README.md

---
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:1030
- loss:ContrastiveLoss
widget:
- source_sentence: First climber to reach the summit of Everest
  sentences:
  - How to create a podcast?
  - How to cook sushi rice?
  - Who was the first person to climb Mount Everest?
- source_sentence: What methods are used to measure a nation's GDP?
  sentences:
  - How is the GDP of a country measured?
  - How do I sign out of my email account?
  - How does digital marketing differ from traditional marketing?
- source_sentence: Steps to sign up for a new account
  sentences:
  - How to grow tomatoes in a garden?
  - What is the process for creating a new account?
  - What is the GDP of India?
- source_sentence: Name of the tallest building in New York
  sentences:
  - What are the symptoms of anxiety?
  - What is the tallest building in New York?
  - Who was the first female Prime Minister of the UK?
- source_sentence: How do you make a paper boat?
  sentences:
  - What are the benefits of using solar energy?
  - Where can I buy a new phone?
  - How do you make a paper airplane?
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: 0.9478260869565217
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6633322238922119
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9558823529411764
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6633322238922119
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9154929577464789
      name: Cosine Precision
    - type: cosine_recall
      value: 1.0
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9777355464218691
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9478260869565217
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.6633322238922119
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9558823529411764
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.6633322238922119
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9154929577464789
      name: Dot Precision
    - type: dot_recall
      value: 1.0
      name: Dot Recall
    - type: dot_ap
      value: 0.9777355464218691
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9391304347826087
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 9.603110313415527
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9489051094890512
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 12.660685539245605
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9027777777777778
      name: Manhattan Precision
    - type: manhattan_recall
      value: 1.0
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.975614621691024
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9478260869565217
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.8205450773239136
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9558823529411764
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.8205450773239136
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9154929577464789
      name: Euclidean Precision
    - type: euclidean_recall
      value: 1.0
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9777355464218691
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9478260869565217
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 9.603110313415527
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9558823529411764
      name: Max F1
    - type: max_f1_threshold
      value: 12.660685539245605
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9154929577464789
      name: Max Precision
    - type: max_recall
      value: 1.0
      name: Max Recall
    - type: max_ap
      value: 0.9777355464218691
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class test
      type: pair-class-test
    metrics:
    - type: cosine_accuracy
      value: 0.9478260869565217
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7873066663742065
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9558823529411764
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6542514562606812
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9154929577464789
      name: Cosine Precision
    - type: cosine_recall
      value: 1.0
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9776721343444097
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9478260869565217
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.7873067259788513
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9558823529411764
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.6542515158653259
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9154929577464789
      name: Dot Precision
    - type: dot_recall
      value: 1.0
      name: Dot Recall
    - type: dot_ap
      value: 0.9776721343444097
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9478260869565217
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 11.123205184936523
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9558823529411764
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 12.862250328063965
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9154929577464789
      name: Manhattan Precision
    - type: manhattan_recall
      value: 1.0
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9774497925836063
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9478260869565217
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.652188777923584
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9558823529411764
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.8315430879592896
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9154929577464789
      name: Euclidean Precision
    - type: euclidean_recall
      value: 1.0
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9776721343444097
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9478260869565217
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 11.123205184936523
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9558823529411764
      name: Max F1
    - type: max_f1_threshold
      value: 12.862250328063965
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9154929577464789
      name: Max Precision
    - type: max_recall
      value: 1.0
      name: Max Recall
    - type: max_ap
      value: 0.9776721343444097
      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_2")
# Run inference
sentences = [
    'How do you make a paper boat?',
    'How do you make a paper airplane?',
    'What are the benefits of using solar energy?',
]
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>
-->

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### 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

#### 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              | 0.9478     |
| cosine_accuracy_threshold    | 0.6633     |
| cosine_f1                    | 0.9559     |
| cosine_f1_threshold          | 0.6633     |
| cosine_precision             | 0.9155     |
| cosine_recall                | 1.0        |
| cosine_ap                    | 0.9777     |
| dot_accuracy                 | 0.9478     |
| dot_accuracy_threshold       | 0.6633     |
| dot_f1                       | 0.9559     |
| dot_f1_threshold             | 0.6633     |
| dot_precision                | 0.9155     |
| dot_recall                   | 1.0        |
| dot_ap                       | 0.9777     |
| manhattan_accuracy           | 0.9391     |
| manhattan_accuracy_threshold | 9.6031     |
| manhattan_f1                 | 0.9489     |
| manhattan_f1_threshold       | 12.6607    |
| manhattan_precision          | 0.9028     |
| manhattan_recall             | 1.0        |
| manhattan_ap                 | 0.9756     |
| euclidean_accuracy           | 0.9478     |
| euclidean_accuracy_threshold | 0.8205     |
| euclidean_f1                 | 0.9559     |
| euclidean_f1_threshold       | 0.8205     |
| euclidean_precision          | 0.9155     |
| euclidean_recall             | 1.0        |
| euclidean_ap                 | 0.9777     |
| max_accuracy                 | 0.9478     |
| max_accuracy_threshold       | 9.6031     |
| max_f1                       | 0.9559     |
| max_f1_threshold             | 12.6607    |
| max_precision                | 0.9155     |
| max_recall                   | 1.0        |
| **max_ap**                   | **0.9777** |

#### 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              | 0.9478     |
| cosine_accuracy_threshold    | 0.7873     |
| cosine_f1                    | 0.9559     |
| cosine_f1_threshold          | 0.6543     |
| cosine_precision             | 0.9155     |
| cosine_recall                | 1.0        |
| cosine_ap                    | 0.9777     |
| dot_accuracy                 | 0.9478     |
| dot_accuracy_threshold       | 0.7873     |
| dot_f1                       | 0.9559     |
| dot_f1_threshold             | 0.6543     |
| dot_precision                | 0.9155     |
| dot_recall                   | 1.0        |
| dot_ap                       | 0.9777     |
| manhattan_accuracy           | 0.9478     |
| manhattan_accuracy_threshold | 11.1232    |
| manhattan_f1                 | 0.9559     |
| manhattan_f1_threshold       | 12.8623    |
| manhattan_precision          | 0.9155     |
| manhattan_recall             | 1.0        |
| manhattan_ap                 | 0.9774     |
| euclidean_accuracy           | 0.9478     |
| euclidean_accuracy_threshold | 0.6522     |
| euclidean_f1                 | 0.9559     |
| euclidean_f1_threshold       | 0.8315     |
| euclidean_precision          | 0.9155     |
| euclidean_recall             | 1.0        |
| euclidean_ap                 | 0.9777     |
| max_accuracy                 | 0.9478     |
| max_accuracy_threshold       | 11.1232    |
| max_f1                       | 0.9559     |
| max_f1_threshold             | 12.8623    |
| max_precision                | 0.9155     |
| max_recall                   | 1.0        |
| **max_ap**                   | **0.9777** |

<!--
## 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: 1,030 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: ~49.60%</li><li>1: ~50.40%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.27 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.9 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | label          | sentence2                                             | sentence1                                                              |
  |:---------------|:------------------------------------------------------|:-----------------------------------------------------------------------|
  | <code>1</code> | <code>Speed of sound in air</code>                    | <code>What is the speed of sound?</code>                               |
  | <code>1</code> | <code>World's most popular tourist destination</code> | <code>What is the most visited tourist attraction in the world?</code> |
  | <code>1</code> | <code>How do I write a resume?</code>                 | <code>How do I create a resume?</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": 0.6,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 115 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: ~43.48%</li><li>1: ~56.52%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.04 tokens</li><li>max: 15 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>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.6,
      "size_average": true
  }
  ```

### 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
- `lr_scheduler_type`: reduce_lr_on_plateau
- `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`: reduce_lr_on_plateau
- `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       | -             | -          | 0.7625                | -                      |
| 0.6061     | 10      | 0.0417        | -          | -                     | -                      |
| 0.9697     | 16      | -             | 0.0119     | 0.9695                | -                      |
| 1.2121     | 20      | 0.0189        | -          | -                     | -                      |
| 1.8182     | 30      | 0.0148        | -          | -                     | -                      |
| 2.0        | 33      | -             | 0.0102     | 0.9741                | -                      |
| 2.4242     | 40      | 0.0114        | -          | -                     | -                      |
| 2.9697     | 49      | -             | 0.0098     | 0.9752                | -                      |
| 3.0303     | 50      | 0.009         | -          | -                     | -                      |
| 3.6364     | 60      | 0.008         | -          | -                     | -                      |
| 4.0        | 66      | -             | 0.0095     | 0.9778                | -                      |
| 4.2424     | 70      | 0.0065        | -          | -                     | -                      |
| 4.8485     | 80      | 0.0056        | -          | -                     | -                      |
| 4.9697     | 82      | -             | 0.0092     | 0.9749                | -                      |
| 5.4545     | 90      | 0.0056        | -          | -                     | -                      |
| 6.0        | 99      | -             | 0.0088     | 0.9766                | -                      |
| 6.0606     | 100     | 0.0045        | -          | -                     | -                      |
| 6.6667     | 110     | 0.0044        | -          | -                     | -                      |
| **6.9697** | **115** | **-**         | **0.0087** | **0.9777**            | **-**                  |
| 7.2727     | 120     | 0.0038        | -          | -                     | -                      |
| 7.7576     | 128     | -             | 0.0090     | 0.9777                | 0.9777                 |

* 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",
}
```

#### 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}
}
```

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