--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:62039 - loss:MultipleNegativesRankingLoss base_model: Qwen/Qwen3-Embedding-0.6B widget: - source_sentence: proplan sterilised kısırlaştırılmış somonlu kedi sentences: - ProPlan sterilised somonlu kısırlaştırılmış kedi maması 2 kg açık mama - Homedius Sofa Buklet Kapitoneli Yer-Sedir Minderi - LEGO Ideas 21349 Smokinli Kedi - source_sentence: 40 inc tv sentences: - Xiaomi Yasomi Tefal Philips Karaca 3,5- 4 Litre Uyumlu 7 Inc (18 Cm) 12 Li Airfryer Fritöz Seti - Onvo 40OVF4000AF 40" FHD Frameless Android 13 Smart LED TV - adidas JQ6725 TERREX SKYCHASER AX5 GTX W Kadın Outdoor-Bot - source_sentence: araç lastik şişirme pompası sentences: - 'Elektrikli 220V Otomatik Sistem Metal Dişli Bakır Sargılı Su Pompası 0.50HP Ev Tipi Paket Hidrofor ' - Tutku İç Giyim Pamuklu Erkek Slip Külot Don 6 Lı Paket - Araba Oto Araç Lastik Şişirme Pompası Çift Silindirli 628 - source_sentence: new balance sentences: - Yenilenmiş APPLE IPHONE 15 PRO MAX 256GB SİYAH TİTANYUM İYİ - New Balance 740 Lifestyle Unisex Spor Ayakkabı - Maybelline New York Lifter Glaze Shea Yağı ve Hyalüronik Asit içeren Renkli Dudak Balmı - 08 Acai Glaze - source_sentence: xiaomi mi 11t 5g kılıf sentences: - Samsung Galaxy S25 FE 256 GB 8 GB Ram Lacivet - Xiaomi Mi 11T Pro 5G Uyumlu Kılıf Esnek ve Darbe Emici Renkli Koruyucu Kapak - Xiaomi Redmi Note 13 Pro Mor 256 GB 8 GB Ram Akıllı Telefon ( Xiaomi Türkiye Garantili ) 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:** 32768 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': 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: ```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 = [ "xiaomi mi 11t 5g k\u0131l\u0131f", ] documents = [ 'Xiaomi Mi 11T Pro 5G Uyumlu Kılıf Esnek ve Darbe Emici Renkli Koruyucu Kapak', 'Xiaomi Redmi Note 13 Pro Mor 256 GB 8 GB Ram Akıllı Telefon ( Xiaomi Türkiye Garantili )', 'Samsung Galaxy S25 FE 256 GB 8 GB Ram Lacivet', ] 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([[ 0.6297, 0.1476, -0.1547]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 62,039 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 | |:----------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------| | 12v akü | VARTA 12V 60 AH EN 540 D24 AKÜ | Einhell Te-Cı 18/1 Li - Solo Torklu Darbeli Matkap + 2 x 2.5 Ah Starter Kit Akü | | çekmeceli şifonyer | Arden 5 Çekmeceli Şifonyer , Çamaşırlık | ALTUS AL 708 NE E Enerji Sınıfı 244 L 7 Çekmeceli Derin Dondurucu | | philips elektrikli süpürge | Philips PowerPro City Fc9331/07 Toz Torbasız Elektrikli Süpürge | ERKUGO Elektrikli Bebek Tırnak Törpüsü Asortili | * 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 - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.1289 | 500 | 0.2607 | | 0.2579 | 1000 | 0.1956 | | 0.3868 | 1500 | 0.1726 | | 0.5157 | 2000 | 0.1653 | | 0.6447 | 2500 | 0.1578 | | 0.7736 | 3000 | 0.138 | | 0.9025 | 3500 | 0.1355 | | 1.0315 | 4000 | 0.1341 | | 1.1604 | 4500 | 0.0786 | | 1.2893 | 5000 | 0.0769 | | 1.4183 | 5500 | 0.0824 | | 1.5472 | 6000 | 0.0755 | | 1.6761 | 6500 | 0.0761 | | 1.8051 | 7000 | 0.0713 | | 1.9340 | 7500 | 0.071 | | 2.0629 | 8000 | 0.0574 | | 2.1919 | 8500 | 0.049 | | 2.3208 | 9000 | 0.0574 | | 2.4497 | 9500 | 0.0468 | | 2.5786 | 10000 | 0.0451 | | 2.7076 | 10500 | 0.0414 | | 2.8365 | 11000 | 0.0445 | | 2.9654 | 11500 | 0.0397 | ### Framework Versions - Python: 3.13.2 - Sentence Transformers: 5.1.1 - Transformers: 4.56.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 3.6.0 - Tokenizers: 0.22.1 ## 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} } ```