---
base_model: intfloat/multilingual-e5-small
datasets: []
language: []
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:916
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Ways to enhance memory retention
  sentences:
  - How to improve memory?
  - What is the currency of China?
  - How do I replace a flat tire?
- source_sentence: Why it's essential to maintain a balanced diet
  sentences:
  - What is the importance of a balanced diet?
  - What is the population of Canada?
  - How to create a website from scratch?
- source_sentence: What is the chemical formula for methanol?
  sentences:
  - What is the highest mountain in North America?
  - What are the advantages of electric cars over gasoline vehicles?
  - What is the chemical formula for ethanol?
- source_sentence: How is photosynthesis carried out?
  sentences:
  - What is the currency of the United States?
  - What is the capital of Norway?
  - How does photosynthesis work?
- source_sentence: How is the weather today?
  sentences:
  - Who invented the airplane?
  - What is the weather like today?
  - Who was the first female Prime Minister of the UK?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-small
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class dev
      type: pair-class-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9388646288209607
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7886800765991211
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9411764705882353
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7886800765991211
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9572649572649573
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9256198347107438
      name: Cosine Recall
    - type: cosine_ap
      value: 0.973954773457217
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9388646288209607
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.7886800765991211
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9411764705882353
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.7886800765991211
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9572649572649573
      name: Dot Precision
    - type: dot_recall
      value: 0.9256198347107438
      name: Dot Recall
    - type: dot_ap
      value: 0.973954773457217
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9388646288209607
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 10.132380485534668
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9411764705882353
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 10.132380485534668
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9572649572649573
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9256198347107438
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9728889947842537
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9388646288209607
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.6500871777534485
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9411764705882353
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.6500871777534485
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9572649572649573
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9256198347107438
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.973954773457217
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9388646288209607
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 10.132380485534668
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9411764705882353
      name: Max F1
    - type: max_f1_threshold
      value: 10.132380485534668
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9572649572649573
      name: Max Precision
    - type: max_recall
      value: 0.9256198347107438
      name: Max Recall
    - type: max_ap
      value: 0.973954773457217
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class test
      type: pair-class-test
    metrics:
    - type: cosine_accuracy
      value: 0.9388646288209607
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8207830190658569
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9421487603305785
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8207830190658569
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9421487603305785
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9421487603305785
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9731728800864022
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9388646288209607
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.8207829594612122
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9421487603305785
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.8207829594612122
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9421487603305785
      name: Dot Precision
    - type: dot_recall
      value: 0.9421487603305785
      name: Dot Recall
    - type: dot_ap
      value: 0.9731728800864022
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9344978165938864
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 9.387104988098145
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9382716049382717
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 9.516077041625977
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9344262295081968
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9421487603305785
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9720713665843098
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9388646288209607
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5986893177032471
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9421487603305785
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5986893177032471
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9421487603305785
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9421487603305785
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9731728800864022
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9388646288209607
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 9.387104988098145
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9421487603305785
      name: Max F1
    - type: max_f1_threshold
      value: 9.516077041625977
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9421487603305785
      name: Max Precision
    - type: max_recall
      value: 0.9421487603305785
      name: Max Recall
    - type: max_ap
      value: 0.9731728800864022
      name: Max Ap
---

# SentenceTransformer based on intfloat/multilingual-e5-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
  (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("srikarvar/fine_tuned_model_1")
# Run inference
sentences = [
    'How is the weather today?',
    'What is the weather like today?',
    'Who was the first female Prime Minister of the UK?',
]
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

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

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

| Metric                       | Value     |
|:-----------------------------|:----------|
| cosine_accuracy              | 0.9389    |
| cosine_accuracy_threshold    | 0.7887    |
| cosine_f1                    | 0.9412    |
| cosine_f1_threshold          | 0.7887    |
| cosine_precision             | 0.9573    |
| cosine_recall                | 0.9256    |
| cosine_ap                    | 0.974     |
| dot_accuracy                 | 0.9389    |
| dot_accuracy_threshold       | 0.7887    |
| dot_f1                       | 0.9412    |
| dot_f1_threshold             | 0.7887    |
| dot_precision                | 0.9573    |
| dot_recall                   | 0.9256    |
| dot_ap                       | 0.974     |
| manhattan_accuracy           | 0.9389    |
| manhattan_accuracy_threshold | 10.1324   |
| manhattan_f1                 | 0.9412    |
| manhattan_f1_threshold       | 10.1324   |
| manhattan_precision          | 0.9573    |
| manhattan_recall             | 0.9256    |
| manhattan_ap                 | 0.9729    |
| euclidean_accuracy           | 0.9389    |
| euclidean_accuracy_threshold | 0.6501    |
| euclidean_f1                 | 0.9412    |
| euclidean_f1_threshold       | 0.6501    |
| euclidean_precision          | 0.9573    |
| euclidean_recall             | 0.9256    |
| euclidean_ap                 | 0.974     |
| max_accuracy                 | 0.9389    |
| max_accuracy_threshold       | 10.1324   |
| max_f1                       | 0.9412    |
| max_f1_threshold             | 10.1324   |
| max_precision                | 0.9573    |
| max_recall                   | 0.9256    |
| **max_ap**                   | **0.974** |

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

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.9389     |
| cosine_accuracy_threshold    | 0.8208     |
| cosine_f1                    | 0.9421     |
| cosine_f1_threshold          | 0.8208     |
| cosine_precision             | 0.9421     |
| cosine_recall                | 0.9421     |
| cosine_ap                    | 0.9732     |
| dot_accuracy                 | 0.9389     |
| dot_accuracy_threshold       | 0.8208     |
| dot_f1                       | 0.9421     |
| dot_f1_threshold             | 0.8208     |
| dot_precision                | 0.9421     |
| dot_recall                   | 0.9421     |
| dot_ap                       | 0.9732     |
| manhattan_accuracy           | 0.9345     |
| manhattan_accuracy_threshold | 9.3871     |
| manhattan_f1                 | 0.9383     |
| manhattan_f1_threshold       | 9.5161     |
| manhattan_precision          | 0.9344     |
| manhattan_recall             | 0.9421     |
| manhattan_ap                 | 0.9721     |
| euclidean_accuracy           | 0.9389     |
| euclidean_accuracy_threshold | 0.5987     |
| euclidean_f1                 | 0.9421     |
| euclidean_f1_threshold       | 0.5987     |
| euclidean_precision          | 0.9421     |
| euclidean_recall             | 0.9421     |
| euclidean_ap                 | 0.9732     |
| max_accuracy                 | 0.9389     |
| max_accuracy_threshold       | 9.3871     |
| max_f1                       | 0.9421     |
| max_f1_threshold             | 9.5161     |
| max_precision                | 0.9421     |
| max_recall                   | 0.9421     |
| **max_ap**                   | **0.9732** |

<!--
## 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

#### Unnamed Dataset


* Size: 916 training samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
  |         | label                                           | sentence2                                                                         | sentence1                                                                         |
  |:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | int                                             | string                                                                            | string                                                                            |
  | details | <ul><li>0: ~49.56%</li><li>1: ~50.44%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.32 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.92 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | label          | sentence2                                                            | sentence1                                            |
  |:---------------|:---------------------------------------------------------------------|:-----------------------------------------------------|
  | <code>1</code> | <code>What are the potential side effects of this medication?</code> | <code>What are the side effects of this drug?</code> |
  | <code>0</code> | <code>How to fix a torn pocket?</code>                               | <code>How to fix a broken zipper?</code>             |
  | <code>0</code> | <code>How to make a chocolate chip cookie dough?</code>              | <code>How to bake a chocolate chip cookie?</code>    |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 229 evaluation samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
  |         | label                                           | sentence2                                                                        | sentence1                                                                         |
  |:--------|:------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | int                                             | string                                                                           | string                                                                            |
  | details | <ul><li>0: ~47.16%</li><li>1: ~52.84%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.95 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
  | label          | sentence2                                                     | sentence1                                          |
  |:---------------|:--------------------------------------------------------------|:---------------------------------------------------|
  | <code>0</code> | <code>What methods are used to measure a nation's GDP?</code> | <code>How is the GDP of a country measured?</code> |
  | <code>0</code> | <code>What is the currency of Japan?</code>                   | <code>What is the currency of China?</code>        |
  | <code>1</code> | <code>Steps to cultivate tomatoes at home</code>              | <code>How to grow tomatoes in a garden?</code>     |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `weight_decay`: 0.01
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `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_fused
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | loss       | pair-class-dev_max_ap | pair-class-test_max_ap |
|:----------:|:------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0          | 0      | -             | -          | 0.7130                | -                      |
| 0.6897     | 10     | 3.0972        | -          | -                     | -                      |
| 1.0345     | 15     | -             | 0.8033     | 0.9272                | -                      |
| 1.3448     | 20     | 1.0451        | -          | -                     | -                      |
| 2.0345     | 30     | 0.5786        | 0.4910     | 0.9680                | -                      |
| 2.6897     | 40     | 0.2996        | -          | -                     | -                      |
| 3.0345     | 45     | -             | 0.4487     | 0.9731                | -                      |
| 3.3448     | 50     | 0.0901        | -          | -                     | -                      |
| **4.0345** | **60** | **0.067**     | **0.4115** | **0.9732**            | **-**                  |
| 4.6897     | 70     | 0.0729        | -          | -                     | -                      |
| 5.0345     | 75     | -             | 0.4543     | 0.9727                | -                      |
| 5.3448     | 80     | 0.0453        | -          | -                     | -                      |
| 6.0345     | 90     | 0.0637        | 0.4249     | 0.9736                | -                      |
| 6.6897     | 100    | 0.0388        | -          | -                     | -                      |
| 7.0345     | 105    | -             | 0.4223     | 0.9740                | -                      |
| 7.3448     | 110    | 0.0382        | -          | -                     | -                      |
| 7.4828     | 112    | -             | 0.4226     | 0.9740                | 0.9732                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- Datasets: 2.19.1
- 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",
}
```

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