leafxyz
/

main_v1

@@ -1,496 +1,24 @@
 ---
 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")
-# 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]])
-```
-<!--
-### Direct Usage (Transformers)
-<details><summary>Click to see the direct usage in Transformers</summary>
-</details>
--->
-<!--
-### Downstream Usage (Sentence Transformers)
-You can finetune this model on your own dataset.
-<details><summary>Click to expand</summary>
-</details>
--->
-<!--
-### Out-of-Scope Use
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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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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-### Recommendations
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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**: 2.0 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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 ---
+language:
+- ar
 tags:
+- dense-retrieval
+- bi-encoder
 - sentence-transformers
+metrics:
+- loss
 ---
+# Bi-Encoder Retrieval Model (`leafxyz/main`)
+## 📊 Complete Training Artifacts & Logs
+- **Architecture**: Bi-Encoder (Dense Vector Retrieval)
+- **Loss Function**: `MultipleNegativesRankingLoss`
+- **Training Step**: `500`
+- **Training Loss**: `0.177432`
+- **Positives Loss**: `0.111283`
+- **Pairs with Negatives Loss**: `0.330405`
+## 🗂️ Dataset Splits & Mixture
+Data splits (Train/Validation/Test) are stored at:
+👉 [leafxyz/arabic-retrieval-mixture-v2](https://huggingface.co/datasets/leafxyz/arabic-retrieval-mixture-v2)