main_v2 / README.md

Add new SentenceTransformer model

5383a6a verified 19 days ago

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	---
	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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	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

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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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