models/ksaa_custom/README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:131044
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
widget:
- source_sentence: 'ماهي الكلمه التي تعني: عمله يتم تداولها بين مجموعه من الدول المتحالفه
    اقتصاديا بدلا من عملاتها المحليه.'
  sentences:
  - 'رجحان، وترجمتها: appropriate، وقسمها الكلامي: اسم معنى'
  - محيا
  - العمله الموحده
- source_sentence: 'ماهي الكلمه التي تعني: تطابق اللفظين او تشابههما في المعنى.'
  sentences:
  - 'ترادف، وترجمتها: synonymity، وقسمها الكلامي: اسم معنى'
  - تابن
  - 'شحذ، وترجمتها: To whet، وقسمها الكلامي: فعل متعدي'
- source_sentence: 'ماهي الكلمه التي تعني: مصطلح على رمز الطرح في الحساب.'
  sentences:
  - 'ناقص، وقسمها الكلامي: اسم ذات'
  - ملغوب في
  - 'استعاره، وترجمتها: borrowing، وقسمها الكلامي: اسم معنى'
- source_sentence: 'ماهي الكلمه التي تعني: توثيق، واحكام.'
  sentences:
  - تلاحم
  - 'متفق، ومثال الكلمه هو: منذ البدايه اقول باني (متفق) معه في معظم ما قاله.، وترجمتها:
    Agreeing with، وقسمها الكلامي: صفه فاعل'
  - توكيد
- source_sentence: 'ماهي الكلمه التي تعني: تحزم بالثوب وشده تهيؤا لامر واستعدادا له،
    او جذب شخص من ثيابه التي عند عنقه'
  sentences:
  - تحشيه
  - انذر
  - تلبب
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: تحزم بالثوب وشده تهيؤا لامر واستعدادا له، او جذب شخص من ثيابه التي عند عنقه',
    'تلبب',
    'تحشيه',
]
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,  0.4581,  0.1173],
#         [ 0.4581,  1.0000, -0.0107],
#         [ 0.1173, -0.0107,  1.0000]])
```
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You can finetune this model on your own dataset.

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 131,044 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: 9 tokens</li><li>mean: 17.41 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.64 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.97 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                | positive                                                     | negative                            |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|:------------------------------------|
  | <code>ماهي الكلمه التي تعني: فتر فلان همه فلان: اضعفها.</code>                                                                                                        | <code>فتر</code>                                             | <code>هتف</code>                    |
  | <code>ماهي الكلمه التي تعني: عاصمه ايرلندا، تقع قرب منتصف الساحل الشرقي الايرلندي، عند مصب نهر ليفي، وتقدر مساحتها بنحو 117.8  كيلو متر مربعا.</code>                 | <code>دبلن، وترجمتها: Dublin، وقسمها الكلامي: اسم ذات</code> | <code>واشنطن</code>                 |
  | <code>ماهي الكلمه التي تعني: النظام الذي يكون فيه الاقتران الذي يختلف عن اقتران المعادله الاصلي لا يساوي صفرا سواء اكانت المعادله من الرتبه الاولى او الثانيه.</code> | <code>معادله غير متجانسه</code>                              | <code>سياسه الاستخدام العادل</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
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 14,561 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: 9 tokens</li><li>mean: 17.36 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.68 tokens</li><li>max: 313 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.97 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                                                 | positive                                                                                                            | negative           |
  |:-----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------|
  | <code>ماهي الكلمه التي تعني: مؤنث (رخو)</code>                         | <code>رخوه، وقسمها الكلامي: صفه مشبهه</code>                                                                        | <code>رخويه</code> |
  | <code>ماهي الكلمه التي تعني: مزخرف ومرصع</code>                        | <code>مبرقش</code>                                                                                                  | <code>مخاف</code>  |
  | <code>ماهي الكلمه التي تعني: مكتسب حرام وغير مشروع من مال ونحوه</code> | <code>مسحت، ومثال الكلمه هو: فانه يضمر في المسحت والمجلف ما يرفعه مثل الذي ونحوه،، وقسمها الكلامي: صفه مفعول</code> | <code>مسحوت</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`: 512
- `per_device_eval_batch_size`: 512
- `num_train_epochs`: 5
- `warmup_steps`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

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

- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0.1
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.3906 | 100  | 4.8407        | 2.1680          |
| 0.7812 | 200  | 3.2924        | 1.7895          |
| 1.1719 | 300  | 2.7619        | 1.6139          |
| 1.5625 | 400  | 2.4898        | 1.5162          |
| 1.9531 | 500  | 2.3912        | 1.4297          |
| 2.3438 | 600  | 2.0123        | 1.3458          |
| 2.7344 | 700  | 1.9851        | 1.3046          |
| 3.125  | 800  | 1.8550        | 1.2478          |
| 3.5156 | 900  | 1.7236        | 1.2185          |
| 3.9062 | 1000 | 1.7033        | 1.1993          |
| 4.2969 | 1100 | 1.5782        | 1.1799          |
| 4.6875 | 1200 | 1.5676        | 1.1659          |


### Training Time
- **Training**: 13.5 minutes

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+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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