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gromoboy
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qwen3_06b_items_matcher

Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
dense
Generated from Trainer
dataset_size:8914
loss:MultipleNegativesRankingLoss
Eval Results
Model card Files
xet
Community

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the data1 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: Qwen/Qwen3-Embedding-0.6B
  • Maximum Sequence Length: 32768 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • data1

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, '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("gromoboy/qwen3_06b_items_matcher")
# Run inference
queries = [
    "\u041e\u0434\u0435\u0436\u0434\u0430 \u0434\u043b\u044f \u043d\u043e\u0432\u043e\u0440\u043e\u0436\u0434\u0435\u043d\u043d\u044b\u0445 \u043c\u0430\u043b\u044c\u0447\u0438\u043a\u043e\u0432 \u0441\u043b\u0438\u043f \u0434\u043b\u044f \u043c\u0430\u043b\u044b\u0448\u0435\u0439 \u043a\u043e\u043c\u0431\u0438\u043d\u0435\u0437\u043e\u043d \u043d\u0430\u0440\u044f\u0434\u043d\u044b\u0439 \u043d\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0434\u043b\u044f \u0444\u043e\u0442\u043e\u0441\u0435\u0441\u0441\u0438\u0438",
]
documents = [
    'Одежда для новорожденных мальчиков слипдля малышей комбинезон нарядный нательный для фотосесии',
    'Шапка детская для мальчика и снуд',
    'ТелескопРефрактор/Детский игровойнабор',
]
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([[0.9531, 0.2704, 0.1847]])

Evaluation

Metrics

Triplet

  • Dataset: dev
  • Evaluated with TripletEvaluator with these parameters:
    {
    "margin": {
        "cosine": 0.3,
        "dot": 0.3,
        "manhattan": 0.3,
        "euclidean": 0.3
    }
}
Metric Value
cosine_accuracy 0.9423

Training Details

Training Dataset

data1

  • Dataset: data1
  • Size: 8,914 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 6 tokens
    • mean: 14.74 tokens
    • max: 46 tokens
    • min: 36 tokens
    • mean: 51.42 tokens
    • max: 86 tokens
    • min: 6 tokens
    • mean: 14.61 tokens
    • max: 46 tokens
  • Samples:
    anchor positive negative
    Cоуc рыбный Cook&Lobster Соус рыбный, Таиланд 750мл*12 ,стекло (Штук/ящ: [12], Вес в кг: [1.448] Соус устричный Genso
    Cоуc рыбный Cook&Lobster Соус рыбный, Таиланд, 700мл*12 (Штук/ящ: [12], Вес в кг: [1.250] Соус устричный Genso
    Kimchi Чипсы нори Tidori Чипсы нори TIDORI, Корея, Kimchi, 15г (5г х 3) * 24 (Штук/ящ: [24], Вес в кг: [0.038] Original Чипсы нори Tidori
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 25,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

data1

  • Dataset: data1
  • Size: 2,288 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 6 tokens
    • mean: 19.04 tokens
    • max: 83 tokens
    • min: 6 tokens
    • mean: 34.31 tokens
    • max: 88 tokens
    • min: 6 tokens
    • mean: 18.38 tokens
    • max: 100 tokens
  • Samples:
    anchor positive negative
    BBQ Чипсы нори Tidori Чипсы нори TIDORI, Корея, BBQ, 15г (5г х 3) * 24 (Штук/ящ: [24], Вес в кг: [0.038] Kimchi Чипсы нори Tidori
    Original Чипсы нори Tidori Чипсы нори TIDORI, Корея, Original, 15г (5г х 3) * 24 (Штук/ящ: [24], Вес в кг: [0.038] Kimchi Чипсы нори Tidori
    Авокадо пюре десертное с кокосом, голубикой и сиропом агавы, быстрозамороженное, блок (57 г*4) Авокадо пюре десерт. с КОКОСОМ, ГОЛУБИКОЙ и сиропом агавы, быстрозамороженный 227гр12 блок (57гр4) (Штук/ящ: [12], Вес в кг: [0.235] Авокадо пюре с киви, мятой и сиропом агавы, быстрозамороженное, блок (57 г*4)
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 25,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • warmup_ratio: 0.1
  • fp16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-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: 5
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • 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
  • use_legacy_prediction_loop: False
  • 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_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • 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: False
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss Validation Loss dev_cosine_accuracy
-1 -1 - - 0.5848
0.3584 100 - 0.0570 0.9030
0.7168 200 0.0638 0.0504 0.9008
1.0753 300 - 0.0431 0.9331
1.4337 400 0.0067 0.0385 0.9292
1.7921 500 - 0.0715 0.9191
2.1505 600 0.0045 0.0664 0.9309
2.5090 700 - 0.0620 0.9414
2.8674 800 0.0029 0.0532 0.9467
3.2258 900 - 0.0586 0.9432
3.5842 1000 0.0041 0.0431 0.9432
3.9427 1100 - 0.0464 0.9432
4.3011 1200 0.0022 0.0611 0.9406
4.6595 1300 - 0.0646 0.9423

Framework Versions

  • Python: 3.11.10
  • Sentence Transformers: 5.0.0
  • Transformers: 4.54.0
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.2

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{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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