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora |
| qasc_qa_with_separated_facts_5 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora |
| glue_mnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora |
| wiki_bio_key_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_key_content | lora |
| dream_generate_first_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora |
| quartz_read_passage_below_choose | phi-2 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora |
| web_questions_question_answer | phi-2 | sordonia/flan-10k-flat/web_questions_question_answer | lora |
| glue_stsb_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| wmt16_translate_tr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora |
| cot_qasc | phi-2 | sordonia/flan-10k-flat/cot_qasc | lora |
| duorc_ParaphraseRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora |
| quail_description_context_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_qa_Topic_Prediction_Question_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_Only | lora |
| quoref_Find_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Find_Answer | lora |
| social_i_qa_I_was_wondering | phi-2 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora |
| wiki_hop_original_choose_best_object_affirmative_3 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora |
| duorc_ParaphraseRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_build_story_around_qa | lora |
| qasc_qa_with_separated_facts_3 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora |
| race_middle_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora |
| paws_wiki_1_1_0 | phi-2 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora |
| app_reviews_categorize_rating_using_review | phi-2 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora |
| anli_r3_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora |
| app_reviews_convert_to_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora |
| wiqa_what_is_the_final_step_of_the_following_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| adversarial_qa_droberta_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora |
| wiki_qa_Decide_good_answer | phi-2 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora |
| adversarial_qa_dbert_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora |
| gem_dart_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora |
| adversarial_qa_dbert_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora |
| quarel_choose_between | phi-2 | sordonia/flan-10k-flat/quarel_choose_between | lora |
| duorc_ParaphraseRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora |
| wiki_hop_original_generate_subject | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject | lora |
| dream_baseline | phi-2 | sordonia/flan-10k-flat/dream_baseline | lora |
| cos_e_v1_11_question_description_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora |
| aeslc_1_0_0 | phi-2 | sordonia/flan-10k-flat/aeslc_1_0_0 | lora |
| anli_r2_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r2_0_1_0 | lora |
| dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_list_what_category_does_the_paragraph_belong_to | lora |
| quail_context_question_description_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora |
| race_middle_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora |
| wmt16_translate_ro_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora |
| race_high_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora |
| quail_description_context_question_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_text | lora |
| sciq_Direct_Question_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora |
| openbookqa_0_1_0 | phi-2 | sordonia/flan-10k-flat/openbookqa_0_1_0 | lora |
| duorc_SelfRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora |
| cot_gsm8k | phi-2 | sordonia/flan-10k-flat/cot_gsm8k | lora |
| quartz_answer_question_below | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_below | lora |
| snli_1_1_0 | phi-2 | sordonia/flan-10k-flat/snli_1_1_0 | lora |
| sciq_Multiple_Choice_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora |
| cot_strategyqa | phi-2 | sordonia/flan-10k-flat/cot_strategyqa | lora |
| qasc_qa_with_separated_facts_4 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora |
| ropes_prompt_bottom_no_hint | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora |
| duorc_SelfRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora |
| quartz_given_the_fact_answer_the_q | phi-2 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora |
| anli_r1_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| wiki_qa_Topic_Prediction_Question_and_Answer_Pair | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora |
| wiki_qa_Direct_Answer_to_Question | phi-2 | sordonia/flan-10k-flat/wiki_qa_Direct_Answer_to_Question | lora |
| qasc_is_correct_2 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora |
| wiki_hop_original_generate_subject_and_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora |
| ai2_arc_ARC_Challenge_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora |
| race_middle_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora |
| quail_context_question_answer_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora |
| quail_context_question_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_text | lora |
| wiki_hop_original_choose_best_object_interrogative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora |
| duorc_SelfRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora |
| quoref_Given_Context_Answer_Question | phi-2 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora |
| wiki_hop_original_explain_relation | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora |
| super_glue_record_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora |
| adversarial_qa_dbidaf_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_tell_what_it_is | lora |
| cot_ecqa_ii | phi-2 | sordonia/flan-10k-flat/cot_ecqa_ii | lora |
| ropes_background_new_situation_answer | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora |
| web_questions_short_general_knowledge_q | phi-2 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora |
| wiqa_what_might_be_the_first_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora |
| duorc_SelfRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora |
| ag_news_subset_1_0_0 | phi-2 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| race_middle_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora |
| wmt14_translate_fr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora |
| sciq_Direct_Question | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question | lora |
| super_glue_multirc_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| dbpedia_14_given_a_choice_of_categories_ | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora |
| super_glue_wic_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora |
| social_i_qa_Show_choices_and_generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| quoref_Answer_Question_Given_Context | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora |
| quoref_Context_Contains_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora |
| cos_e_v1_11_description_question_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora |
| adversarial_qa_dbert_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora |
| multi_news_1_0_0 | phi-2 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora |
| cos_e_v1_11_generate_explanation_given_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora |
| true_case | phi-2 | sordonia/flan-10k-flat/true_case | lora |
| duorc_ParaphraseRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora |
| quartz_answer_question_based_on | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora |
| bool_q_1_0_0 | phi-2 | sordonia/flan-10k-flat/bool_q_1_0_0 | lora |
| quoref_Guess_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora |
| quarel_do_not_use | phi-2 | sordonia/flan-10k-flat/quarel_do_not_use | lora |
| cos_e_v1_11_explain_why_human | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| wiki_qa_Generate_Question_from_Topic | phi-2 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora |
| kilt_tasks_hotpotqa_straighforward_qa | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora |
| adversarial_qa_dbidaf_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora |
| dbpedia_14_pick_one_category_for_the_following_text | phi-2 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora |
| kilt_tasks_hotpotqa_final_exam | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora |
| quoref_Answer_Friend_Question | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora |
| race_high_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora |
| ropes_prompt_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora |
| adversarial_qa_dbert_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora |
| cot_creak | phi-2 | sordonia/flan-10k-flat/cot_creak | lora |
| gem_e2e_nlg_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora |
| cos_e_v1_11_description_question_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora |
| social_i_qa_Generate_the_question_from_the_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| quarel_heres_a_story | phi-2 | sordonia/flan-10k-flat/quarel_heres_a_story | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | phi-2 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ropes_background_situation_middle | phi-2 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora |
| sciq_Multiple_Choice_Question_First | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora |
| cot_strategyqa_ii | phi-2 | sordonia/flan-10k-flat/cot_strategyqa_ii | lora |
| huggingface_xsum | phi-2 | sordonia/flan-10k-flat/huggingface_xsum | lora |
| kilt_tasks_hotpotqa_complex_question | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora |
| wmt16_translate_fi_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora |
| ai2_arc_ARC_Easy_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Easy_1_0_0 | lora |
| stream_qed | phi-2 | sordonia/flan-10k-flat/stream_qed | lora |
| definite_pronoun_resolution_1_1_0 | phi-2 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora |
| super_glue_rte_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| ropes_new_situation_background_answer | phi-2 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora |
| dream_read_the_following_conversation_and_answer_the_question | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora |
| cot_sensemaking | phi-2 | sordonia/flan-10k-flat/cot_sensemaking | lora |
| wiki_qa_Topic_Prediction_Answer_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora |
| duorc_ParaphraseRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question | lora |
| dream_generate_last_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora |
| race_middle_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora |
| piqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/piqa_1_0_0 | lora |
| cot_ecqa | phi-2 | sordonia/flan-10k-flat/cot_ecqa | lora |
| glue_mrpc_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora |
| race_middle_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora |
| ropes_plain_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora |
| quail_description_context_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora |
| qasc_qa_with_combined_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora |
| cot_creak_ii | phi-2 | sordonia/flan-10k-flat/cot_creak_ii | lora |
| duorc_ParaphraseRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora |
| quoref_Answer_Test | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Test | lora |
| wiki_bio_who | phi-2 | sordonia/flan-10k-flat/wiki_bio_who | lora |
| kilt_tasks_hotpotqa_formulate | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora |
| glue_wnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora |
| gigaword_1_2_0 | phi-2 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora |
| quail_context_description_question_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_text | lora |
| dream_answer_to_dialogue | phi-2 | sordonia/flan-10k-flat/dream_answer_to_dialogue | lora |
| cos_e_v1_11_question_option_description_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora |
| duorc_ParaphraseRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora |
| wiki_qa_automatic_system | phi-2 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora |
| adversarial_qa_droberta_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora |
| super_glue_wsc_fixed_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora |
| word_segment | phi-2 | sordonia/flan-10k-flat/word_segment | lora |
| quac_1_0_0 | phi-2 | sordonia/flan-10k-flat/quac_1_0_0 | lora |
| quartz_paragraph_question_plain_concat | phi-2 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora |
| wiqa_which_of_the_following_is_the_supposed_perturbation | phi-2 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora |
| quartz_use_info_from_question_paragraph | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora |
| ropes_plain_no_background | phi-2 | sordonia/flan-10k-flat/ropes_plain_no_background | lora |
| race_high_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora |
| glue_cola_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora |
| social_i_qa_Show_choices_and_generate_index | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora |
| ropes_prompt_bottom_hint_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora |
| stream_qed_ii | phi-2 | sordonia/flan-10k-flat/stream_qed_ii | lora |
Last updated on: 2024-04-19 14:36:04+00:00
| [
"SCIQ"
] |
RWKV/v5-EagleX-v2-7B-pth | RWKV | null | [
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:EleutherAI/pile",
"license:apache-2.0",
"region:us"
] | "2024-04-17T04:48:41Z" | 2024-05-02T22:02:31+00:00 | 0 | 0 | ---
datasets:
- cerebras/SlimPajama-627B
- EleutherAI/pile
language:
- en
license: apache-2.0
---
### RWKV EagleX 7B v2 Model
> **!Important!: This is not meant to be used with huggingface transformers library**
> [Use the Hugging Face varient instead, found here (v5-EagleX-v2-7B-HF)](https://huggingface.co/RWKV/v5-EagleX-v2-7B-HF)
>
> The following is the raw representation of the EagleX 7B v2 model. For use with our own set of trainers
>
>
> This is not an instruct tune model! (soon...)
## Quickstart with the hugging face transformer library
[See the huggingface version here (v5-EagleX-v2-7B-HF)](huggingface.co/RWKV/v5-EagleX-v2-7B-HF)
```
model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True)
```
## Evaluation
The following shows the progression of the model from 1.1T trained to 2.25T trained.
|Model |Eagle-7B-HF|EagleX-7B-HF-v1|EagleX-7B-HF-v2|
|----------------------|-----------|---------------|---------------|
|Param Count |7.52 B |7.52 B |7.52 B |
|Tokens Trained |1.1 T |1.7 T |2.25 T |
|avg_acc |0.4822 |0.5391 |0.5495 |
|glue (acc) |0.5752 |0.7463 |0.7439 |
|anli (acc) |0.3594 |0.4847 |0.5097 |
|mnli (acc) |0.3802 |0.7928 |0.7884 |
|mnli_mismatch (acc) |0.3687 |0.7985 |0.784 |
|swag (acc) |0.568 |0.5814 |0.5905 |
|lambada_standard (acc)|0.685 |0.686 |0.7004 |
|lambada_openai (acc) |0.7425 |0.7522 |0.7502 |
|mmlu (acc) |0.3321 |0.4014 |0.438 |
|winogrande (acc) |0.674 |0.7206 |0.7332 |
|wnli (acc) |0.4225 |0.4648 |0.493 |
|truthfulqa (acc) |0.3303 |0.3268 |0.3401 |
|logiqa (acc) |0.2458 |0.2458 |0.2458 |
|logiqa2 (acc) |0.2494 |0.2595 |0.2621 |
|sciq (acc) |0.955 |0.96 |0.93 |
|piqa (acc) |0.7704 |0.7758 |0.7764 |
|arc_easy (acc) |0.7382 |0.7555 |0.7445 |
|arc_challenge (acc) |0.3951 |0.4087 |0.4155 |
|hellaswag (acc) |0.5264 |0.5411 |0.56 |
|openbookqa (acc) |0.302 |0.296 |0.304 |
|mathqa (acc) |0.26 |0.26 |0.2593 |
|arithmetic (acc) |0.245 |0.0634 |0.1703 |
Compared against other top performing models in the same weight class.
|Model |OLMo-7B |falcon-7b |Llama-2-7b-hf|EagleX-7B-HF-v2|Mistral-7B-v0.1|
|----------------------|---------------|----------------|-------------|---------------|---------------|
|Param Count |6.89 B |6.92 B |6.74 B |7.52 B |7.24 B |
|Tokens Trained |2.5 T |1.5 T |2 T |2.25 T |2 - 7 T? |
|avg_acc |0.4578 |0.4775 |0.5045 |0.5495 |0.5676 |
|glue (acc) |0.474 |0.4578 |0.4289 |0.7439 |0.515 |
|anli (acc) |0.3478 |0.3541 |0.3697 |0.5097 |0.3803 |
|mnli (acc) |0.3294 |0.3893 |0.4269 |0.7884 |0.4542 |
|mnli_mismatch (acc) |0.3348 |0.404 |0.4395 |0.784 |0.4632 |
|swag (acc) |0.5512 |0.5685 |0.5658 |0.5905 |0.5756 |
|lambada_standard (acc)|0.6396 |0.6868 |0.6808 |0.7004 |0.6944 |
|lambada_openai (acc) |0.6872 |0.746 |0.7353 |0.7502 |0.7553 |
|mmlu (acc) |0.2812 |0.2512 |0.4077 |0.438 |0.5964 |
|winogrande (acc) |0.6725 |0.6709 |0.6914 |0.7332 |0.7364 |
|wnli (acc) |0.5775 |0.4789 |0.4648 |0.493 |0.5775 |
|truthfulqa (acc) |0.3015 |0.2826 |0.3205 |0.3401 |0.3537 |
|logiqa (acc) |0.2335 |0.2151 |0.2535 |0.2458 |0.2427 |
|logiqa2 (acc) |0.2506 |0.2252 |0.2564 |0.2621 |0.3022 |
|sciq (acc) |0.927 |0.944 |0.939 |0.93 |0.959 |
|piqa (acc) |0.7878 |0.7949 |0.7807 |0.7764 |0.8052 |
|arc_easy (acc) |0.7353 |0.7479 |0.7643 |0.7445 |0.8081 |
|arc_challenge (acc) |0.3677 |0.4027 |0.4309 |0.4155 |0.5009 |
|hellaswag (acc) |0.5572 |0.5772 |0.5713 |0.56 |0.6131 |
|openbookqa (acc) |0.292 |0.306 |0.316 |0.304 |0.33 |
|mathqa (acc) |0.26 |0.2884 |0.2801 |0.2593 |0.3554 |
|arithmetic (acc) |0.0069 |0.2367 |0.4703 |0.1703 |0.9004 |
See the following, for the full details on this model: [https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b)
## Links
- [Our wiki](https://wiki.rwkv.com)
- [Full eval data](https://docs.google.com/spreadsheets/d/1CBLU6yKkW-8FMvGD4INO3qjeHZ0qkKnZFcM6n6lWNOs/edit#gid=912381775)
- [Recursal.AI Cloud Platform](https://recursal.ai)
- [HF Gradio Demo](https://huggingface.co/spaces/RWKV/v5-EagleX-v2-7B-gradio)
- [Blog article, detailing our model launch](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b)
## Acknowledgement
We are grateful for the help and support from the following key groups:
- [Recursal.ai](https://recursal.ai) team for financing the GPU resources, and managing the training of this foundation model - you can run the Eagle line of RWKV models on their cloud / on-premise platform today.
- EleutherAI for their support, especially in the v5/v6 Eagle/Finch paper
- Linux Foundation AI & Data group for supporting and hosting the RWKV project | [
"SCIQ"
] |
apple/OpenELM | apple | null | [
"arxiv:2404.14619",
"license:other",
"region:us"
] | "2024-04-17T20:01:04Z" | 2024-05-02T00:54:23+00:00 | 0 | 1,429 | ---
license: other
license_name: apple-sample-code-license
license_link: LICENSE
---
# OpenELM: An Efficient Language Model Family with Open Training and Inference Framework
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce **OpenELM**, a family of **Open** **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.
Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
See the list below for the details of each model:
- [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)
- [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)
- [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)
- [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)
- [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct)
- [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct)
- [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct)
- [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)
```python
from transformers import AutoModelForCausalLM
openelm_270m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M", trust_remote_code=True)
openelm_450m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M", trust_remote_code=True)
openelm_1b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B", trust_remote_code=True)
openelm_3b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B", trust_remote_code=True)
openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True)
openelm_450m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M-Instruct", trust_remote_code=True)
openelm_1b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B-Instruct", trust_remote_code=True)
openelm_3b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B-Instruct", trust_remote_code=True)
```
## Usage
We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`.
You can try the model by running the following command:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
```
Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.
Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
```
Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL_NAME]
```
## Main Results
### Zero-Shot
| **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------|-----------|---------------|-----------|-----------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 26.45 | 45.08 | **53.98** | 46.71 | 69.75 | **84.70** | **53.91** | 54.37 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **30.55** | **46.68** | 48.56 | **52.07** | **70.78** | 84.40 | 52.72 | **55.11** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 27.56 | 48.06 | 55.78 | 53.97 | 72.31 | 87.20 | 58.01 | 57.56 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **30.38** | **50.00** | **60.37** | **59.34** | **72.63** | **88.00** | **58.96** | **59.95** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 32.34 | **55.43** | 63.58 | 64.81 | **75.57** | **90.60** | 61.72 | 63.44 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **37.97** | 52.23 | **70.00** | **71.20** | 75.03 | 89.30 | **62.75** | **65.50** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 35.58 | 59.89 | 67.40 | 72.44 | 78.24 | **92.70** | 65.51 | 67.39 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **39.42** | **61.74** | **68.17** | **76.36** | **79.00** | 92.50 | **66.85** | **69.15** |
### LLM360
| **Model Size** | **ARC-c** | **HellaSwag** | **MMLU** | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|---------------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | 47.15 | 25.72 | **39.24** | **53.83** | 38.72 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | **51.58** | **26.70** | 38.72 | 53.20 | **40.54** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | 53.86 | **26.01** | 40.18 | 57.22 | 41.50 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | **59.31** | 25.41 | **40.48** | **58.33** | **43.41** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | 65.71 | **27.05** | 36.98 | 63.22 | 45.93 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | **71.83** | 25.65 | **45.95** | **64.72** | **49.94** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | 73.28 | **26.76** | 34.98 | 67.25 | 48.90 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | **76.87** | 24.80 | **38.76** | **67.96** | **51.22** |
### OpenLLM Leaderboard
| **Model Size** | **ARC-c** | **CrowS-Pairs** | **HellaSwag** | **MMLU** | **PIQA** | **RACE** | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------------|---------------|-----------|-----------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | **66.79** | 47.15 | 25.72 | 69.75 | 30.91 | **39.24** | **53.83** | 45.13 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01 | **51.58** | **26.70** | **70.78** | 33.78 | 38.72 | 53.20 | **46.66** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | **68.63** | 53.86 | **26.01** | 72.31 | 33.11 | 40.18 | 57.22 | 47.69 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44 | **59.31** | 25.41 | **72.63** | **36.84** | **40.48** | **58.33** | **49.25** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | **71.74** | 65.71 | **27.05** | **75.57** | 36.46 | 36.98 | 63.22 | 51.68 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02 | **71.83** | 25.65 | 75.03 | **39.43** | **45.95** | **64.72** | **54.40** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | **73.29** | 73.28 | **26.76** | 78.24 | **38.76** | 34.98 | 67.25 | 54.35 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | 72.33 | **76.87** | 24.80 | **79.00** | 38.47 | **38.76** | **67.96** | **55.73** |
See the technical report for more results and comparison.
## Evaluation
### Setup
Install the following dependencies:
```bash
# install public lm-eval-harness
harness_repo="public-lm-eval-harness"
git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo}
cd ${harness_repo}
# use main branch on 03-15-2024, SHA is dc90fec
git checkout dc90fec
pip install -e .
cd ..
# 66d6242 is the main branch on 2024-04-01
pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242
pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0
```
### Evaluate OpenELM
```bash
# OpenELM-270M
hf_model=apple/OpenELM-270M
# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
tokenizer=meta-llama/Llama-2-7b-hf
add_bos_token=True
batch_size=1
mkdir lm_eval_output
shot=0
task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=5
task=mmlu,winogrande
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=25
task=arc_challenge,crows_pairs_english
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=10
task=hellaswag
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
```
## Bias, Risks, and Limitations
The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
## Citation
If you find our work useful, please cite:
```BibTex
@article{mehtaOpenELMEfficientLanguage2024,
title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}},
shorttitle = {{OpenELM}},
url = {https://arxiv.org/abs/2404.14619v1},
language = {en},
urldate = {2024-04-24},
journal = {arXiv.org},
author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
month = apr,
year = {2024},
}
@inproceedings{mehta2022cvnets,
author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
title = {CVNets: High Performance Library for Computer Vision},
year = {2022},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
series = {MM '22}
}
```
| [
"SCIQ"
] |
Subsets and Splits