sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:2818353 loss:CachedMultipleNegativesRankingLoss dataset:atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset arxiv:1908.10084 arxiv:2101.06983 base_model:answerdotai/ModernBERT-base base_model:finetune:answerdotai/ModernBERT-base autotrain_compatible endpoints_compatible region:us
---
base_model: answerdotai/ModernBERT-base
datasets:
- atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2818353
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: واش كا يحبس هاد الطوبيس في شارع ستونر؟
  sentences:
  - '{''ar'': ''هل هذه الحافلة تتوقف في شارع أستونر ؟''}'
  - tachicart/mo_darija_merged
  - tachicart/mo_darija_merged
- source_sentence: العمال تما يقدرو يبدلو ليك الدولار بالفيتشات ديال الكازينو. مشينا؟
  sentences:
  - tachicart/mo_darija_merged
  - tachicart/mo_darija_merged
  - '{''ar'': ''يستطيع الصرافون أن يغيروا دولاراتك من أجل بقشيش الكازينو . هل نذهب
    ؟''}'
- source_sentence: واخا توريني شي كبوط مضاد للماء؟
  sentences:
  - tachicart/mo_darija_merged
  - '{''ar'': ''هل لك أن ترنى معطفاً ضد الماء ؟''}'
  - tachicart/mo_darija_merged
- source_sentence: فين كاين البلاطو رقم خمسة؟
  sentences:
  - tachicart/mo_darija_merged
  - tachicart/mo_darija_merged
  - '{''ar'': ''أين الرصيف رقم خمسة ؟''}'
- source_sentence: شحال للمطار؟
  sentences:
  - tachicart/mo_darija_merged
  - tachicart/mo_darija_merged
  - '{''ar'': ''كم سأدفع للوصول إلى المطار ؟''}'
---

# SentenceTransformer based on answerdotai/ModernBERT-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [al-atlas-moroccan-darija-pretraining-dataset](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset) 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [al-atlas-moroccan-darija-pretraining-dataset](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset)
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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("BounharAbdelaziz/ModernBERT-basemoroccan-arabic-epoch-2lr-0.0005batch-32")
# Run inference
sentences = [
    'شحال للمطار؟',
    'tachicart/mo_darija_merged',
    "{'ar': 'كم سأدفع للوصول إلى المطار ؟'}",
]
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]
```

<!--
### Direct Usage (Transformers)

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</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

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## 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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## Training Details

### Training Dataset

#### al-atlas-moroccan-darija-pretraining-dataset

* Dataset: [al-atlas-moroccan-darija-pretraining-dataset](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset) at [6668961](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset/tree/66689612b03f0d7a9528bf74ea30782dd2976569)
* Size: 2,818,353 training samples
* Columns: <code>text</code>, <code>dataset_source</code>, and <code>metadata</code>
* Approximate statistics based on the first 1000 samples:
  |         | text                                                                                 | dataset_source                                                                    | metadata                                                                          |
  |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 132.63 tokens</li><li>max: 2469 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 21.0 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 25.5 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | dataset_source                               | metadata                                                          |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:------------------------------------------------------------------|
  | <code>سامي خضيرة  : <br><br>الكابيتان فوقتنا كان هو كاسياس ولكن  كنا كنحسو باللي راموس هو القائد الفعلي كان فيه الروح و الغرينتا ديال الاسبان .<br><br>ماتنساش كان معانا تا رونالدو  كيهضر مع كولشي ويحفزنا ،  و عادي تسمعو وسط الفيستير كيقول  " خضيرة زير راسك وكون عدواني " ، " مسعود عطينا شوية من سحرك الكروي فالتيران " ونتا  أدي ماريا حاول تشد الكرة وقصد المرمى " كان هادشي كيخلينا نعطيو كل ما فجهدنا <br><br>و بطبيعة الحال كان مورينيو  الخطير فهاد الضومين ،  و كانت المشكلة الكبيرة ديما هي كيفاش نوقفو ميسي ماشي غير حنا ولكن كاع الفراقي فداك الوقت .</code>                                                                                                                                                                                                                                  | <code>atlasia/facebook_darija_dataset</code> | <code>{'pageName': "Football B'darija - فوتبول بالداريجة"}</code> |
  | <code>الأحداث كاتتطور بسرعة رهيبة ف بريتوريا !!<br><br>ميغيل كاردوزو المدرب السابق للترجي الرياضي التونسي وصل البارح بشكل مفاجئ لجنوب افريقيا..  وصباح اليوم الصحافة المحلية كاتأكد انو ماميلودي سانداونز غاتقيل المدرب ديالها اليوم و غاتعين كاردوزو ك بديل !</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>atlasia/facebook_darija_dataset</code> | <code>{'pageName': "Football B'darija - فوتبول بالداريجة"}</code> |
  | <code>الريال و تحدي جديد هاد الليلة باش يرجعو للمنافسة ف التشامبيانزليغ قدام خصم أقل ما يتقال عليه انو عتيد هو اتلانتا بيرغامو وليدات العبقري جيانبييرو غاسبيريني..<br><br>الريال مؤخرا ورغم الشكوك اللي دايرة على الفريق والمشاكل الدفاعية و الإصابات اللي زادت ف الهشاشة ديال الدفاع ديالو الا انو رجع بقوة للمنافسة فالليغا واستغل الفترة د الفراغ اللي تا تعيشها البارسا حاليا باش يرجع على بعد نقطتين من الصدارة و عندو ماتش مؤجل مرشح بقوة يفوز فيه على فالنسيا ويطلع للقمة ..<br><br>الريال تانضن لا ربح اليوم غايمحي بشكل شبه كلي الغمامة اللي كاتطوف فوق منو من بدا الموسم و غايقوي ثقة الجمهور فيه و يرجع الثقة للمجموعة و غايرسم راسو ك رقم قوي ف المنافسة المفضلة ليه واحنا ديجا عارفين ان الريال diesel فرقة كاتديماري بشوية بشوية وفالفترات الحاسمة ف الموسم كاتورك على السانكيام فيتيس.</code> | <code>atlasia/facebook_darija_dataset</code> | <code>{'pageName': "Football B'darija - فوتبول بالداريجة"}</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### al-atlas-moroccan-darija-pretraining-dataset

* Dataset: [al-atlas-moroccan-darija-pretraining-dataset](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset) at [6668961](https://huggingface.co/datasets/atlasia/AL-Atlas-Moroccan-Darija-Pretraining-Dataset/tree/66689612b03f0d7a9528bf74ea30782dd2976569)
* Size: 1,875 evaluation samples
* Columns: <code>text</code>, <code>dataset_source</code>, and <code>metadata</code>
* Approximate statistics based on the first 1000 samples:
  |         | text                                                                              | dataset_source                                                                    | metadata                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 11.01 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 18.0 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 20.88 tokens</li><li>max: 74 tokens</li></ul> |
* Samples:
  | text                                                                                                     | dataset_source                          | metadata                                                                                                   |
  |:---------------------------------------------------------------------------------------------------------|:----------------------------------------|:-----------------------------------------------------------------------------------------------------------|
  | <code>كاين في اللاخر ديال هاد القاعة. انجيب ليك شويا دابا. و إلا حتاجيتي شي حاجا اخرى، قولها ليا.</code> | <code>tachicart/mo_darija_merged</code> | <code>{'ar': 'إنها في أخر القاعة . سوف آتي لك ببعض منها الآن . إذا أردت أي شيئاً آخر فقط أعلمني .'}</code> |
  | <code>واش كا دير التعديلات؟</code>                                                                       | <code>tachicart/mo_darija_merged</code> | <code>{'ar': 'هل تقومون بعمل تعديلات ؟'}</code>                                                            |
  | <code>بغينا ناخدو طابلة حدا الشرجم.</code>                                                               | <code>tachicart/mo_darija_merged</code> | <code>{'ar': 'نريد مائدة بجانب النافذة .'}</code>                                                          |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 0.0005
- `num_train_epochs`: 2
- `warmup_ratio`: 0.03
- `bf16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0005
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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`: True
- `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`: 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, 'non_blocking': False, '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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss | Validation Loss |
|:------:|:------:|:-------------:|:---------------:|
| 0.0114 | 1000   | 3.2165        | 3.9089          |
| 0.0227 | 2000   | 3.0702        | 3.4543          |
| 0.0341 | 3000   | 3.0376        | 3.5355          |
| 0.0454 | 4000   | 3.0205        | 3.4417          |
| 0.0568 | 5000   | 3.0262        | 3.4540          |
| 0.0681 | 6000   | 3.0141        | 3.4423          |
| 0.0795 | 7000   | 3.0152        | 3.4597          |
| 0.0908 | 8000   | 3.0089        | 3.4972          |
| 0.1022 | 9000   | 3.0201        | 3.4511          |
| 0.1135 | 10000  | 3.0043        | 3.4342          |
| 0.1249 | 11000  | 2.9931        | 3.4398          |
| 0.1362 | 12000  | 2.9955        | 3.4332          |
| 0.1476 | 13000  | 3.0002        | 3.4291          |
| 0.1590 | 14000  | 2.9924        | 3.4298          |
| 0.1703 | 15000  | 3.0046        | 3.4330          |
| 0.1817 | 16000  | 2.9917        | 3.4301          |
| 0.1930 | 17000  | 3.0091        | 3.4520          |
| 0.2044 | 18000  | 3.0021        | 3.4260          |
| 0.2157 | 19000  | 2.9968        | 3.4222          |
| 0.2271 | 20000  | 2.9966        | 3.4202          |
| 0.2384 | 21000  | 3.0037        | 3.4315          |
| 0.2498 | 22000  | 3.0024        | 3.4155          |
| 0.2611 | 23000  | 2.9916        | 3.4174          |
| 0.2725 | 24000  | 2.9891        | 3.4384          |
| 0.2839 | 25000  | 2.9956        | 3.4443          |
| 0.2952 | 26000  | 2.9966        | 3.4174          |
| 0.3066 | 27000  | 2.9927        | 3.4233          |
| 0.3179 | 28000  | 2.9895        | 3.4133          |
| 0.3293 | 29000  | 2.9924        | 3.4124          |
| 0.3406 | 30000  | 2.9879        | 3.4154          |
| 0.3520 | 31000  | 2.9952        | 3.4209          |
| 0.3633 | 32000  | 2.9901        | 3.4177          |
| 0.3747 | 33000  | 2.9913        | 3.4140          |
| 0.3860 | 34000  | 2.9985        | 3.4130          |
| 0.3974 | 35000  | 2.9953        | 3.4131          |
| 0.4087 | 36000  | 2.9987        | 3.4167          |
| 0.4201 | 37000  | 2.9917        | 3.4165          |
| 0.4315 | 38000  | 2.9908        | 3.4154          |
| 0.4428 | 39000  | 2.9866        | 3.4103          |
| 0.4542 | 40000  | 2.9931        | 3.4115          |
| 0.4655 | 41000  | 2.9807        | 3.4100          |
| 0.4769 | 42000  | 3.0011        | 3.4124          |
| 0.4882 | 43000  | 3.0037        | 3.4098          |
| 0.4996 | 44000  | 2.993         | 3.4082          |
| 0.5109 | 45000  | 3.0012        | 3.4181          |
| 0.5223 | 46000  | 3.0004        | 3.4117          |
| 0.5336 | 47000  | 3.0003        | 3.4090          |
| 0.5450 | 48000  | 2.9915        | 3.4055          |
| 0.5564 | 49000  | 2.9992        | 3.4034          |
| 0.5677 | 50000  | 2.9915        | 3.4061          |
| 0.5791 | 51000  | 3.0028        | 3.4055          |
| 0.5904 | 52000  | 2.9928        | 3.4027          |
| 0.6018 | 53000  | 2.9899        | 3.4076          |
| 0.6131 | 54000  | 2.9875        | 3.4032          |
| 0.6245 | 55000  | 2.9956        | 3.4044          |
| 0.6358 | 56000  | 2.9797        | 3.4011          |
| 0.6472 | 57000  | 2.988         | 3.4050          |
| 0.6585 | 58000  | 2.9832        | 3.4071          |
| 0.6699 | 59000  | 2.9889        | 3.4134          |
| 0.6812 | 60000  | 2.987         | 3.4057          |
| 0.6926 | 61000  | 3.0046        | 3.4094          |
| 0.7040 | 62000  | 2.984         | 3.4076          |
| 0.7153 | 63000  | 2.9834        | 3.4090          |
| 0.7267 | 64000  | 2.9932        | 3.4038          |
| 0.7380 | 65000  | 2.9829        | 3.4009          |
| 0.7494 | 66000  | 2.9976        | 3.4053          |
| 0.7607 | 67000  | 2.9868        | 3.3996          |
| 0.7721 | 68000  | 2.9925        | 3.3988          |
| 0.7834 | 69000  | 2.9935        | 3.4042          |
| 0.7948 | 70000  | 2.9877        | 3.4072          |
| 0.8061 | 71000  | 2.995         | 3.4045          |
| 0.8175 | 72000  | 2.9949        | 3.3988          |
| 0.8288 | 73000  | 2.9969        | 3.4013          |
| 0.8402 | 74000  | 3.0033        | 3.4027          |
| 0.8516 | 75000  | 2.99          | 3.4041          |
| 0.8629 | 76000  | 3.0038        | 3.3999          |
| 0.8743 | 77000  | 3.0072        | 3.4022          |
| 0.8856 | 78000  | 2.9878        | 3.4001          |
| 0.8970 | 79000  | 2.9821        | 3.3992          |
| 0.9083 | 80000  | 2.9921        | 3.3995          |
| 0.9197 | 81000  | 2.9959        | 3.3977          |
| 0.9310 | 82000  | 3.0004        | 3.3963          |
| 0.9424 | 83000  | 2.9784        | 3.4021          |
| 0.9537 | 84000  | 2.9923        | 3.3998          |
| 0.9651 | 85000  | 2.9836        | 3.3972          |
| 0.9765 | 86000  | 2.9949        | 3.3971          |
| 0.9878 | 87000  | 2.9925        | 3.3968          |
| 0.9992 | 88000  | 2.9777        | 3.3947          |
| 1.0105 | 89000  | 2.9785        | 3.3975          |
| 1.0219 | 90000  | 2.9988        | 3.3974          |
| 1.0332 | 91000  | 2.9898        | 3.3954          |
| 1.0446 | 92000  | 2.9866        | 3.3943          |
| 1.0559 | 93000  | 2.9909        | 3.3936          |
| 1.0673 | 94000  | 2.9843        | 3.3942          |
| 1.0786 | 95000  | 2.9858        | 3.3924          |
| 1.0900 | 96000  | 2.9942        | 3.3927          |
| 1.1013 | 97000  | 2.9955        | 3.3936          |
| 1.1127 | 98000  | 3.0003        | 3.3921          |
| 1.1241 | 99000  | 2.9878        | 3.3947          |
| 1.1354 | 100000 | 2.9972        | 3.3951          |
| 1.1468 | 101000 | 2.9874        | 3.3999          |
| 1.1581 | 102000 | 2.9828        | 3.3950          |
| 1.1695 | 103000 | 2.9956        | 3.3929          |
| 1.1808 | 104000 | 2.9886        | 3.3935          |
| 1.1922 | 105000 | 2.982         | 3.3921          |
| 1.2035 | 106000 | 2.9913        | 3.3916          |
| 1.2149 | 107000 | 2.9831        | 3.3924          |
| 1.2262 | 108000 | 2.9958        | 3.3926          |
| 1.2376 | 109000 | 2.9969        | 3.3924          |
| 1.2489 | 110000 | 2.9893        | 3.3920          |
| 1.2603 | 111000 | 2.9888        | 3.3936          |
| 1.2717 | 112000 | 2.9885        | 3.3925          |
| 1.2830 | 113000 | 2.9866        | 3.3913          |
| 1.2944 | 114000 | 2.9885        | 3.3907          |
| 1.3057 | 115000 | 2.9782        | 3.3917          |
| 1.3171 | 116000 | 2.9816        | 3.3907          |
| 1.3284 | 117000 | 2.9857        | 3.3923          |
| 1.3398 | 118000 | 2.9824        | 3.3925          |
| 1.3511 | 119000 | 2.9966        | 3.3911          |
| 1.3625 | 120000 | 2.9951        | 3.3923          |
| 1.3738 | 121000 | 2.9914        | 3.3907          |
| 1.3852 | 122000 | 2.9745        | 3.3916          |
| 1.3966 | 123000 | 3.0008        | 3.3928          |
| 1.4079 | 124000 | 2.9787        | 3.3942          |
| 1.4193 | 125000 | 2.9789        | 3.3929          |
| 1.4306 | 126000 | 2.9845        | 3.3928          |
| 1.4420 | 127000 | 2.9792        | 3.3919          |
| 1.4533 | 128000 | 2.9847        | 3.3911          |
| 1.4647 | 129000 | 2.9905        | 3.3910          |
| 1.4760 | 130000 | 2.9878        | 3.3916          |
| 1.4874 | 131000 | 2.987         | 3.3918          |
| 1.4987 | 132000 | 3.0025        | 3.3915          |
| 1.5101 | 133000 | 2.9829        | 3.3911          |
| 1.5214 | 134000 | 2.982         | 3.3914          |
| 1.5328 | 135000 | 2.9923        | 3.3912          |
| 1.5442 | 136000 | 2.9849        | 3.3918          |
| 1.5555 | 137000 | 3.0002        | 3.3917          |
| 1.5669 | 138000 | 2.9845        | 3.3918          |
| 1.5782 | 139000 | 2.9906        | 3.3923          |
| 1.5896 | 140000 | 2.9897        | 3.3921          |
| 1.6009 | 141000 | 2.9813        | 3.3919          |
| 1.6123 | 142000 | 2.9992        | 3.3919          |
| 1.6236 | 143000 | 2.9872        | 3.3919          |
| 1.6350 | 144000 | 2.9847        | 3.3919          |
| 1.6463 | 145000 | 2.994         | 3.3917          |
| 1.6577 | 146000 | 2.982         | 3.3916          |
| 1.6691 | 147000 | 2.9994        | 3.3914          |
| 1.6804 | 148000 | 2.9817        | 3.3914          |
| 1.6918 | 149000 | 2.9889        | 3.3914          |
| 1.7031 | 150000 | 2.9864        | 3.3914          |
| 1.7145 | 151000 | 2.9912        | 3.3913          |
| 1.7258 | 152000 | 2.9852        | 3.3912          |
| 1.7372 | 153000 | 2.987         | 3.3912          |
| 1.7485 | 154000 | 2.9762        | 3.3912          |
| 1.7599 | 155000 | 2.9864        | 3.3912          |
| 1.7712 | 156000 | 2.9947        | 3.3912          |
| 1.7826 | 157000 | 2.9937        | 3.3911          |
| 1.7939 | 158000 | 3.004         | 3.3912          |
| 1.8053 | 159000 | 2.9804        | 3.3912          |
| 1.8167 | 160000 | 2.9928        | 3.3912          |
| 1.8280 | 161000 | 2.9966        | 3.3912          |
| 1.8394 | 162000 | 2.9902        | 3.3912          |
| 1.8507 | 163000 | 2.9807        | 3.3912          |
| 1.8621 | 164000 | 2.9782        | 3.3911          |
| 1.8734 | 165000 | 2.9963        | 3.3912          |
| 1.8848 | 166000 | 2.9911        | 3.3911          |
| 1.8961 | 167000 | 2.9969        | 3.3911          |
| 1.9075 | 168000 | 2.9951        | 3.3911          |
| 1.9188 | 169000 | 2.9948        | 3.3911          |
| 1.9302 | 170000 | 2.9931        | 3.3911          |
| 1.9415 | 171000 | 2.9895        | 3.3911          |
| 1.9529 | 172000 | 2.9846        | 3.3911          |
| 1.9643 | 173000 | 2.9888        | 3.3911          |
| 1.9756 | 174000 | 2.9833        | 3.3911          |
| 1.9870 | 175000 | 2.9816        | 3.3911          |
| 1.9983 | 176000 | 2.9929        | 3.3911          |

</details>

### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0

## 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",
}
```

#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

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