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leafxyz/qwen3-embedding-0.6b-arabic-ecom

sourceHugging Faceupdated 2mo agoView on Hugging Face
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Model Card

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the positives and pairswithnegatives 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: Qwen/Qwen3-Embedding-0.6B <!-- at revision 97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3 -->
  • —Maximum Sequence Length: 128 tokens
  • —Output Dimensionality: 1024 dimensions
  • —Similarity Function: Cosine Similarity
  • —Supported Modality: Text
  • —Training Datasets:
  • —positives
  • —pairswithnegatives <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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("sentence_transformers_model_id")
# Run inference
queries = [
    'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: فمّا ساعة منبه',
]
documents = [
    'ساعة منبه وردي - QUARTZ',
    'عدسات كاميرات سداسية - ازرق',
    'قلم ايباد - XO-ST-10',
]
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([[0.4883, 0.0481, 0.0815]], dtype=torch.bfloat16)

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Downstream Usage (Sentence Transformers)

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Training Details

Training Datasets

positives
  • —Dataset: positives
  • —Size: 558,253 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: 29.07 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.99 tokens</li><li>max: 27 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 تشيبس حارة</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: دوريستوس طوفان توابل</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: تشيبس</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> |
  • —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 8,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
pairswithnegatives
  • —Dataset: pairswithnegatives
  • —Size: 125,517 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: 28.74 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.51 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.81 tokens</li><li>max: 30 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: دوريستوس طوفان توابل</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> | <code>شيبس جبنة الناتشو - Doritos</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نحب تشيبس حارة</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> | <code>شيبس جبنة الناتشو - Doritos</code> | | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: دوريستوس</code> | <code>شيبس التوابل الحارة - Doritos Storm</code> | <code>شيبس جبنة الناتشو - Doritos</code> |
  • —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 8,
      "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
  • —learning_rate: 0.0001
  • —max_steps: 500
  • —lr_scheduler_type: cosine
  • —warmup_steps: 0.05
  • —gradient_checkpointing: 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: 1
  • —eval_accumulation_steps: None
  • —torch_empty_cache_steps: None
  • —learning_rate: 0.0001
  • —weight_decay: 0.0
  • —adam_beta1: 0.9
  • —adam_beta2: 0.999
  • —adam_epsilon: 1e-08
  • —max_grad_norm: 1.0
  • —num_train_epochs: 3.0
  • —max_steps: 500
  • —lr_scheduler_type: cosine
  • —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: False
  • —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: 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

EpochStepTraining Loss
0.0012250.6661
0.0023500.5316
0.0035750.4130
0.00471000.5524
0.00581250.3809
0.00701500.4590
0.00821750.3889
0.00942000.3555
0.01052250.3777
0.01172500.4123
0.01292750.3507
0.01403000.4022
0.01523250.3623
0.01643500.4201
0.01753750.4134
0.01874000.3386
0.01994250.2656
0.02114500.3384
0.02224750.3705
0.02345000.3033

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",
}
CachedMultipleNegativesRankingLoss
bibtex
@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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