---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:90678
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
widget:
- source_sentence: 'ماهي الكلمه التي تعني: حزن الشخص وجرت دمعته.'
  sentences:
  - استذكاء
  - مستعبر
  - تشخيصه
- source_sentence: 'ماهي الكلمه التي تعني: المره من تناول طعام يسير؛ لتهدئه الجوع
    مؤقتا.'
  sentences:
  - صاحن
  - ادعج
  - تسكيته
- source_sentence: 'ماهي الكلمه التي تعني: اعتياد التقشف وشظف العيش.'
  sentences:
  - اخشيشان
  - هزيم
  - استذهال
- source_sentence: 'ماهي الكلمه التي تعني: تعب مرهق منهك القوى.'
  sentences:
  - تلفان
  - نقزه
  - عامل
- source_sentence: 'ماهي الكلمه التي تعني: بال قديم، عديم القيمه.'
  sentences:
  - ادرن
  - خثير
  - هريء
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2) <!-- at revision 408d483803e83aaea0aceec550deac66e5f8dc11 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'ماهي الكلمه التي تعني: بال قديم، عديم القيمه.',
    'هريء',
    'ادرن',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4617, 0.1454],
#         [0.4617, 1.0000, 0.0522],
#         [0.1454, 0.0522, 1.0000]])
```
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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 90,678 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 100 samples:
  |          | anchor                                                                             | positive                                                                        |
  |:---------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type     | string                                                                             | string                                                                          |
  | modality | text                                                                               | text                                                                            |
  | details  | <ul><li>min: 10 tokens</li><li>mean: 14.58 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.75 tokens</li><li>max: 5 tokens</li></ul> |
* Samples:
  | anchor                                                                                               | positive             |
  |:-----------------------------------------------------------------------------------------------------|:---------------------|
  | <code>ماهي الكلمه التي تعني: وفقا للشيء.</code>                                                      | <code>تبعا لـ</code> |
  | <code>ماهي الكلمه التي تعني: مركب لنقل الناس او البضائع في البحر او النهر او الفضاء الخارجي .</code> | <code>سفين</code>    |
  | <code>ماهي الكلمه التي تعني: المهزوم.</code>                                                         | <code>هزيم</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
      ],
      "matryoshka_weights": [
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `per_device_train_batch_size`: 128
- `num_train_epochs`: 5
- `warmup_steps`: 0.1
- `gradient_accumulation_steps`: 2
- `bf16`: True
- `batch_sampler`: no_duplicates

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

- `per_device_train_batch_size`: 128
- `num_train_epochs`: 5
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 2
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.2821 | 100  | 1.8383        |
| 0.5642 | 200  | 1.3010        |
| 0.8463 | 300  | 1.1525        |
| 1.1269 | 400  | 0.9740        |
| 1.4090 | 500  | 0.8594        |
| 1.6911 | 600  | 0.8258        |
| 1.9732 | 700  | 0.8039        |
| 2.2539 | 800  | 0.6164        |
| 2.5360 | 900  | 0.6076        |
| 2.8181 | 1000 | 0.6035        |
| 3.0987 | 1100 | 0.5412        |
| 3.3808 | 1200 | 0.4620        |
| 3.6629 | 1300 | 0.4595        |
| 3.9450 | 1400 | 0.4667        |
| 4.2257 | 1500 | 0.4030        |
| 4.5078 | 1600 | 0.3940        |
| 4.7898 | 1700 | 0.3759        |


### Training Time
- **Training**: 10.7 minutes

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.5.1
- Transformers: 5.9.0
- PyTorch: 2.11.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

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