---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:49500
- loss:MultipleNegativesRankingLoss
base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom
widget:
- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
    product that best matches it

    Query: عناية بالفم'
  sentences:
  - تن ريقا بزيت الزيتون 160جم
  - Foramen Denture Clean Box
  - صبغة شعر L'Oréal Paris - 5.45 Excellence
- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
    product that best matches it

    Query: بسكويت شوكولاتة بالحليب'
  sentences:
  - بسكويت - Bahlsen
  - حقيبة هدايا - RA040
  - Cicabio Arnica+ - Bioderma
- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
    product that best matches it

    Query: بيتر صودا'
  sentences:
  - ميرندا صودا
  - زبدة فول السوداني حدائق كاليفورنيا ناعمه - 510 غ
  - مشروب بيتر صودا - ميرندا
- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
    product that best matches it

    Query: شيجلام بودرة'
  sentences:
  - بودرة SHEGLAM - High Coverage Linen
  - برايمر فائق الترطيب - SHEGLAM
  - سباتلة حجم صغير تريبولي سنتر
- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
    product that best matches it

    Query: عطور نسائية'
  sentences:
  - تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ
  - مجموعة عطر نسائي - ابراهيم القرشي سكر
  - قبعة رجالية - 07
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) on the pairs_with_negatives and positives datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **Training Datasets:**
    - pairs_with_negatives
    - positives
<!-- - **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': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', '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("leafxyz/main_v2")
# Run inference
queries = [
    'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: عطور نسائية',
]
documents = [
    'مجموعة عطر نسائي - ابراهيم القرشي سكر',
    'قبعة رجالية - 07',
    'تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5593, -0.0069,  0.0400]])
```
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## Training Details

### Training Datasets

#### pairs_with_negatives

* Dataset: pairs_with_negatives
* Size: 9,900 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: 24 tokens</li><li>mean: 29.36 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.82 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 15.12 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                              | positive                                     | negative                                     |
  |:------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: زيت بابايا WKL</code>  | <code>زيت جسم - WKL Papaya</code>            | <code>زيت جسم - Vaseline Cocoa</code>        |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: منكير</code>           | <code>اظافر هيفا - TWINKLE</code>            | <code>اظافر هيفا - SPARKLE</code>            |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: توب فريش حفاضات</code> | <code>توب فريش حفاضات رقم 1 - 44 قطعة</code> | <code>توب فريش حفاضات رقم 2 - 40 قطعة</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

#### positives

* Dataset: positives
* Size: 39,600 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 23 tokens</li><li>mean: 29.52 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.77 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                  | positive                                                 |
  |:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نبي فيكسول ارجواني</code>  | <code>منظف ​​الحمام الذكي فيكسول ارجواني - 900 مل</code> |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: شربة نجمة اريغي 500</code> | <code>شربة نجمة اريغي - 500 غ</code>                     |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: Gas relife drops</code>    | <code>Gas relife drops</code>                            |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

### Evaluation Datasets

#### pairs_with_negatives

* Dataset: pairs_with_negatives
* Size: 100 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 100 samples:
  |         | anchor                                                                             | positive                                                                          | negative                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                           |
  | details | <ul><li>min: 24 tokens</li><li>mean: 29.35 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.2 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                | positive                                                       | negative                                                   |
  |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------|
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: عناية بالجسم</code>      | <code>معطر جسم وشعر نسائي - Sol de Janeiro Água Mística</code> | <code>قارورة عصير - AS02</code>                            |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بخاخ تشيكو 100 مل</code> | <code>بخاخ تشيكو للحماية من البعوض - 100 مل</code>             | <code>مناديل الحماية من البعوض تشيكو - 20 قطعة</code>      |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بخاخ مانع التصاق</code>  | <code>بخاخ الطبخ بنكهة الفلفل مانع للالتصاق - 200 مل</code>    | <code>بخاخ الطبخ بنكهة الثوم مانع للالتصاق - 200 مل</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

#### positives

* Dataset: positives
* Size: 400 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 400 samples:
  |         | anchor                                                                             | positive                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 24 tokens</li><li>mean: 29.43 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.74 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                              | positive                              |
  |:------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: سوار نسائي ذهبي</code> | <code>سوار نسائي - DX052</code>       |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: سناكس</code>           | <code>شوكلاتة كندر ترونكي 8*48</code> |
  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: مشروب حليب</code>      | <code>حليب - Safi</code>              |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

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

- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `warmup_steps`: 0.05
- `fp16`: True
- `dataloader_num_workers`: 2
- `gradient_checkpointing`: True

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

- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-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`: None
- `warmup_ratio`: None
- `warmup_steps`: 0.05
- `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`: 2
- `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`: True
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss | pairs with negatives loss | positives loss |
|:------:|:----:|:-------------:|:-------------------------:|:--------------:|
| 0.0323 | 50   | 0.2092        | -                         | -              |
| 0.0646 | 100  | 0.2008        | -                         | -              |
| 0.0970 | 150  | 0.1948        | -                         | -              |
| 0.1293 | 200  | 0.1749        | -                         | -              |
| 0.1616 | 250  | 0.1455        | -                         | -              |
| 0.1939 | 300  | 0.1924        | -                         | -              |
| 0.2262 | 350  | 0.1959        | -                         | -              |
| 0.2586 | 400  | 0.1606        | -                         | -              |
| 0.2909 | 450  | 0.1679        | -                         | -              |
| 0.3232 | 500  | 0.1774        | 0.3304                    | 0.1113         |
| 0.3555 | 550  | 0.1924        | -                         | -              |
| 0.3878 | 600  | 0.1487        | -                         | -              |
| 0.4202 | 650  | 0.1859        | -                         | -              |
| 0.4525 | 700  | 0.1807        | -                         | -              |
| 0.4848 | 750  | 0.1785        | -                         | -              |
| 0.5171 | 800  | 0.1534        | -                         | -              |
| 0.5495 | 850  | 0.1468        | -                         | -              |
| 0.5818 | 900  | 0.1566        | -                         | -              |
| 0.6141 | 950  | 0.1153        | -                         | -              |
| 0.6464 | 1000 | 0.1322        | 0.3138                    | 0.0943         |
| 0.6787 | 1050 | 0.1320        | -                         | -              |
| 0.7111 | 1100 | 0.1533        | -                         | -              |
| 0.7434 | 1150 | 0.1358        | -                         | -              |
| 0.7757 | 1200 | 0.1457        | -                         | -              |
| 0.8080 | 1250 | 0.1320        | -                         | -              |
| 0.8403 | 1300 | 0.1680        | -                         | -              |
| 0.8727 | 1350 | 0.1280        | -                         | -              |
| 0.9050 | 1400 | 0.1632        | -                         | -              |
| 0.9373 | 1450 | 0.1656        | -                         | -              |
| 0.9696 | 1500 | 0.1363        | 0.3024                    | 0.0914         |


### Training Time
- **Training**: 1.9 hours

### 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: 5.0.0
- 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",
}
```

#### 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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