---
base_model: intfloat/multilingual-e5-small
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:2305
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Steps to start a vegetable garden
  sentences:
  - How to plant a vegetable garden?
  - If there will be a war between India and Pakistan who will win?
  - What is the most visited tourist attraction in the world?
- source_sentence: What's the best way to jump rope?
  sentences:
  - If I jump rope for five minutes, how many calories will I use?
  - You can collaborate on models and datasets using Machine Learning platforms by
    joining the community and accessing enhanced resources.
  - How can I improve my public speaking skills?
- source_sentence: How can remote team management be improved?
  sentences:
  - What are the key challenges of managing remote teams?
  - The library supports various audio formats such as WAV, MP3, and FLAC.
  - The `validate_data` method is used to perform checks on the data set for correctness.
- source_sentence: Latest advancements in quantum computing
  sentences:
  - How to cook a turkey?
  - Latest advancements in AI
  - How to create a resume?
- source_sentence: Practical guides are available to assist you in achieving specific
    goals and addressing real-world challenges with the framework.
  sentences:
  - How to bake cookies?
  - Yes, there are practical guides to help you achieve specific objectives and solve
    real-world problems with the framework.
  - How to create an email signature?
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: 0.9182879377431906
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8421422243118286
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.920754716981132
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8421422243118286
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9037037037037037
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9384615384615385
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9452952670187734
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9182879377431906
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.8421421647071838
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.920754716981132
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.8421421647071838
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9037037037037037
      name: Dot Precision
    - type: dot_recall
      value: 0.9384615384615385
      name: Dot Recall
    - type: dot_ap
      value: 0.9452952670187734
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9182879377431906
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 8.50709342956543
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9195402298850576
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 8.64261245727539
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.916030534351145
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9230769230769231
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9453829621939649
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9182879377431906
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5618841648101807
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.920754716981132
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5618841648101807
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9037037037037037
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9384615384615385
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9452952670187734
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9182879377431906
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 8.50709342956543
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.920754716981132
      name: Max F1
    - type: max_f1_threshold
      value: 8.64261245727539
      name: Max F1 Threshold
    - type: max_precision
      value: 0.916030534351145
      name: Max Precision
    - type: max_recall
      value: 0.9384615384615385
      name: Max Recall
    - type: max_ap
      value: 0.9453829621939649
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class test
      type: pair-class-test
    metrics:
    - type: cosine_accuracy
      value: 0.9182879377431906
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8421422243118286
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.920754716981132
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8421422243118286
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9037037037037037
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9384615384615385
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9452952670187734
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9182879377431906
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.8421421647071838
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.920754716981132
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.8421421647071838
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9037037037037037
      name: Dot Precision
    - type: dot_recall
      value: 0.9384615384615385
      name: Dot Recall
    - type: dot_ap
      value: 0.9452952670187734
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9182879377431906
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 8.50709342956543
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9195402298850576
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 8.64261245727539
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.916030534351145
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9230769230769231
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9453829621939649
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9182879377431906
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5618841648101807
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.920754716981132
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5618841648101807
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9037037037037037
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9384615384615385
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9452952670187734
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9182879377431906
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 8.50709342956543
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.920754716981132
      name: Max F1
    - type: max_f1_threshold
      value: 8.64261245727539
      name: Max F1 Threshold
    - type: max_precision
      value: 0.916030534351145
      name: Max Precision
    - type: max_recall
      value: 0.9384615384615385
      name: Max Recall
    - type: max_ap
      value: 0.9453829621939649
      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_8")
# Run inference
sentences = [
    'Practical guides are available to assist you in achieving specific goals and addressing real-world challenges with the framework.',
    'Yes, there are practical guides to help you achieve specific objectives and solve real-world problems with the framework.',
    'How to bake cookies?',
]
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>
-->

<!--
### Out-of-Scope Use

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

## 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              | 0.9183     |
| cosine_accuracy_threshold    | 0.8421     |
| cosine_f1                    | 0.9208     |
| cosine_f1_threshold          | 0.8421     |
| cosine_precision             | 0.9037     |
| cosine_recall                | 0.9385     |
| cosine_ap                    | 0.9453     |
| dot_accuracy                 | 0.9183     |
| dot_accuracy_threshold       | 0.8421     |
| dot_f1                       | 0.9208     |
| dot_f1_threshold             | 0.8421     |
| dot_precision                | 0.9037     |
| dot_recall                   | 0.9385     |
| dot_ap                       | 0.9453     |
| manhattan_accuracy           | 0.9183     |
| manhattan_accuracy_threshold | 8.5071     |
| manhattan_f1                 | 0.9195     |
| manhattan_f1_threshold       | 8.6426     |
| manhattan_precision          | 0.916      |
| manhattan_recall             | 0.9231     |
| manhattan_ap                 | 0.9454     |
| euclidean_accuracy           | 0.9183     |
| euclidean_accuracy_threshold | 0.5619     |
| euclidean_f1                 | 0.9208     |
| euclidean_f1_threshold       | 0.5619     |
| euclidean_precision          | 0.9037     |
| euclidean_recall             | 0.9385     |
| euclidean_ap                 | 0.9453     |
| max_accuracy                 | 0.9183     |
| max_accuracy_threshold       | 8.5071     |
| max_f1                       | 0.9208     |
| max_f1_threshold             | 8.6426     |
| max_precision                | 0.916      |
| max_recall                   | 0.9385     |
| **max_ap**                   | **0.9454** |

#### 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              | 0.9183     |
| cosine_accuracy_threshold    | 0.8421     |
| cosine_f1                    | 0.9208     |
| cosine_f1_threshold          | 0.8421     |
| cosine_precision             | 0.9037     |
| cosine_recall                | 0.9385     |
| cosine_ap                    | 0.9453     |
| dot_accuracy                 | 0.9183     |
| dot_accuracy_threshold       | 0.8421     |
| dot_f1                       | 0.9208     |
| dot_f1_threshold             | 0.8421     |
| dot_precision                | 0.9037     |
| dot_recall                   | 0.9385     |
| dot_ap                       | 0.9453     |
| manhattan_accuracy           | 0.9183     |
| manhattan_accuracy_threshold | 8.5071     |
| manhattan_f1                 | 0.9195     |
| manhattan_f1_threshold       | 8.6426     |
| manhattan_precision          | 0.916      |
| manhattan_recall             | 0.9231     |
| manhattan_ap                 | 0.9454     |
| euclidean_accuracy           | 0.9183     |
| euclidean_accuracy_threshold | 0.5619     |
| euclidean_f1                 | 0.9208     |
| euclidean_f1_threshold       | 0.5619     |
| euclidean_precision          | 0.9037     |
| euclidean_recall             | 0.9385     |
| euclidean_ap                 | 0.9453     |
| max_accuracy                 | 0.9183     |
| max_accuracy_threshold       | 8.5071     |
| max_f1                       | 0.9208     |
| max_f1_threshold             | 8.6426     |
| max_precision                | 0.916      |
| max_recall                   | 0.9385     |
| **max_ap**                   | **0.9454** |

<!--
## 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: 2,305 training samples
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence2                                                                         | sentence1                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 13.74 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.13 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>0: ~43.00%</li><li>1: ~57.00%</li></ul> |
* Samples:
  | sentence2                                                                                              | sentence1                                                                        | label          |
  |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
  | <code>What are the components of a computer?</code>                                                    | <code>How does a computer work?</code>                                           | <code>0</code> |
  | <code>You have the option to create your own personal blog with the help of Blogging Platforms.</code> | <code>Yes, you can start your own personal blog using Blogging Platforms.</code> | <code>1</code> |
  | <code>It provides the layout of the data and its components.</code>                                    | <code>It returns the structure of the data and its fields.</code>                | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 257 evaluation samples
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code>
* Approximate statistics based on the first 257 samples:
  |         | sentence2                                                                         | sentence1                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.92 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.84 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>0: ~49.42%</li><li>1: ~50.58%</li></ul> |
* Samples:
  | sentence2                                       | sentence1                                            | label          |
  |:------------------------------------------------|:-----------------------------------------------------|:---------------|
  | <code>What is the speed of sound in air?</code> | <code>What is the speed of light in a vacuum?</code> | <code>0</code> |
  | <code>Steps to fix a leaking faucet</code>      | <code>How to repair a leaking faucet?</code>         | <code>1</code> |
  | <code>Total bones in an adult human</code>      | <code>How many bones are in the human body?</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       | -             | -          | 0.7947                | -                      |
| 0.2740     | 10      | 1.6052        | -          | -                     | -                      |
| 0.5479     | 20      | 0.8914        | -          | -                     | -                      |
| 0.8219     | 30      | 0.8434        | -          | -                     | -                      |
| 0.9863     | 36      | -             | 0.6144     | 0.9366                | -                      |
| 1.0959     | 40      | 0.7351        | -          | -                     | -                      |
| 1.3699     | 50      | 0.5016        | -          | -                     | -                      |
| 1.6438     | 60      | 0.3754        | -          | -                     | -                      |
| 1.9178     | 70      | 0.3364        | -          | -                     | -                      |
| 2.0        | 73      | -             | 0.5985     | 0.9396                | -                      |
| 2.1918     | 80      | 0.3456        | -          | -                     | -                      |
| 2.4658     | 90      | 0.1953        | -          | -                     | -                      |
| 2.7397     | 100     | 0.1186        | -          | -                     | -                      |
| 2.9863     | 109     | -             | 0.5853     | 0.9455                | -                      |
| 3.0137     | 110     | 0.1622        | -          | -                     | -                      |
| 3.2877     | 120     | 0.1863        | -          | -                     | -                      |
| 3.5616     | 130     | 0.0906        | -          | -                     | -                      |
| 3.8356     | 140     | 0.1035        | -          | -                     | -                      |
| **3.9452** | **144** | **-**         | **0.5461** | **0.9454**            | **0.9454**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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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