--- 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) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 72,985 training samples * Columns: sentence_0, sentence_1, and sentence_2 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:----------------------------------|:------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------| | ayçiçek yağı | Komili Ayçiçek Yağı 4 lt | Shiffa Home Katı Hindistan Cevizi Yağı 330 ml. | | the purest solutions | The Purest Solutions Bha %2 Oil Control Toner & Siyah Nokta Hedefli, Yağlanma, Gözenek Dengeleyici T | Stanley The AeroLight Transit Lacivert 0.35 lt Termos Bardak | | banyo paspası | Eko Trend Djt 3 Lü Yıkanabilir Kaymaz Taban Banyo Paspas Seti 747 Klasik | Sepetli Bursa Banyo Dolabı | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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`: {}
### 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} } ```