---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: aubmindlab/bert-base-arabertv02
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط
    النظيفة
  sentences:
  - رجل يقدم عرضاً
  - هناك رجل بالخارج قرب الشاطئ
  - رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
  sentences:
  - السرير قذر.
  - رجل يضحك أثناء غسيل الملابس
  - الرجل على القمر
- source_sentence: الفتيات بالخارج
  sentences:
  - امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
  - فتيان يركبان في جولة متعة
  - ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة تتحدث
    إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
  sentences:
  - رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة حمراء
    مع الماء في الخلفية.
  - كتاب القصص مفتوح
  - رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة
    شابة.
  sentences:
  - ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
  - رجل يستلقي على وجهه على مقعد في الحديقة.
  - الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on aubmindlab/bert-base-arabertv02
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.5949906740977448
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6159750250469712
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6295622269205102
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6269654283099967
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6326526932327604
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6317081341785673
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.42816790752358297
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4295282086669423
      name: Spearman Dot
    - type: pearson_max
      value: 0.6326526932327604
      name: Pearson Max
    - type: spearman_max
      value: 0.6317081341785673
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.5846223235167534
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6064092420664184
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6287774004727389
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6263546541183983
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.631267664308041
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6301778108727977
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3788565672017437
      name: Pearson Dot
    - type: spearman_dot
      value: 0.37680551461721923
      name: Spearman Dot
    - type: pearson_max
      value: 0.631267664308041
      name: Pearson Max
    - type: spearman_max
      value: 0.6301778108727977
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.5778623383989389
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5959667709300495
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6242980982402613
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6217473192873829
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6237908608463304
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6215304658549996
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.35968442092444003
      name: Pearson Dot
    - type: spearman_dot
      value: 0.35304547874806785
      name: Spearman Dot
    - type: pearson_max
      value: 0.6242980982402613
      name: Pearson Max
    - type: spearman_max
      value: 0.6217473192873829
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.5830782075122916
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6022044167653756
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6151866925343435
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6121950064533626
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6162225316000448
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.615301209345362
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.40438461342780957
      name: Pearson Dot
    - type: spearman_dot
      value: 0.40153111017443666
      name: Spearman Dot
    - type: pearson_max
      value: 0.6162225316000448
      name: Pearson Max
    - type: spearman_max
      value: 0.615301209345362
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.5724838823862283
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5914127847098
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6023812283389073
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5967205030284914
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6069294574719372
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6041440553344074
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.36315938245739166
      name: Pearson Dot
    - type: spearman_dot
      value: 0.358512645020771
      name: Spearman Dot
    - type: pearson_max
      value: 0.6069294574719372
      name: Pearson Max
    - type: spearman_max
      value: 0.6041440553344074
      name: Spearman Max
---

# SentenceTransformer based on aubmindlab/bert-base-arabertv02

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 768-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:** [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) <!-- at revision 016fb9d6768f522a59c6e0d2d5d5d43a4e1bff60 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-arabert-all-nli-triplet")
# Run inference
sentences = [
    'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
    'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
    'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.595     |
| **spearman_cosine** | **0.616** |
| pearson_manhattan   | 0.6296    |
| spearman_manhattan  | 0.627     |
| pearson_euclidean   | 0.6327    |
| spearman_euclidean  | 0.6317    |
| pearson_dot         | 0.4282    |
| spearman_dot        | 0.4295    |
| pearson_max         | 0.6327    |
| spearman_max        | 0.6317    |

#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5846     |
| **spearman_cosine** | **0.6064** |
| pearson_manhattan   | 0.6288     |
| spearman_manhattan  | 0.6264     |
| pearson_euclidean   | 0.6313     |
| spearman_euclidean  | 0.6302     |
| pearson_dot         | 0.3789     |
| spearman_dot        | 0.3768     |
| pearson_max         | 0.6313     |
| spearman_max        | 0.6302     |

#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.5779    |
| **spearman_cosine** | **0.596** |
| pearson_manhattan   | 0.6243    |
| spearman_manhattan  | 0.6217    |
| pearson_euclidean   | 0.6238    |
| spearman_euclidean  | 0.6215    |
| pearson_dot         | 0.3597    |
| spearman_dot        | 0.353     |
| pearson_max         | 0.6243    |
| spearman_max        | 0.6217    |

#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5831     |
| **spearman_cosine** | **0.6022** |
| pearson_manhattan   | 0.6152     |
| spearman_manhattan  | 0.6122     |
| pearson_euclidean   | 0.6162     |
| spearman_euclidean  | 0.6153     |
| pearson_dot         | 0.4044     |
| spearman_dot        | 0.4015     |
| pearson_max         | 0.6162     |
| spearman_max        | 0.6153     |

#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5725     |
| **spearman_cosine** | **0.5914** |
| pearson_manhattan   | 0.6024     |
| spearman_manhattan  | 0.5967     |
| pearson_euclidean   | 0.6069     |
| spearman_euclidean  | 0.6041     |
| pearson_dot         | 0.3632     |
| spearman_dot        | 0.3585     |
| pearson_max         | 0.6069     |
| spearman_max        | 0.6041     |

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## Training Details

### Training Dataset

#### Omartificial-Intelligence-Space/arabic-n_li-triplet

* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.02 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.03 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.72 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | anchor                                                      | positive                                    | negative                            |
  |:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
  | <code>شخص على حصان يقفز فوق طائرة معطلة</code>              | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
  | <code>أطفال يبتسمون و يلوحون للكاميرا</code>                | <code>هناك أطفال حاضرون</code>              | <code>الاطفال يتجهمون</code>        |
  | <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code>        | <code>الصبي يتزلج على الرصيف</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### Omartificial-Intelligence-Space/arabic-n_li-triplet

* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.87 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.54 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.14 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                               | positive                                               | negative                                           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
  | <code>امرأتان يتعانقان بينما يحملان حزمة</code>                                                                                                      | <code>إمرأتان يحملان حزمة</code>                       | <code>الرجال يتشاجرون خارج مطعم</code>             |
  | <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
  | <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code>                                                                             | <code>رجل يبيع الدونات لعميل</code>                    | <code>امرأة تشرب قهوتها في مقهى صغير</code>        |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 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`: 1
- `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
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: 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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200  | 14.4811       | -                            | -                            | -                            | -                           | -                            |
| 0.0459 | 400  | 9.0389        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 600  | 8.1478        | -                            | -                            | -                            | -                           | -                            |
| 0.0918 | 800  | 7.168         | -                            | -                            | -                            | -                           | -                            |
| 0.1147 | 1000 | 7.1998        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 1200 | 6.7985        | -                            | -                            | -                            | -                           | -                            |
| 0.1606 | 1400 | 6.3754        | -                            | -                            | -                            | -                           | -                            |
| 0.1835 | 1600 | 6.3202        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 1800 | 5.9186        | -                            | -                            | -                            | -                           | -                            |
| 0.2294 | 2000 | 5.9594        | -                            | -                            | -                            | -                           | -                            |
| 0.2524 | 2200 | 6.0211        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 2400 | 5.9984        | -                            | -                            | -                            | -                           | -                            |
| 0.2983 | 2600 | 5.8321        | -                            | -                            | -                            | -                           | -                            |
| 0.3212 | 2800 | 5.621         | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 3000 | 5.9004        | -                            | -                            | -                            | -                           | -                            |
| 0.3671 | 3200 | 5.562         | -                            | -                            | -                            | -                           | -                            |
| 0.3900 | 3400 | 5.5125        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 3600 | 5.4922        | -                            | -                            | -                            | -                           | -                            |
| 0.4359 | 3800 | 5.3023        | -                            | -                            | -                            | -                           | -                            |
| 0.4589 | 4000 | 5.4376        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 4200 | 5.1048        | -                            | -                            | -                            | -                           | -                            |
| 0.5048 | 4400 | 5.0605        | -                            | -                            | -                            | -                           | -                            |
| 0.5277 | 4600 | 4.9985        | -                            | -                            | -                            | -                           | -                            |
| 0.5506 | 4800 | 5.2594        | -                            | -                            | -                            | -                           | -                            |
| 0.5736 | 5000 | 5.2183        | -                            | -                            | -                            | -                           | -                            |
| 0.5965 | 5200 | 5.1621        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 5400 | 5.166         | -                            | -                            | -                            | -                           | -                            |
| 0.6424 | 5600 | 5.2241        | -                            | -                            | -                            | -                           | -                            |
| 0.6654 | 5800 | 5.1342        | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 6000 | 5.2267        | -                            | -                            | -                            | -                           | -                            |
| 0.7113 | 6200 | 5.1083        | -                            | -                            | -                            | -                           | -                            |
| 0.7342 | 6400 | 5.0119        | -                            | -                            | -                            | -                           | -                            |
| 0.7571 | 6600 | 4.6471        | -                            | -                            | -                            | -                           | -                            |
| 0.7801 | 6800 | 3.6699        | -                            | -                            | -                            | -                           | -                            |
| 0.8030 | 7000 | 3.2954        | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 7200 | 3.1039        | -                            | -                            | -                            | -                           | -                            |
| 0.8489 | 7400 | 3.001         | -                            | -                            | -                            | -                           | -                            |
| 0.8719 | 7600 | 2.8992        | -                            | -                            | -                            | -                           | -                            |
| 0.8948 | 7800 | 2.7504        | -                            | -                            | -                            | -                           | -                            |
| 0.9177 | 8000 | 2.7891        | -                            | -                            | -                            | -                           | -                            |
| 0.9407 | 8200 | 2.7157        | -                            | -                            | -                            | -                           | -                            |
| 0.9636 | 8400 | 2.6795        | -                            | -                            | -                            | -                           | -                            |
| 0.9866 | 8600 | 2.6278        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 8717 | -             | 0.6022                       | 0.5960                       | 0.6064                       | 0.5914                      | 0.6160                       |


### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

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

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