--- base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:410745 - loss:ContrastiveLoss widget: - source_sentence: وینچ sentences: - ترقه شکلاتی ( هفت ترقه ) ناریه پارس درجه 1 بسته 15 عددی ترقه شکلاتی ( هفت ترقه ) ناریه پارس درجه 1 بسته 15 عددی 10عدد ناریه ترقه شکلاتی هفت ترقه بار تازه بدون رطوبت وخرابی مارک معتبر نورافشانی - پارچه میکرو کجراه - Car winch-1500LBS-KARA وینچ خودرو آفرود ۶۸۰ کیلوگرم کارا ۱۵۰۰lbs وینچ خودرویی (جلو ماشینی) 1500LBS کارا (KARA) - source_sentence: ' وسپا ' sentences: - پولوشرت زرد وسپا - دوچرخه بند سقفی لیفان X70 ایکس 70 آلومینیومی طرح منابو - دوچرخه ویوا Oxygen سایز 26 دوچرخه 26 ويوا OXYGEN دوچرخه کوهستان ویوا مدل OXYGEN سایز 26 - source_sentence: دوچرخه المپیا سایز 27 5 sentences: - دوچرخه شهری المپیا کد 16220 سایز 16 دوچرخه شهری المپیا کد 16220 سایز 16 دوچرخه المپیا کد 16220 سایز 16 - OLYMPIA - لامپ اس ام دی خودرو مدل 8B بسته 2 عددی - قیمت کمپرس سنج موتور - source_sentence: دچرخه ی sentences: - هیدروفیشیال ۷ کاره نیوفیس پلاس متور سنگین ۲۰۲۲ - جامدادی کیوت - جعبه ی کادو ی رنگی - source_sentence: هایومکس sentences: - انگشتر حدید صینی کد2439 - ژل هایومکس ولومایزر 2 سی سی - دزدگیر پاناتک مدل P-CA501 دزدگیر پاناتک P-CA501-2 دزدگیر پاناتک مدل P-CA501-2 model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.8531738206358597 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.763870358467102 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9032999224561303 name: Cosine F1 - type: cosine_f1_threshold value: 0.7447167634963989 name: Cosine F1 Threshold - type: cosine_precision value: 0.8649689236015621 name: Cosine Precision - type: cosine_recall value: 0.9451857194374323 name: Cosine Recall - type: cosine_ap value: 0.9354580013152192 name: Cosine Ap - type: dot_accuracy value: 0.8179627073336401 name: Dot Accuracy - type: dot_accuracy_threshold value: 17.24372100830078 name: Dot Accuracy Threshold - type: dot_f1 value: 0.8831898479427548 name: Dot F1 - type: dot_f1_threshold value: 16.905807495117188 name: Dot F1 Threshold - type: dot_precision value: 0.8255042324171805 name: Dot Precision - type: dot_recall value: 0.9495432143286453 name: Dot Recall - type: dot_ap value: 0.9192801272426158 name: Dot Ap - type: manhattan_accuracy value: 0.8484629374000306 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 56.168235778808594 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.9006901291486498 name: Manhattan F1 - type: manhattan_f1_threshold value: 57.448089599609375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8601706503309084 name: Manhattan Precision - type: manhattan_recall value: 0.9452157711263373 name: Manhattan Recall - type: manhattan_ap value: 0.9331690796886208 name: Manhattan Ap - type: euclidean_accuracy value: 0.8485944039089375 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 3.5569825172424316 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.9009756516265629 name: Euclidean F1 - type: euclidean_f1_threshold value: 3.694398880004883 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.8597717468465025 name: Euclidean Precision - type: euclidean_recall value: 0.9463276836158192 name: Euclidean Recall - type: euclidean_ap value: 0.9332275611001725 name: Euclidean Ap - type: max_accuracy value: 0.8531738206358597 name: Max Accuracy - type: max_accuracy_threshold value: 56.168235778808594 name: Max Accuracy Threshold - type: max_f1 value: 0.9032999224561303 name: Max F1 - type: max_f1_threshold value: 57.448089599609375 name: Max F1 Threshold - type: max_precision value: 0.8649689236015621 name: Max Precision - type: max_recall value: 0.9495432143286453 name: Max Recall - type: max_ap value: 0.9354580013152192 name: Max Ap --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 tokens - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("DashReza7/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2_FINETUNED_on_torob_data_v5") # Run inference sentences = [ 'هایومکس', 'ژل هایومکس ولومایزر 2 سی سی', 'دزدگیر پاناتک مدل P-CA501 دزدگیر پاناتک P-CA501-2 دزدگیر پاناتک مدل P-CA501-2', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.8532 | | cosine_accuracy_threshold | 0.7639 | | cosine_f1 | 0.9033 | | cosine_f1_threshold | 0.7447 | | cosine_precision | 0.865 | | cosine_recall | 0.9452 | | cosine_ap | 0.9355 | | dot_accuracy | 0.818 | | dot_accuracy_threshold | 17.2437 | | dot_f1 | 0.8832 | | dot_f1_threshold | 16.9058 | | dot_precision | 0.8255 | | dot_recall | 0.9495 | | dot_ap | 0.9193 | | manhattan_accuracy | 0.8485 | | manhattan_accuracy_threshold | 56.1682 | | manhattan_f1 | 0.9007 | | manhattan_f1_threshold | 57.4481 | | manhattan_precision | 0.8602 | | manhattan_recall | 0.9452 | | manhattan_ap | 0.9332 | | euclidean_accuracy | 0.8486 | | euclidean_accuracy_threshold | 3.557 | | euclidean_f1 | 0.901 | | euclidean_f1_threshold | 3.6944 | | euclidean_precision | 0.8598 | | euclidean_recall | 0.9463 | | euclidean_ap | 0.9332 | | max_accuracy | 0.8532 | | max_accuracy_threshold | 56.1682 | | max_f1 | 0.9033 | | max_f1_threshold | 57.4481 | | max_precision | 0.865 | | max_recall | 0.9495 | | **max_ap** | **0.9355** | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 2 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `dispatch_batches`: None - `split_batches`: 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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | max_ap | |:------:|:----:|:-------------:|:------:| | None | 0 | - | 0.8131 | | 0.3115 | 500 | 0.0256 | - | | 0.6231 | 1000 | 0.0179 | - | | 0.9346 | 1500 | 0.0165 | - | | 1.2461 | 2000 | 0.0152 | - | | 1.5576 | 2500 | 0.0148 | - | | 1.8692 | 3000 | 0.0144 | - | | 2.0 | 3210 | - | 0.9355 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.21.0 - Tokenizers: 0.19.1 ## 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```