README.md

---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-xsmall
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- 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
widget:
- source_sentence: No, monsieur.
  sentences:
  - Yes, sir.
  - Look, there's a legend here.
  - All models are subject to analysis.
- source_sentence: She shrugged.
  sentences:
  - She acted like it didn't matter.
  - He felt bad for doubting her.
  - Jacques Teti movies are my favorite.
- source_sentence: We can think.
  sentences:
  - We need to think.
  - A man is on his way to work.
  - Her favorite candy is chocolate.
- source_sentence: He loved her.
  sentences:
  - She was loved by him.
  - The person is playing rugby.
  - All models are subject to analysis.
- source_sentence: in each square
  sentences:
  - It is widespread.
  - A young girl flips an omelet.
  - He charged Jon with a knife.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-xsmall
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.7972304062599285
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8069984848350104
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8078500467589406
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8072286629818308
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8083747460970299
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.807329204776433
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7028547677818588
      name: Pearson Dot
    - type: spearman_dot
      value: 0.690944321229592
      name: Spearman Dot
    - type: pearson_max
      value: 0.8083747460970299
      name: Pearson Max
    - type: spearman_max
      value: 0.807329204776433
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.677155205095155
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7285403609275818
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7186860786908915
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6111028790473938
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6110485933503836
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8723528552650796
      name: Cosine Recall
    - type: cosine_ap
      value: 0.73917897685454
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6382591553567367
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 228.40408325195312
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.706771220880316
      name: Dot F1
    - type: dot_f1_threshold
      value: 177.3942108154297
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5811370481927711
      name: Dot Precision
    - type: dot_recall
      value: 0.9017087775668176
      name: Dot Recall
    - type: dot_ap
      value: 0.6903597943138529
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.6635074683448328
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 174.62747192382812
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7054413268204022
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 232.6788330078125
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5771911887721908
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.906966554695487
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7282119371967055
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6650997042990371
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 13.422540664672852
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7067711563398544
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 17.634807586669922
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5755739210284665
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9154374178472323
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.730311832588485
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.677155205095155
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 228.40408325195312
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7186860786908915
      name: Max F1
    - type: max_f1_threshold
      value: 232.6788330078125
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6110485933503836
      name: Max Precision
    - type: max_recall
      value: 0.9154374178472323
      name: Max Recall
    - type: max_ap
      value: 0.73917897685454
      name: Max Ap
---

# SentenceTransformer based on microsoft/deberta-v3-xsmall

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 384-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:** [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) <!-- at revision 4b419818330868dff6a60ad3e6b1c730f8b8c0c6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **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: DebertaV2Model 
  (1): Pooling({'word_embedding_dimension': 384, '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("bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03")
# Run inference
sentences = [
    'in each square',
    'It is widespread.',
    'A young girl flips an omelet.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.7972    |
| **spearman_cosine** | **0.807** |
| pearson_manhattan   | 0.8079    |
| spearman_manhattan  | 0.8072    |
| pearson_euclidean   | 0.8084    |
| spearman_euclidean  | 0.8073    |
| pearson_dot         | 0.7029    |
| spearman_dot        | 0.6909    |
| pearson_max         | 0.8084    |
| spearman_max        | 0.8073    |

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.6772     |
| cosine_accuracy_threshold    | 0.7285     |
| cosine_f1                    | 0.7187     |
| cosine_f1_threshold          | 0.6111     |
| cosine_precision             | 0.611      |
| cosine_recall                | 0.8724     |
| cosine_ap                    | 0.7392     |
| dot_accuracy                 | 0.6383     |
| dot_accuracy_threshold       | 228.4041   |
| dot_f1                       | 0.7068     |
| dot_f1_threshold             | 177.3942   |
| dot_precision                | 0.5811     |
| dot_recall                   | 0.9017     |
| dot_ap                       | 0.6904     |
| manhattan_accuracy           | 0.6635     |
| manhattan_accuracy_threshold | 174.6275   |
| manhattan_f1                 | 0.7054     |
| manhattan_f1_threshold       | 232.6788   |
| manhattan_precision          | 0.5772     |
| manhattan_recall             | 0.907      |
| manhattan_ap                 | 0.7282     |
| euclidean_accuracy           | 0.6651     |
| euclidean_accuracy_threshold | 13.4225    |
| euclidean_f1                 | 0.7068     |
| euclidean_f1_threshold       | 17.6348    |
| euclidean_precision          | 0.5756     |
| euclidean_recall             | 0.9154     |
| euclidean_ap                 | 0.7303     |
| max_accuracy                 | 0.6772     |
| max_accuracy_threshold       | 228.4041   |
| max_f1                       | 0.7187     |
| max_f1_threshold             | 232.6788   |
| max_precision                | 0.611      |
| max_recall                   | 0.9154     |
| **max_ap**                   | **0.7392** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 314,315 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: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                  | sentence2                                        | label          |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>0</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>0</code> |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### sentence-transformers/stsb

* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 7.5e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n
- `hub_strategy`: checkpoint

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 7.5e-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`: 0.25
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n
- `hub_strategy`: checkpoint
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | max_ap | sts-dev_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:------:|:-----------------------:|
| None   | 0    | -             | 3.7624 | 0.5721 | 0.4168                  |
| 0.0501 | 246  | 3.3825        | -      | -      | -                       |
| 0.1002 | 492  | 1.8307        | -      | -      | -                       |
| 0.1500 | 737  | -             | 1.0084 | 0.7024 | -                       |
| 0.1502 | 738  | 1.055         | -      | -      | -                       |
| 0.2003 | 984  | 0.7961        | -      | -      | -                       |
| 0.2504 | 1230 | 0.6859        | -      | -      | -                       |
| 0.3001 | 1474 | -             | 0.7410 | 0.7191 | -                       |
| 0.3005 | 1476 | 0.5914        | -      | -      | -                       |
| 0.3506 | 1722 | 0.5324        | -      | -      | -                       |
| 0.4007 | 1968 | 0.5077        | -      | -      | -                       |
| 0.4501 | 2211 | -             | 0.6152 | 0.7144 | -                       |
| 0.4507 | 2214 | 0.4647        | -      | -      | -                       |
| 0.5008 | 2460 | 0.4443        | -      | -      | -                       |
| 0.5509 | 2706 | 0.4169        | -      | -      | -                       |
| 0.6002 | 2948 | -             | 0.5820 | 0.7207 | -                       |
| 0.6010 | 2952 | 0.3831        | -      | -      | -                       |
| 0.6511 | 3198 | 0.393         | -      | -      | -                       |
| 0.7011 | 3444 | 0.3654        | -      | -      | -                       |
| 0.7502 | 3685 | -             | 0.5284 | 0.7264 | -                       |
| 0.7512 | 3690 | 0.344         | -      | -      | -                       |
| 0.8013 | 3936 | 0.3336        | -      | -      | -                       |
| 0.8514 | 4182 | 0.3382        | -      | -      | -                       |
| 0.9002 | 4422 | -             | 0.4911 | 0.7294 | -                       |
| 0.9015 | 4428 | 0.3182        | -      | -      | -                       |
| 0.9515 | 4674 | 0.3213        | -      | -      | -                       |
| 1.0016 | 4920 | 0.3032        | -      | -      | -                       |
| 1.0503 | 5159 | -             | 0.4777 | 0.7325 | -                       |
| 1.0517 | 5166 | 0.2526        | -      | -      | -                       |
| 1.1018 | 5412 | 0.2652        | -      | -      | -                       |
| 1.1519 | 5658 | 0.2538        | -      | -      | -                       |
| 1.2003 | 5896 | -             | 0.4569 | 0.7331 | -                       |
| 1.2020 | 5904 | 0.2454        | -      | -      | -                       |
| 1.2520 | 6150 | 0.2528        | -      | -      | -                       |
| 1.3021 | 6396 | 0.2448        | -      | -      | -                       |
| 1.3504 | 6633 | -             | 0.4334 | 0.7370 | -                       |
| 1.3522 | 6642 | 0.2282        | -      | -      | -                       |
| 1.4023 | 6888 | 0.2295        | -      | -      | -                       |
| 1.4524 | 7134 | 0.2313        | -      | -      | -                       |
| 1.5004 | 7370 | -             | 0.4237 | 0.7342 | -                       |
| 1.5024 | 7380 | 0.2218        | -      | -      | -                       |
| 1.5525 | 7626 | 0.2246        | -      | -      | -                       |
| 1.6026 | 7872 | 0.218         | -      | -      | -                       |
| 1.6504 | 8107 | -             | 0.4102 | 0.7388 | -                       |
| 1.6527 | 8118 | 0.2095        | -      | -      | -                       |
| 1.7028 | 8364 | 0.2114        | -      | -      | -                       |
| 1.7529 | 8610 | 0.2063        | -      | -      | -                       |
| 1.8005 | 8844 | -             | 0.4075 | 0.7370 | -                       |
| 1.8029 | 8856 | 0.1968        | -      | -      | -                       |
| 1.8530 | 9102 | 0.2061        | -      | -      | -                       |
| 1.9031 | 9348 | 0.2089        | -      | -      | -                       |
| 1.9505 | 9581 | -             | 0.3978 | 0.7395 | -                       |
| 1.9532 | 9594 | 0.2005        | -      | -      | -                       |
| 2.0    | 9824 | -             | 0.3963 | 0.7392 | -                       |
| None   | 0    | -             | 1.5506 | -      | 0.8070                  |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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