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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:784827
- loss:ContrastiveLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: >-
Background: The study addresses the need for effective tools that allow both
novice and expert users to analyze the diversity of news coverage about
events. It highlights the importance of tailoring the interface to
accommodate non-expert users while also considering the insights of
journalism-savvy users, indicating a gap in existing systems that cater to
varying levels of expertise in news analysis.
Contribution: Combine 'a coordinated visualization interface tailored for
visualization non-expert users' and
sentences:
- a method considering lexical relationships
- cross-modality self-supervised learning via masked visual language modeling
- cognitive models of chaining
- source_sentence: >-
Background: Existing methods for anomaly detection on dynamic graphs
struggle with capturing complex time information in graph structures and
generating effective negative samples for unsupervised learning. These
challenges highlight the need for improved methodologies that can address
the limitations of current approaches in this field.
Contribution: Combine 'a message-passing framework' and
sentences:
- the grouping task
- a forecaster
- the optimisation algorithm producing the learnable model
- source_sentence: >-
Background: The accuracy of pixel flows is crucial for achieving
high-quality video enhancement, yet most prior works focus on estimating
dense flows that are generally less robust and computationally expensive.
This highlights a gap in existing methodologies that fail to prioritize
accuracy over density, necessitating a more efficient approach to flow
estimation for video enhancement tasks.
Contribution: Combine 'sparse point cloud data' and
sentences:
- a deep CNN
- a reinforcement learning view of the dialog generation task
- graphical models
- source_sentence: >-
Background: The optimal robot assembly planning problem is challenging due
to the necessity of finding the optimal solution amongst an exponentially
vast number of possible plans while satisfying a selection of constraints.
Traditional heuristic methods are limited as they are specific to a given
objective structure or set of problem parameters, indicating a need for more
versatile and effective approaches.
Contribution: 'pos[e] assembly sequencing' inspired by
sentences:
- 3D geometric neural field representation
- prompt learning
- gestures
- source_sentence: >-
Background: Patients find it difficult to use dexterous prosthetic hands
without a suitable control system, highlighting a need for improved grasp
performance and ease of operation. Existing methods may not adequately
address the challenges faced by users, particularly those with inferior
myoelectric signals, in effectively controlling prosthetic devices.
Contribution: Combine 'myoelectric signal' and
sentences:
- >-
a unified framework for collaborative decoding between large and small
language models (Large Language Models and small language models)
- image understanding
- joint biomedical entity linking and event extraction
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: cc
datasets:
- noystl/Recombination-Pred
language:
- en
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
"Background: Patients find it difficult to use dexterous prosthetic hands without a suitable control system, highlighting a need for improved grasp performance and ease of operation. Existing methods may not adequately address the challenges faced by users, particularly those with inferior myoelectric signals, in effectively controlling prosthetic devices.\nContribution: Combine 'myoelectric signal' and ",
'a unified framework for collaborative decoding between large and small language models (Large Language Models and small language models)',
'joint biomedical entity linking and event extraction',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### 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 Dataset
#### Unnamed Dataset
* Size: 784,827 training samples
* Columns: <code>query</code>, <code>answer</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | label |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 60 tokens</li><li>mean: 77.86 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.82 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>0: ~96.70%</li><li>1: ~3.30%</li></ul> |
* Samples:
| query | answer | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
| <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>a multilayer perceptron</code> | <code>1</code> |
| <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>synthetic occlusion augmentation during training</code> | <code>0</code> |
| <code>Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.<br>Contribution: Combine 'a Hidden Markov Model' and </code> | <code>robustness of deep learning methods</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `learning_rate`: 1.9218937402834593e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.08278167292320517
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1.9218937402834593e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.08278167292320517
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0082 | 100 | 0.0104 |
| 0.0163 | 200 | 0.0068 |
| 0.0245 | 300 | 0.005 |
| 0.0326 | 400 | 0.0041 |
| 0.0408 | 500 | 0.0054 |
| 0.0489 | 600 | 0.004 |
| 0.0571 | 700 | 0.0037 |
| 0.0652 | 800 | 0.0037 |
| 0.0734 | 900 | 0.0049 |
| 0.0815 | 1000 | 0.0038 |
| 0.0897 | 1100 | 0.004 |
| 0.0979 | 1200 | 0.0037 |
| 0.1060 | 1300 | 0.004 |
| 0.1142 | 1400 | 0.0049 |
| 0.1223 | 1500 | 0.0038 |
| 0.1305 | 1600 | 0.0036 |
| 0.1386 | 1700 | 0.0037 |
| 0.1468 | 1800 | 0.0045 |
| 0.1549 | 1900 | 0.0038 |
| 0.1631 | 2000 | 0.0034 |
| 0.1712 | 2100 | 0.0034 |
| 0.1794 | 2200 | 0.0035 |
| 0.1876 | 2300 | 0.0045 |
| 0.1957 | 2400 | 0.0036 |
| 0.2039 | 2500 | 0.0036 |
| 0.2120 | 2600 | 0.0033 |
| 0.2202 | 2700 | 0.004 |
| 0.2283 | 2800 | 0.0036 |
| 0.2365 | 2900 | 0.0033 |
| 0.2446 | 3000 | 0.0033 |
| 0.2528 | 3100 | 0.0037 |
| 0.2609 | 3200 | 0.0038 |
| 0.2691 | 3300 | 0.0033 |
| 0.2773 | 3400 | 0.0034 |
| 0.2854 | 3500 | 0.0033 |
| 0.2936 | 3600 | 0.0041 |
| 0.3017 | 3700 | 0.0033 |
| 0.3099 | 3800 | 0.0033 |
| 0.3180 | 3900 | 0.0032 |
| 0.3262 | 4000 | 0.004 |
| 0.3343 | 4100 | 0.0035 |
| 0.3425 | 4200 | 0.0031 |
| 0.3506 | 4300 | 0.0033 |
| 0.3588 | 4400 | 0.0033 |
| 0.3670 | 4500 | 0.0039 |
| 0.3751 | 4600 | 0.0032 |
| 0.3833 | 4700 | 0.0034 |
| 0.3914 | 4800 | 0.0031 |
| 0.3996 | 4900 | 0.004 |
| 0.4077 | 5000 | 0.0032 |
| 0.4159 | 5100 | 0.0031 |
| 0.4240 | 5200 | 0.0031 |
| 0.4322 | 5300 | 0.0032 |
| 0.4403 | 5400 | 0.0039 |
| 0.4485 | 5500 | 0.0031 |
| 0.4567 | 5600 | 0.003 |
| 0.4648 | 5700 | 0.0032 |
| 0.4730 | 5800 | 0.0038 |
| 0.4811 | 5900 | 0.0033 |
| 0.4893 | 6000 | 0.0031 |
| 0.4974 | 6100 | 0.0032 |
| 0.5056 | 6200 | 0.0033 |
| 0.5137 | 6300 | 0.0033 |
| 0.5219 | 6400 | 0.0032 |
| 0.5300 | 6500 | 0.0031 |
| 0.5382 | 6600 | 0.0032 |
| 0.5464 | 6700 | 0.0038 |
| 0.5545 | 6800 | 0.003 |
| 0.5627 | 6900 | 0.003 |
| 0.5708 | 7000 | 0.0029 |
| 0.5790 | 7100 | 0.0038 |
| 0.5871 | 7200 | 0.0032 |
| 0.5953 | 7300 | 0.0031 |
| 0.6034 | 7400 | 0.003 |
| 0.6116 | 7500 | 0.003 |
| 0.6198 | 7600 | 0.0039 |
| 0.6279 | 7700 | 0.0031 |
| 0.6361 | 7800 | 0.0031 |
| 0.6442 | 7900 | 0.0031 |
| 0.6524 | 8000 | 0.0039 |
| 0.6605 | 8100 | 0.003 |
| 0.6687 | 8200 | 0.003 |
| 0.6768 | 8300 | 0.003 |
| 0.6850 | 8400 | 0.0028 |
| 0.6931 | 8500 | 0.0035 |
| 0.7013 | 8600 | 0.0031 |
| 0.7095 | 8700 | 0.003 |
| 0.7176 | 8800 | 0.0026 |
| 0.7258 | 8900 | 0.0034 |
| 0.7339 | 9000 | 0.0033 |
| 0.7421 | 9100 | 0.003 |
| 0.7502 | 9200 | 0.0027 |
| 0.7584 | 9300 | 0.0029 |
| 0.7665 | 9400 | 0.0034 |
| 0.7747 | 9500 | 0.0029 |
| 0.7828 | 9600 | 0.0028 |
| 0.7910 | 9700 | 0.0027 |
| 0.7992 | 9800 | 0.0033 |
| 0.8073 | 9900 | 0.0031 |
| 0.8155 | 10000 | 0.0029 |
| 0.8236 | 10100 | 0.0028 |
| 0.8318 | 10200 | 0.0031 |
| 0.8399 | 10300 | 0.0031 |
| 0.8481 | 10400 | 0.003 |
| 0.8562 | 10500 | 0.0029 |
| 0.8644 | 10600 | 0.0028 |
| 0.8725 | 10700 | 0.0033 |
| 0.8807 | 10800 | 0.003 |
| 0.8889 | 10900 | 0.0029 |
| 0.8970 | 11000 | 0.0027 |
| 0.9052 | 11100 | 0.0033 |
| 0.9133 | 11200 | 0.0029 |
| 0.9215 | 11300 | 0.0029 |
| 0.9296 | 11400 | 0.0029 |
| 0.9378 | 11500 | 0.003 |
| 0.9459 | 11600 | 0.0034 |
| 0.9541 | 11700 | 0.0031 |
| 0.9622 | 11800 | 0.0027 |
| 0.9704 | 11900 | 0.0029 |
| 0.9786 | 12000 | 0.0034 |
| 0.9867 | 12100 | 0.0032 |
| 0.9949 | 12200 | 0.003 |
| 1.0030 | 12300 | 0.0032 |
| 1.0112 | 12400 | 0.0028 |
| 1.0193 | 12500 | 0.003 |
| 1.0275 | 12600 | 0.0027 |
| 1.0356 | 12700 | 0.0034 |
| 1.0438 | 12800 | 0.0029 |
| 1.0519 | 12900 | 0.0025 |
| 1.0601 | 13000 | 0.0028 |
| 1.0683 | 13100 | 0.0026 |
| 1.0764 | 13200 | 0.0035 |
| 1.0846 | 13300 | 0.0026 |
| 1.0927 | 13400 | 0.0028 |
| 1.1009 | 13500 | 0.0026 |
| 1.1090 | 13600 | 0.0034 |
| 1.1172 | 13700 | 0.0028 |
| 1.1253 | 13800 | 0.0027 |
| 1.1335 | 13900 | 0.0026 |
| 1.1416 | 14000 | 0.0031 |
| 1.1498 | 14100 | 0.0025 |
| 1.1580 | 14200 | 0.0025 |
| 1.1661 | 14300 | 0.0025 |
| 1.1743 | 14400 | 0.0024 |
| 1.1824 | 14500 | 0.0031 |
| 1.1906 | 14600 | 0.0025 |
| 1.1987 | 14700 | 0.0024 |
| 1.2069 | 14800 | 0.0025 |
| 1.2150 | 14900 | 0.0029 |
| 1.2232 | 15000 | 0.0025 |
| 1.2313 | 15100 | 0.0025 |
| 1.2395 | 15200 | 0.0023 |
| 1.2477 | 15300 | 0.0024 |
| 1.2558 | 15400 | 0.0029 |
| 1.2640 | 15500 | 0.0023 |
| 1.2721 | 15600 | 0.0023 |
| 1.2803 | 15700 | 0.0023 |
| 1.2884 | 15800 | 0.0032 |
| 1.2966 | 15900 | 0.0023 |
| 1.3047 | 16000 | 0.0023 |
| 1.3129 | 16100 | 0.0024 |
| 1.3210 | 16200 | 0.0025 |
| 1.3292 | 16300 | 0.0028 |
| 1.3374 | 16400 | 0.0023 |
| 1.3455 | 16500 | 0.0021 |
| 1.3537 | 16600 | 0.0023 |
| 1.3618 | 16700 | 0.0029 |
| 1.3700 | 16800 | 0.0023 |
| 1.3781 | 16900 | 0.0023 |
| 1.3863 | 17000 | 0.0025 |
| 1.3944 | 17100 | 0.0028 |
| 1.4026 | 17200 | 0.0023 |
| 1.4107 | 17300 | 0.0023 |
| 1.4189 | 17400 | 0.0023 |
| 1.4271 | 17500 | 0.0023 |
| 1.4352 | 17600 | 0.0029 |
| 1.4434 | 17700 | 0.0022 |
| 1.4515 | 17800 | 0.0022 |
| 1.4597 | 17900 | 0.0023 |
| 1.4678 | 18000 | 0.0026 |
| 1.4760 | 18100 | 0.0024 |
| 1.4841 | 18200 | 0.0023 |
| 1.4923 | 18300 | 0.0024 |
| 1.5004 | 18400 | 0.0024 |
| 1.5086 | 18500 | 0.0026 |
| 1.5168 | 18600 | 0.0022 |
| 1.5249 | 18700 | 0.0023 |
| 1.5331 | 18800 | 0.0023 |
| 1.5412 | 18900 | 0.003 |
| 1.5494 | 19000 | 0.002 |
| 1.5575 | 19100 | 0.0022 |
| 1.5657 | 19200 | 0.0023 |
| 1.5738 | 19300 | 0.0023 |
| 1.5820 | 19400 | 0.0028 |
| 1.5901 | 19500 | 0.0022 |
| 1.5983 | 19600 | 0.0023 |
| 1.6065 | 19700 | 0.0022 |
| 1.6146 | 19800 | 0.0028 |
| 1.6228 | 19900 | 0.0022 |
| 1.6309 | 20000 | 0.0023 |
| 1.6391 | 20100 | 0.0025 |
| 1.6472 | 20200 | 0.0028 |
| 1.6554 | 20300 | 0.0023 |
| 1.6635 | 20400 | 0.0021 |
| 1.6717 | 20500 | 0.0022 |
| 1.6798 | 20600 | 0.0022 |
| 1.6880 | 20700 | 0.0025 |
| 1.6962 | 20800 | 0.0024 |
| 1.7043 | 20900 | 0.0023 |
| 1.7125 | 21000 | 0.0021 |
| 1.7206 | 21100 | 0.0024 |
| 1.7288 | 21200 | 0.0024 |
| 1.7369 | 21300 | 0.0023 |
| 1.7451 | 21400 | 0.0022 |
| 1.7532 | 21500 | 0.0021 |
| 1.7614 | 21600 | 0.0025 |
| 1.7696 | 21700 | 0.0023 |
| 1.7777 | 21800 | 0.002 |
| 1.7859 | 21900 | 0.0022 |
| 1.7940 | 22000 | 0.0025 |
| 1.8022 | 22100 | 0.0022 |
| 1.8103 | 22200 | 0.0023 |
| 1.8185 | 22300 | 0.0022 |
| 1.8266 | 22400 | 0.0021 |
| 1.8348 | 22500 | 0.0025 |
| 1.8429 | 22600 | 0.0025 |
| 1.8511 | 22700 | 0.0022 |
| 1.8593 | 22800 | 0.0023 |
| 1.8674 | 22900 | 0.0026 |
| 1.8756 | 23000 | 0.0022 |
| 1.8837 | 23100 | 0.0022 |
| 1.8919 | 23200 | 0.0022 |
| 1.9000 | 23300 | 0.0024 |
| 1.9082 | 23400 | 0.0022 |
| 1.9163 | 23500 | 0.0022 |
| 1.9245 | 23600 | 0.0023 |
| 1.9326 | 23700 | 0.0023 |
| 1.9408 | 23800 | 0.0027 |
| 1.9490 | 23900 | 0.0023 |
| 1.9571 | 24000 | 0.0023 |
| 1.9653 | 24100 | 0.0022 |
| 1.9734 | 24200 | 0.0027 |
| 1.9816 | 24300 | 0.0025 |
| 1.9897 | 24400 | 0.0023 |
| 1.9979 | 24500 | 0.0025 |
</details>
### Framework Versions
- Python: 3.11.2
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.0.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
```bibtex
@misc{sternlicht2025chimeraknowledgebaseidea,
title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature},
author={Noy Sternlicht and Tom Hope},
year={2025},
eprint={2505.20779},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.20779},
}
```
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
**Quick Links**
- 🌐 [Project](https://noy-sternlicht.github.io/CHIMERA-Web)
- 📃 [Paper](https://arxiv.org/abs/2505.20779)
- 🛠️ [Code](https://github.com/noy-sternlicht/CHIMERA-KB)
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