--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:111476 - loss:CosineSimilarityLoss base_model: sergeyzh/LaBSE-ru-sts widget: - source_sentence: 'трюковый самокат plank 180 белый ' sentences: - смарт-телевизор 75 sony kd-75x950h - самокат для трюков плэнк 1.80 м белый - xiaomi mi 11 8gb 128gb - source_sentence: 'вейп vaporesso xros ' sentences: - садовая ограда классика 4 2 м белый - кухонные весы - электронная сигарета voopoo drag - source_sentence: серьги l atelier precieux 1628710 sentences: - фильтр hepa для пылесоса варис st400 - потолочная люстра майтон nostalgia ceiling chandelier mod048pl-06g - серьги atelier de bijoux 1628712 - source_sentence: 'мобильный геймпад триггерами x2 ' sentences: - электроскутер nitro pro milano 750w led - наушники без проводов мейзу ep52 lite - геймпад с функцией триггеров x2 - source_sentence: комод 7 рисунком машинки 4 ящика sentences: - удлинитель far f 505 d lara выключателем 2 0м - беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный - комод 8 с изображением супергероев 6 ящиков datasets: - seregadgl/data_cross_gpt_139k pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on sergeyzh/LaBSE-ru-sts results: - task: type: binary-classification name: Binary Classification dataset: name: eval type: eval metrics: - type: cosine_accuracy value: 0.9722640832436311 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.630459189414978 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9724366041896361 name: Cosine F1 - type: cosine_f1_threshold value: 0.5821653008460999 name: Cosine F1 Threshold - type: cosine_precision value: 0.9647847565278758 name: Cosine Precision - type: cosine_recall value: 0.9802107980210798 name: Cosine Recall - type: cosine_ap value: 0.9945729266353226 name: Cosine Ap - type: cosine_mcc value: 0.9445047865635516 name: Cosine Mcc --- # SentenceTransformer based on sergeyzh/LaBSE-ru-sts This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) on the [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) dataset. It maps sentences & paragraphs to a 768-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:** [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) ### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, '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': False, '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("seregadgl/sts_v11") # Run inference sentences = [ 'комод 7 рисунком машинки 4 ящика', 'комод 8 с изображением супергероев 6 ящиков', 'беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `eval` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9723 | | cosine_accuracy_threshold | 0.6305 | | cosine_f1 | 0.9724 | | cosine_f1_threshold | 0.5822 | | cosine_precision | 0.9648 | | cosine_recall | 0.9802 | | **cosine_ap** | **0.9946** | | cosine_mcc | 0.9445 | ## Training Details ### Training Dataset #### data_cross_gpt_139k * Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e) * Size: 111,476 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-----------------| | нож кухонный 21см синий | кухонный нож 22см зелёный | 0.0 | | блок питания универсальный для мерцающих флэш гирлянд rich led бахрома занавес нить белый | адаптер питания для мигающих led гирлянд "luminous decor" бахрома занавес нить зелёный | 0.0 | | защитная пленка для apple iphone 6 прозрачная | protective film for apple iphone 6 transparent | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### data_cross_gpt_139k * Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e) * Size: 27,870 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------| | сумка дорожная складная полет оранжевая bradex td 0599 | сумка для путешествий складная брадекс orange | 1.0 | | наушники sennheiser hd 450bt белый | наушники сенхайзер hd 450bt white | 1.0 | | перчатки stg al-05-1871 синие серые черные зеленыеполноразмерные xl | перчатки stg al-05-1871 blue gray black green full size xl | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 4.7459131195420915e-05 - `weight_decay`: 0.03196240090522689 - `num_train_epochs`: 2 - `warmup_ratio`: 0.014344463935915175 - `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`: 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`: 4.7459131195420915e-05 - `weight_decay`: 0.03196240090522689 - `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`: 0.014344463935915175 - `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} - `tp_size`: 0 - `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 - `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 - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | eval_cosine_ap | |:------:|:----:|:-------------:|:---------------:|:--------------:| | 0.0287 | 100 | 0.189 | - | - | | 0.0574 | 200 | 0.0695 | - | - | | 0.0861 | 300 | 0.067 | - | - | | 0.1148 | 400 | 0.0643 | - | - | | 0.1435 | 500 | 0.0594 | 0.0549 | 0.9862 | | 0.1722 | 600 | 0.0565 | - | - | | 0.2009 | 700 | 0.0535 | - | - | | 0.2296 | 800 | 0.0506 | - | - | | 0.2583 | 900 | 0.0549 | - | - | | 0.2870 | 1000 | 0.0535 | 0.0451 | 0.9888 | | 0.3157 | 1100 | 0.0492 | - | - | | 0.3444 | 1200 | 0.0499 | - | - | | 0.3731 | 1300 | 0.0486 | - | - | | 0.4018 | 1400 | 0.0458 | - | - | | 0.4305 | 1500 | 0.0458 | 0.0419 | 0.9877 | | 0.4592 | 1600 | 0.0502 | - | - | | 0.4879 | 1700 | 0.045 | - | - | | 0.5166 | 1800 | 0.0435 | - | - | | 0.5454 | 1900 | 0.0426 | - | - | | 0.5741 | 2000 | 0.0422 | 0.0386 | 0.9906 | | 0.6028 | 2100 | 0.0436 | - | - | | 0.6315 | 2200 | 0.043 | - | - | | 0.6602 | 2300 | 0.0432 | - | - | | 0.6889 | 2400 | 0.0397 | - | - | | 0.7176 | 2500 | 0.0394 | 0.0357 | 0.9903 | | 0.7463 | 2600 | 0.039 | - | - | | 0.7750 | 2700 | 0.0398 | - | - | | 0.8037 | 2800 | 0.0394 | - | - | | 0.8324 | 2900 | 0.0426 | - | - | | 0.8611 | 3000 | 0.0345 | 0.0341 | 0.9921 | | 0.8898 | 3100 | 0.0361 | - | - | | 0.9185 | 3200 | 0.0365 | - | - | | 0.9472 | 3300 | 0.0401 | - | - | | 0.9759 | 3400 | 0.0391 | - | - | | 1.0046 | 3500 | 0.0342 | 0.0310 | 0.9928 | | 1.0333 | 3600 | 0.0267 | - | - | | 1.0620 | 3700 | 0.0264 | - | - | | 1.0907 | 3800 | 0.0263 | - | - | | 1.1194 | 3900 | 0.0248 | - | - | | 1.1481 | 4000 | 0.0282 | 0.0301 | 0.9928 | | 1.1768 | 4100 | 0.0279 | - | - | | 1.2055 | 4200 | 0.0258 | - | - | | 1.2342 | 4300 | 0.0248 | - | - | | 1.2629 | 4400 | 0.0289 | - | - | | 1.2916 | 4500 | 0.0261 | 0.0291 | 0.9935 | | 1.3203 | 4600 | 0.0262 | - | - | | 1.3490 | 4700 | 0.0276 | - | - | | 1.3777 | 4800 | 0.0256 | - | - | | 1.4064 | 4900 | 0.0272 | - | - | | 1.4351 | 5000 | 0.0283 | 0.0284 | 0.9939 | | 1.4638 | 5100 | 0.0254 | - | - | | 1.4925 | 5200 | 0.0252 | - | - | | 1.5212 | 5300 | 0.0234 | - | - | | 1.5499 | 5400 | 0.0228 | - | - | | 1.5786 | 5500 | 0.0248 | 0.0277 | 0.9941 | | 1.6073 | 5600 | 0.024 | - | - | | 1.6361 | 5700 | 0.0225 | - | - | | 1.6648 | 5800 | 0.0234 | - | - | | 1.6935 | 5900 | 0.0226 | - | - | | 1.7222 | 6000 | 0.0248 | 0.0265 | 0.9942 | | 1.7509 | 6100 | 0.0247 | - | - | | 1.7796 | 6200 | 0.0219 | - | - | | 1.8083 | 6300 | 0.026 | - | - | | 1.8370 | 6400 | 0.0209 | - | - | | 1.8657 | 6500 | 0.0252 | 0.0262 | 0.9945 | | 1.8944 | 6600 | 0.0218 | - | - | | 1.9231 | 6700 | 0.0223 | - | - | | 1.9518 | 6800 | 0.0228 | - | - | | 1.9805 | 6900 | 0.0242 | - | - | | 2.0 | 6968 | - | 0.0257 | 0.9946 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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", } ```