| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:72985 |
| - loss:MultipleNegativesRankingLoss |
| base_model: Qwen/Qwen3-Embedding-0.6B |
| widget: |
| - source_sentence: kablosuz klavye mouse set |
| sentences: |
| - Dexim KM-317 Slim DKM004 Kablosuz Klavye Mouse Set |
| - EXEP BASİC BEYAZ 200 3 LÜ ANKASTRE SET (ED400-EO320-EF200) |
| - Samsung Galaxy A17 128 GB 4 GB Siyah Cep Telefonu |
| - source_sentence: masa tenisi filesi |
| sentences: |
| - Motul 8100 X-Cess Gen2 5W40 5lt |
| - BUFFER® Mekanizmalı Taşınabilir Kaymaz Masa Tenisi Filesi Ağı Portatif Tüm Masalara |
| Uyumlu |
| - Robin Home Dragon Oyuncu Masası Gamer Masa Gaming Bilgisayar Masası Siyah |
| - source_sentence: yapışkanlı banyo rafı |
| sentences: |
| - Çaykur Tiryaki 5 kg Çay |
| - Yapışkanlı 2 Adet Banyo Düzenleyici Rafı ve Katı Sabunluk Bulaşık Süngerlik Seti |
| - HESA Dekoratif 5 Raflı Ahşap Ayaklı Kitaplık Ayakkabılık mutfak rafı - Atlantik |
| Çam |
| - source_sentence: raflı kitaplık |
| sentences: |
| - Apple iPhone 16e 128GB Beyaz |
| - Morpanya Sera Metal Kitaplık 5 Raflı Çok Amaçlı Dosya Kitap Rafı Ofis Salon Mutfak |
| Raf 150 cm Ceviz |
| - Piev House by House Glow Vitrin 360 Döner Standlı Çekmeceli Raflı Hazneli Makyaj |
| Kozmetik Organizer |
| - source_sentence: balon standı |
| sentences: |
| - Mermer Ayaklı Gümüş / Metal Banyo El Havluluk Kağıt Havluluk Standı |
| - 2 Adet Balon Süsleme Standı 7li Çubuklu Ikili Set Ayaklı Standı |
| - Philips Avent Hızlı Biberon Isıtıcı SCF355/07 |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). 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](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 256, '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: |
|
|
| ```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("turanyigitpazarama/qwen-0.6B-turkish-ecommerce-tuned") |
| # Run inference |
| queries = [ |
| "balon stand\u0131", |
| ] |
| documents = [ |
| '2 Adet Balon Süsleme Standı 7li Çubuklu Ikili Set Ayaklı Standı', |
| 'Mermer Ayaklı Gümüş / Metal Banyo El Havluluk Kağıt Havluluk Standı', |
| 'Philips Avent Hızlı Biberon Isıtıcı SCF355/07', |
| ] |
| 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([[ 0.6758, 0.3398, -0.0081]], dtype=torch.bfloat16) |
| ``` |
|
|
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 72,985 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | sentence_2 | |
| |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 2 tokens</li><li>mean: 7.15 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 26.01 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 27.99 tokens</li><li>max: 102 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | |
| |:----------------------------------|:------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------| |
| | <code>ayçiçek yağı</code> | <code>Komili Ayçiçek Yağı 4 lt</code> | <code>Shiffa Home Katı Hindistan Cevizi Yağı 330 ml.</code> | |
| | <code>the purest solutions</code> | <code>The Purest Solutions Bha %2 Oil Control Toner & Siyah Nokta Hedefli, Yağlanma, Gözenek Dengeleyici T</code> | <code>Stanley The AeroLight Transit Lacivert 0.35 lt Termos Bardak</code> | |
| | <code>banyo paspası</code> | <code>Eko Trend Djt 3 Lü Yıkanabilir Kaymaz Taban Banyo Paspas Seti 747 Klasik</code> | <code>Sepetli Bursa Banyo Dolabı</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 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 16 |
| - `num_train_epochs`: 3 |
| - `max_steps`: -1 |
| - `learning_rate`: 5e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: False |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `eval_strategy`: no |
| - `per_device_eval_batch_size`: 16 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: 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 |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:-----:|:-------------:| |
| | 0.1096 | 500 | 0.3013 | |
| | 0.2192 | 1000 | 0.1969 | |
| | 0.3288 | 1500 | 0.1656 | |
| | 0.4384 | 2000 | 0.1587 | |
| | 0.5480 | 2500 | 0.1445 | |
| | 0.6576 | 3000 | 0.1511 | |
| | 0.7672 | 3500 | 0.1298 | |
| | 0.8768 | 4000 | 0.1384 | |
| | 0.9864 | 4500 | 0.1397 | |
| | 1.0960 | 5000 | 0.0981 | |
| | 1.2056 | 5500 | 0.0914 | |
| | 1.3152 | 6000 | 0.0862 | |
| | 1.4248 | 6500 | 0.0882 | |
| | 1.5344 | 7000 | 0.0902 | |
| | 1.6440 | 7500 | 0.0855 | |
| | 1.7536 | 8000 | 0.0863 | |
| | 1.8632 | 8500 | 0.0929 | |
| | 1.9728 | 9000 | 0.0868 | |
| | 2.0824 | 9500 | 0.0795 | |
| | 2.1920 | 10000 | 0.0801 | |
| | 2.3016 | 10500 | 0.0799 | |
| | 2.4112 | 11000 | 0.0778 | |
| | 2.5208 | 11500 | 0.0777 | |
| | 2.6304 | 12000 | 0.0821 | |
| | 2.7400 | 12500 | 0.0730 | |
| | 2.8496 | 13000 | 0.0742 | |
| | 2.9592 | 13500 | 0.0763 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.13.2 |
| - Sentence Transformers: 5.1.1 |
| - Transformers: 5.2.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.10.1 |
| - Datasets: 3.6.0 |
| - 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{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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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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