leafxyz/main

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

This is a sentence-transformers model finetuned from prestoai/qwen3-embedding-0.6b-arabic-ecom on the pairswithnegatives 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 <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 -->
• —Maximum Sequence Length: 64 tokens
• —Output Dimensionality: 1024 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text
• —Training Datasets:
• —pairswithnegatives
• —positives <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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: شاحن ايفون BAVIN',
]
documents = [
    'كابل شحن ايفون  BAVIN - 2.4A CB-015',
    'كابل شحن تايب سي BAVIN -  2.4A',
    'مزيل طلاء اظافر  04 - Acetone',
]
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([[ 0.6069,  0.4563, -0.0245]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Datasets

pairswithnegatives

• —Dataset: pairswithnegatives
• —Size: 23,680 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: 23 tokens</li><li>mean: 29.53 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.91 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.13 tokens</li><li>max: 38 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: صدر دجاج كمون</code> | <code>صدر دجاج بالعظم</code> | <code>صدر دجاج مجمد - الاولى</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: حليب</code> | <code>حليب الزهرات - 410 غ</code> | <code>بدلة نسائية - 051</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: عصائر</code> | <code>عصير بيلو برتقال - 330 غ</code> | <code>D5699-زي تنكري </code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "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: 105,019 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: 29.41 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 13.66 tokens</li><li>max: 36 tokens</li></ul> |
• —Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نحب مكرونة خرز</code> | <code>مكرونة خرز 2</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نحب حذاء افراح فضي</code> | <code>حذاء افراح - 5142wo</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بودرة بدون عطور</code> | <code>Baby Powder - Nunu</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "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

pairswithnegatives

• —Dataset: pairswithnegatives
• —Size: 240 evaluation samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• —Approximate statistics based on the first 240 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 24 tokens</li><li>mean: 29.85 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.65 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.61 tokens</li><li>max: 33 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: سماعات لاسلكية</code> | <code>سماعات بلوتوث - Moxom</code> | <code>كريم مزيل عرق بالجلسرين - Roncey</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نشتي تابل فلفل</code> | <code>تابل كارمنسيتا 4 انواع فلفل مطحنة - 145 غ</code> | <code>تابل كارمنسيتا فلفل اسود حب مطحنة -47 غ</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: صلصة تريكي حدائق سويسرا 250</code> | <code>صلصة التريكي حدائق سويسرا - 250 مل</code> | <code>صلصة وسترشاير حدائق سويسرا - 250 مل</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "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: 1,061 evaluation 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: 29.35 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.76 tokens</li><li>max: 37 tokens</li></ul> |
• —Samples: | anchor | positive | |:----------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كفر</code> | <code>كفر - 876</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بيرير شعير</code> | <code>بيرير شعير 330ملي</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: مكيف هواء 01 1.5 طن</code> | <code>مكيف هواء - 01</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "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

• —per_device_train_batch_size: 16
• —gradient_accumulation_steps: 2
• —learning_rate: 3e-05
• —num_train_epochs: 1
• —warmup_steps: 0.05
• —fp16: True

All Hyperparameters

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

• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 8
• —gradient_accumulation_steps: 2
• —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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

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

</details>

Training Time

• —Training: 1.5 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

@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

@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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