sentence-transformers safetensors electra sentence-similarity feature-extraction generated_from_trainer dataset_size:7004 loss:SoftmaxLoss en dataset:PiC/phrase_similarity arxiv:1908.10084 base_model:google/electra-large-discriminator base_model:finetune:google/electra-large-discriminator model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/electra-large-discriminator
datasets:
- PiC/phrase_similarity
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7004
- loss:SoftmaxLoss
widget:
- source_sentence: Google SEO expert Matt Cutts had a similar experience, of the eight
    magazines and newspapers Cutts tried to order, he received zero.
  sentences:
  - He dissolved the services of her guards and her court attendants and seized an
    expansive reach of properties belonging to her.
  - Google SEO expert Matt Cutts had a comparable occurrence, of the eight magazines
    and newspapers Cutts tried to order, he received zero.
  - bill's newest solo play, "all over the map", premiered off broadway in april 2016,
    produced by all for an individual cinema.
- source_sentence: Shula said that Namath "beat our blitz" with his fast release,
    which let him quickly dump the football off to a receiver.
  sentences:
  - Shula said that Namath "beat our blitz" with his quick throw, which let him quickly
    dump the football off to a receiver.
  - it elects a single component of parliament (mp) by the first past the post system
    of election.
  - Matt Groening said that West was one of the most widely known group to ever come
    to the studio.
- source_sentence: When Angel calls out her name, Cordelia suddenly appears from the
    opposite side of the room saying, "Yep, that chick's in rough shape.
  sentences:
  - The ruined row of text, part of the Florida East Coast Railway, was repaired by
    2014 renewing freight train access to the port.
  - When Angel calls out her name, Cordelia suddenly appears from the opposite side
    of the room saying, "Yep, that chick's in approximate form.
  - Chaplin's films introduced a moderated kind of comedy than the typical Keystone
    farce, and he developed a large fan base.
- source_sentence: The following table shows the distances traversed by National Route
    11 in each different department, showing cities and towns that it passes by (or
    near).
  sentences:
  - The following table shows the distances traversed by National Route 11 in each
    separate city authority, showing cities and towns that it passes by (or near).
  - Similarly, indigenous communities and leaders practice as the main rule of law
    on local native lands and reserves.
  - later, sylvan mixed gary numan's albums "replicas" (with numan's previous band
    tubeway army) and "the quest for instant gratification".
- source_sentence: She wants to write about Keima but suffers a major case of writer's
    block.
  sentences:
  - In some countries, new extremist parties on the extreme opposite of left of the
    political spectrum arose, motivated through issues of immigration, multiculturalism
    and integration.
  - specific medical status of movement and the general condition of movement both
    are conditions under which contradictions can move.
  - She wants to write about Keima but suffers a huge occurrence of writer's block.
model-index:
- name: SentenceTransformer based on google/electra-large-discriminator
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: quora duplicates dev
      type: quora-duplicates-dev
    metrics:
    - type: cosine_accuracy
      value: 0.748
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.9737387895584106
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7604846225535881
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.9574624300003052
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.7120418848167539
      name: Cosine Precision
    - type: cosine_recall
      value: 0.816
      name: Cosine Recall
    - type: cosine_ap
      value: 0.786909093121924
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.667
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 275.4551696777344
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.733229329173167
      name: Dot F1
    - type: dot_f1_threshold
      value: 266.14727783203125
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.6010230179028133
      name: Dot Precision
    - type: dot_recall
      value: 0.94
      name: Dot Recall
    - type: dot_ap
      value: 0.5935392159238977
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.746
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 87.73857116699219
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7614678899082568
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 131.43374633789062
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.7033898305084746
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.83
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7904964653279406
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.747
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 4.5833892822265625
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7610121836925962
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 5.5540361404418945
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.7160493827160493
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.812
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.789806008641207
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.748
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 275.4551696777344
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7614678899082568
      name: Max F1
    - type: max_f1_threshold
      value: 266.14727783203125
      name: Max F1 Threshold
    - type: max_precision
      value: 0.7160493827160493
      name: Max Precision
    - type: max_recall
      value: 0.94
      name: Max Recall
    - type: max_ap
      value: 0.7904964653279406
      name: Max Ap
---

# SentenceTransformer based on google/electra-large-discriminator

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) on the [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) dataset. It maps sentences & paragraphs to a 1024-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:** [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) <!-- at revision c13c3df7efadc2162f42588bd28eb4e187d602a5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity)
- **Language:** en
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: ElectraModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)
```

## 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("Deehan1866/Electra")
# Run inference
sentences = [
    "She wants to write about Keima but suffers a major case of writer's block.",
    "She wants to write about Keima but suffers a huge occurrence of writer's block.",
    'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Binary Classification
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.748      |
| cosine_accuracy_threshold    | 0.9737     |
| cosine_f1                    | 0.7605     |
| cosine_f1_threshold          | 0.9575     |
| cosine_precision             | 0.712      |
| cosine_recall                | 0.816      |
| cosine_ap                    | 0.7869     |
| dot_accuracy                 | 0.667      |
| dot_accuracy_threshold       | 275.4552   |
| dot_f1                       | 0.7332     |
| dot_f1_threshold             | 266.1473   |
| dot_precision                | 0.601      |
| dot_recall                   | 0.94       |
| dot_ap                       | 0.5935     |
| manhattan_accuracy           | 0.746      |
| manhattan_accuracy_threshold | 87.7386    |
| manhattan_f1                 | 0.7615     |
| manhattan_f1_threshold       | 131.4337   |
| manhattan_precision          | 0.7034     |
| manhattan_recall             | 0.83       |
| manhattan_ap                 | 0.7905     |
| euclidean_accuracy           | 0.747      |
| euclidean_accuracy_threshold | 4.5834     |
| euclidean_f1                 | 0.761      |
| euclidean_f1_threshold       | 5.554      |
| euclidean_precision          | 0.716      |
| euclidean_recall             | 0.812      |
| euclidean_ap                 | 0.7898     |
| max_accuracy                 | 0.748      |
| max_accuracy_threshold       | 275.4552   |
| max_f1                       | 0.7615     |
| max_f1_threshold             | 266.1473   |
| max_precision                | 0.716      |
| max_recall                   | 0.94       |
| **max_ap**                   | **0.7905** |

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## Training Details

### Training Dataset

#### PiC/phrase_similarity

* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 7,004 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 26.35 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.89 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~48.80%</li><li>1: ~51.20%</li></ul> |
* Samples:
  | sentence1                                                                                                                                 | sentence2                                                                                                                                        | label          |
  |:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>0</code> |
  | <code>According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code>          | <code>According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code>   | <code>1</code> |
  | <code>Note that Fact 1 does not assume any particular structure on the set formula_65.</code>                                             | <code>Note that Fact 1 does not assume any specific edifice on the set formula_65.</code>                                                        | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### PiC/phrase_similarity

* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 26.21 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 26.8 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                   | sentence2                                                                                                                     | label          |
  |:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.</code>        | <code>after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.</code>   | <code>0</code> |
  | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.</code> | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.</code> | <code>0</code> |
  | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.</code>    | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.</code>           | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: False
- `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`: True
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | loss       | quora-duplicates-dev_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------------:|
| 0          | 0       | -             | -          | 0.6721                      |
| 0.2283     | 100     | -             | 0.6805     | 0.6847                      |
| **0.4566** | **200** | **-**         | **0.5313** | **0.7905**                  |
| 0.6849     | 300     | -             | 0.5383     | 0.7838                      |
| 0.9132     | 400     | -             | 0.6442     | 0.7585                      |
| 1.1416     | 500     | 0.5761        | 0.5742     | 0.7843                      |
| 1.3699     | 600     | -             | 0.5606     | 0.7558                      |
| 1.5982     | 700     | -             | 0.5716     | 0.7772                      |
| 1.8265     | 800     | -             | 0.5573     | 0.7619                      |
| 2.0548     | 900     | -             | 0.6951     | 0.7760                      |
| 2.2831     | 1000    | 0.3712        | 0.7678     | 0.7753                      |
| 2.5114     | 1100    | -             | 0.7712     | 0.7915                      |
| 2.7397     | 1200    | -             | 0.8120     | 0.7914                      |
| 2.9680     | 1300    | -             | 0.8045     | 0.7789                      |
| 3.1963     | 1400    | -             | 0.9936     | 0.7821                      |
| 3.4247     | 1500    | 0.1942        | 1.0883     | 0.7679                      |
| 3.6530     | 1600    | -             | 0.9814     | 0.7566                      |
| 3.8813     | 1700    | -             | 1.0897     | 0.7830                      |
| 4.1096     | 1800    | -             | 1.0764     | 0.7729                      |
| 4.3379     | 1900    | -             | 1.1209     | 0.7802                      |
| 4.5662     | 2000    | 0.1175        | 1.1522     | 0.7804                      |
| 4.7945     | 2100    | -             | 1.1545     | 0.7807                      |
| 5.0        | 2190    | -             | -          | 0.7905                      |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.2.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

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