---
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:2332
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Who discovered the structure of DNA?
  sentences:
  - Who discovered the structure of RNA?
  - Steps to apply for a scholarship
  - First human to set foot on the moon
- source_sentence: Who directed 'Schindler's List'?
  sentences:
  - Who directed 'The Pianist'?
  - What are some high paying jobs for a fresher with an M.Tech in biotechnology?
  - Where can I find gluten-free restaurants?
- source_sentence: Which is the best shares to purchase and sale daily trading?
  sentences:
  - In Sydney, which company would be the best to get advice for Business Sales &
    Purchases?
  - Steps to adjust phone settings
  - Is a 3.8 GPA sufficient to get into a top school?
- source_sentence: Nd she is always sad?
  sentences:
  - Where to purchase organic produce
  - Aerodynamically what happens when propellor rotates?
  - How is unsupervised learning used for data insights?
- source_sentence: How to bake a pie?
  sentences:
  - What is the population of Chicago?
  - Steps to bake a pie
  - 'What is the distribution of traffic between Google organic search results? e.g.
    #1 vs. #2 in rankings, first page vs. second page'
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: 0.8653846153846154
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.872760534286499
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8656716417910447
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8200240135192871
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8285714285714286
      name: Cosine Precision
    - type: cosine_recall
      value: 0.90625
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9322624848213654
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8653846153846154
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.872760534286499
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.8656716417910447
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.8200240135192871
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.8285714285714286
      name: Dot Precision
    - type: dot_recall
      value: 0.90625
      name: Dot Recall
    - type: dot_ap
      value: 0.9322624848213654
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.8692307692307693
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 9.252302169799805
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.8721804511278196
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 9.252302169799805
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8405797101449275
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.90625
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9322911488571455
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8653846153846154
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5044240355491638
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8656716417910447
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5999571084976196
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.8285714285714286
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.90625
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9322624848213654
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.8692307692307693
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 9.252302169799805
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8721804511278196
      name: Max F1
    - type: max_f1_threshold
      value: 9.252302169799805
      name: Max F1 Threshold
    - type: max_precision
      value: 0.8405797101449275
      name: Max Precision
    - type: max_recall
      value: 0.90625
      name: Max Recall
    - type: max_ap
      value: 0.9322911488571455
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class test
      type: pair-class-test
    metrics:
    - type: cosine_accuracy
      value: 0.916
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.844039261341095
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.907488986784141
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8230063319206238
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8728813559322034
      name: Cosine Precision
    - type: cosine_recall
      value: 0.944954128440367
      name: Cosine Recall
    - type: cosine_ap
      value: 0.96095333014952
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.916
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.8440393209457397
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.907488986784141
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.8230063319206238
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.8728813559322034
      name: Dot Precision
    - type: dot_recall
      value: 0.944954128440367
      name: Dot Recall
    - type: dot_ap
      value: 0.96095333014952
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.916
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 8.581160545349121
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.907488986784141
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 9.327116012573242
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8728813559322034
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.944954128440367
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9612698712458685
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.916
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5584936141967773
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.907488986784141
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.594968318939209
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.8728813559322034
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.944954128440367
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.96095333014952
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.916
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 8.581160545349121
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.907488986784141
      name: Max F1
    - type: max_f1_threshold
      value: 9.327116012573242
      name: Max F1 Threshold
    - type: max_precision
      value: 0.8728813559322034
      name: Max Precision
    - type: max_recall
      value: 0.944954128440367
      name: Max Recall
    - type: max_ap
      value: 0.9612698712458685
      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_5")
# Run inference
sentences = [
    'How to bake a pie?',
    'Steps to bake a pie',
    'What is the population of Chicago?',
]
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              | 0.8654     |
| cosine_accuracy_threshold    | 0.8728     |
| cosine_f1                    | 0.8657     |
| cosine_f1_threshold          | 0.82       |
| cosine_precision             | 0.8286     |
| cosine_recall                | 0.9062     |
| cosine_ap                    | 0.9323     |
| dot_accuracy                 | 0.8654     |
| dot_accuracy_threshold       | 0.8728     |
| dot_f1                       | 0.8657     |
| dot_f1_threshold             | 0.82       |
| dot_precision                | 0.8286     |
| dot_recall                   | 0.9062     |
| dot_ap                       | 0.9323     |
| manhattan_accuracy           | 0.8692     |
| manhattan_accuracy_threshold | 9.2523     |
| manhattan_f1                 | 0.8722     |
| manhattan_f1_threshold       | 9.2523     |
| manhattan_precision          | 0.8406     |
| manhattan_recall             | 0.9062     |
| manhattan_ap                 | 0.9323     |
| euclidean_accuracy           | 0.8654     |
| euclidean_accuracy_threshold | 0.5044     |
| euclidean_f1                 | 0.8657     |
| euclidean_f1_threshold       | 0.6        |
| euclidean_precision          | 0.8286     |
| euclidean_recall             | 0.9062     |
| euclidean_ap                 | 0.9323     |
| max_accuracy                 | 0.8692     |
| max_accuracy_threshold       | 9.2523     |
| max_f1                       | 0.8722     |
| max_f1_threshold             | 9.2523     |
| max_precision                | 0.8406     |
| max_recall                   | 0.9062     |
| **max_ap**                   | **0.9323** |

#### 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              | 0.916      |
| cosine_accuracy_threshold    | 0.844      |
| cosine_f1                    | 0.9075     |
| cosine_f1_threshold          | 0.823      |
| cosine_precision             | 0.8729     |
| cosine_recall                | 0.945      |
| cosine_ap                    | 0.961      |
| dot_accuracy                 | 0.916      |
| dot_accuracy_threshold       | 0.844      |
| dot_f1                       | 0.9075     |
| dot_f1_threshold             | 0.823      |
| dot_precision                | 0.8729     |
| dot_recall                   | 0.945      |
| dot_ap                       | 0.961      |
| manhattan_accuracy           | 0.916      |
| manhattan_accuracy_threshold | 8.5812     |
| manhattan_f1                 | 0.9075     |
| manhattan_f1_threshold       | 9.3271     |
| manhattan_precision          | 0.8729     |
| manhattan_recall             | 0.945      |
| manhattan_ap                 | 0.9613     |
| euclidean_accuracy           | 0.916      |
| euclidean_accuracy_threshold | 0.5585     |
| euclidean_f1                 | 0.9075     |
| euclidean_f1_threshold       | 0.595      |
| euclidean_precision          | 0.8729     |
| euclidean_recall             | 0.945      |
| euclidean_ap                 | 0.961      |
| max_accuracy                 | 0.916      |
| max_accuracy_threshold       | 8.5812     |
| max_f1                       | 0.9075     |
| max_f1_threshold             | 9.3271     |
| max_precision                | 0.8729     |
| max_recall                   | 0.945      |
| **max_ap**                   | **0.9613** |

<!--
## 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: 2,332 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: 6 tokens</li><li>mean: 12.96 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.67 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~52.80%</li><li>1: ~47.20%</li></ul> |
* Samples:
  | sentence1                                                              | sentence2                                                | label          |
  |:-----------------------------------------------------------------------|:---------------------------------------------------------|:---------------|
  | <code>How to bake a chocolate cake?</code>                             | <code>Recipe for baking a chocolate cake</code>          | <code>1</code> |
  | <code>Why do girls want to be friends with the guy they reject?</code> | <code>How do guys feel after rejecting a girl?</code>    | <code>0</code> |
  | <code>How can I stop being afraid of working?</code>                   | <code>How do you stop being afraid of everything?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 260 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: 6 tokens</li><li>mean: 13.44 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.99 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>0: ~50.77%</li><li>1: ~49.23%</li></ul> |
* Samples:
  | sentence1                                | sentence2                                         | label          |
  |:-----------------------------------------|:--------------------------------------------------|:---------------|
  | <code>How to cook spaghetti?</code>      | <code>Steps to cook spaghetti</code>              | <code>1</code> |
  | <code>How to create a mobile app?</code> | <code>How to create a desktop application?</code> | <code>0</code> |
  | <code>How can I update my resume?</code> | <code>Steps to revise and update a resume</code>  | <code>1</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
- `num_train_epochs`: 4
- `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`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `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      | -             | -          | 0.6979                | -                      |
| 0.2740  | 10     | 1.9007        | -          | -                     | -                      |
| 0.5479  | 20     | 1.1616        | -          | -                     | -                      |
| 0.8219  | 30     | 0.9094        | -          | -                     | -                      |
| 0.9863  | 36     | -             | 0.7692     | 0.9117                | -                      |
| 1.0959  | 40     | 0.9105        | -          | -                     | -                      |
| 1.3699  | 50     | 0.6629        | -          | -                     | -                      |
| 1.6438  | 60     | 0.4243        | -          | -                     | -                      |
| 1.9178  | 70     | 0.4729        | -          | -                     | -                      |
| **2.0** | **73** | **-**         | **0.7294** | **0.9306**            | **-**                  |
| 2.1918  | 80     | 0.4897        | -          | -                     | -                      |
| 2.4658  | 90     | 0.3103        | -          | -                     | -                      |
| 2.7397  | 100    | 0.2316        | -          | -                     | -                      |
| 2.9863  | 109    | -             | 0.7807     | 0.9311                | -                      |
| 3.0137  | 110    | 0.3179        | -          | -                     | -                      |
| 3.2877  | 120    | 0.1975        | -          | -                     | -                      |
| 3.5616  | 130    | 0.1477        | -          | -                     | -                      |
| 3.8356  | 140    | 0.1034        | -          | -                     | -                      |
| 3.9452  | 144    | -             | 0.8132     | 0.9323                | 0.9613                 |

* 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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