--- base_model: BAAI/bge-m3 datasets: [] language: [] library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4532 - loss:CoSENTLoss widget: - source_sentence: портативный проектор umiio a 008 sentences: - портативный проектор philips a 008 - logitech c270i iptv - детский электромобиль sundays land rover jj012 - source_sentence: запчасти для швейных машин bernette sentences: - мфу samsung m428fdw - запасные части для швейной машины bernette - steelseries apex pro mini wireless - source_sentence: сушильная машина maunfeld mfdm1410wh06 sentences: - кухонные уголки - сушильная машина simens mfdm1410wh06 - сетевой удлинитель евро eu-4 multi-protection 4usb qy-923 2500w - source_sentence: монитор acer k242hql sentences: - multiflashlight armytek zippy green - роутер mi router 4c r4cm dvb4231gl - монитор acer k224hql - source_sentence: набор моя первая кухня sentences: - кухонные наборы - ea sports fc 23 ps4 - da vinci белая model-index: - name: SentenceTransformer based on BAAI/bge-m3 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.9701810342203735 name: Pearson Cosine - type: spearman_cosine value: 0.9168792089469636 name: Spearman Cosine - type: pearson_manhattan value: 0.9695654298959763 name: Pearson Manhattan - type: spearman_manhattan value: 0.9165761310923896 name: Spearman Manhattan - type: pearson_euclidean value: 0.9696385323216731 name: Pearson Euclidean - type: spearman_euclidean value: 0.9166348972420479 name: Spearman Euclidean - type: pearson_dot value: 0.9631206697635591 name: Pearson Dot - type: spearman_dot value: 0.9173046326579305 name: Spearman Dot - type: pearson_max value: 0.9701810342203735 name: Pearson Max - type: spearman_max value: 0.9173046326579305 name: Spearman Max --- # SentenceTransformer based on BAAI/bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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("seregadgl101/test_bge_2_10ep") # Run inference sentences = [ 'набор моя первая кухня', 'кухонные наборы', 'ea sports fc 23 ps4', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.9702 | | **spearman_cosine** | **0.9169** | | pearson_manhattan | 0.9696 | | spearman_manhattan | 0.9166 | | pearson_euclidean | 0.9696 | | spearman_euclidean | 0.9166 | | pearson_dot | 0.9631 | | spearman_dot | 0.9173 | | pearson_max | 0.9702 | | spearman_max | 0.9173 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,532 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------------------------------------------|:-------------------------------------------------------------|:-----------------| | батут evo jump internal 12ft | батут evo jump internal 12ft | 1.0 | | наручные часы orient casual | наручные часы orient | 1.0 | | электрический духовой шкаф weissgauff eov 19 mw | электрический духовой шкаф weissgauff eov 19 mx | 0.4 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 504 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:------------------------------------------------------------------------------|:--------------------------------------------------------|:-----------------| | потолочный светильник yeelight smart led ceiling light c2001s500 | yeelight smart led ceiling light c2001s500 | 1.0 | | канцелярские принадлежности | канцелярские принадлежности разные | 0.4 | | usb-магнитола acv avs-1718g | автомагнитола acv avs-1718g | 1.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `save_only_model`: True - `seed`: 33 - `fp16`: True - `load_best_model_at_end`: 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`: 8 - `per_device_eval_batch_size`: 8 - `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`: 10 - `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`: True - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 33 - `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`: True - `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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |:----------:|:--------:|:-------------:|:----------:|:-----------------------:| | 0.0882 | 50 | - | 2.7444 | 0.4991 | | 0.1764 | 100 | - | 2.5535 | 0.6093 | | 0.2646 | 150 | - | 2.3365 | 0.6761 | | 0.3527 | 200 | - | 2.1920 | 0.7247 | | 0.4409 | 250 | - | 2.2210 | 0.7446 | | 0.5291 | 300 | - | 2.1432 | 0.7610 | | 0.6173 | 350 | - | 2.2488 | 0.7769 | | 0.7055 | 400 | - | 2.3736 | 0.7749 | | 0.7937 | 450 | - | 2.0688 | 0.7946 | | 0.8818 | 500 | 2.3647 | 2.5331 | 0.7879 | | 0.9700 | 550 | - | 2.1087 | 0.7742 | | 1.0582 | 600 | - | 2.1302 | 0.8068 | | 1.1464 | 650 | - | 2.2669 | 0.8114 | | 1.2346 | 700 | - | 2.0269 | 0.8039 | | 1.3228 | 750 | - | 2.2095 | 0.8138 | | 1.4109 | 800 | - | 2.5288 | 0.8190 | | 1.4991 | 850 | - | 2.3442 | 0.8222 | | 1.5873 | 900 | - | 2.3759 | 0.8289 | | 1.6755 | 950 | - | 2.1893 | 0.8280 | | 1.7637 | 1000 | 2.0682 | 2.0056 | 0.8426 | | 1.8519 | 1050 | - | 2.0832 | 0.8527 | | 1.9400 | 1100 | - | 2.0336 | 0.8515 | | 2.0282 | 1150 | - | 2.0571 | 0.8591 | | 2.1164 | 1200 | - | 2.1516 | 0.8565 | | 2.2046 | 1250 | - | 2.2035 | 0.8602 | | 2.2928 | 1300 | - | 2.5294 | 0.8513 | | 2.3810 | 1350 | - | 2.4177 | 0.8647 | | 2.4691 | 1400 | - | 2.1630 | 0.8709 | | 2.5573 | 1450 | - | 2.1279 | 0.8661 | | 2.6455 | 1500 | 1.678 | 2.1639 | 0.8744 | | 2.7337 | 1550 | - | 2.2592 | 0.8799 | | 2.8219 | 1600 | - | 2.2288 | 0.8822 | | 2.9101 | 1650 | - | 2.2427 | 0.8831 | | 2.9982 | 1700 | - | 2.4380 | 0.8776 | | 3.0864 | 1750 | - | 2.1689 | 0.8826 | | 3.1746 | 1800 | - | 1.8099 | 0.8868 | | 3.2628 | 1850 | - | 2.0881 | 0.8832 | | 3.3510 | 1900 | - | 2.0785 | 0.8892 | | 3.4392 | 1950 | - | 2.2512 | 0.8865 | | 3.5273 | 2000 | 1.2168 | 2.1249 | 0.8927 | | 3.6155 | 2050 | - | 2.1179 | 0.8950 | | 3.7037 | 2100 | - | 2.1932 | 0.8973 | | 3.7919 | 2150 | - | 2.2628 | 0.8967 | | 3.8801 | 2200 | - | 2.0764 | 0.8972 | | 3.9683 | 2250 | - | 1.9575 | 0.9012 | | 4.0564 | 2300 | - | 2.3302 | 0.8985 | | 4.1446 | 2350 | - | 2.3008 | 0.8980 | | 4.2328 | 2400 | - | 2.2886 | 0.8968 | | 4.3210 | 2450 | - | 2.1694 | 0.8973 | | 4.4092 | 2500 | 1.0851 | 2.1102 | 0.9010 | | 4.4974 | 2550 | - | 2.2596 | 0.9021 | | 4.5855 | 2600 | - | 2.1944 | 0.9019 | | 4.6737 | 2650 | - | 2.0728 | 0.9029 | | 4.7619 | 2700 | - | 2.4573 | 0.9031 | | 4.8501 | 2750 | - | 2.2306 | 0.9057 | | 4.9383 | 2800 | - | 2.2637 | 0.9068 | | 5.0265 | 2850 | - | 2.5110 | 0.9068 | | 5.1146 | 2900 | - | 2.6613 | 0.9042 | | 5.2028 | 2950 | - | 2.4713 | 0.9070 | | 5.2910 | 3000 | 0.8143 | 2.3709 | 0.9082 | | 5.3792 | 3050 | - | 2.6083 | 0.9058 | | 5.4674 | 3100 | - | 2.5377 | 0.9044 | | 5.5556 | 3150 | - | 2.3146 | 0.9071 | | 5.6437 | 3200 | - | 2.2603 | 0.9085 | | 5.7319 | 3250 | - | 2.5842 | 0.9068 | | 5.8201 | 3300 | - | 2.6045 | 0.9093 | | 5.9083 | 3350 | - | 2.6207 | 0.9103 | | 5.9965 | 3400 | - | 2.5992 | 0.9098 | | 6.0847 | 3450 | - | 2.7799 | 0.9090 | | 6.1728 | 3500 | 0.5704 | 2.7198 | 0.9098 | | 6.2610 | 3550 | - | 2.9783 | 0.9089 | | 6.3492 | 3600 | - | 2.4165 | 0.9120 | | 6.4374 | 3650 | - | 2.4488 | 0.9122 | | 6.5256 | 3700 | - | 2.6764 | 0.9113 | | 6.6138 | 3750 | - | 2.5327 | 0.9130 | | 6.7019 | 3800 | - | 2.5875 | 0.9129 | | 6.7901 | 3850 | - | 2.7036 | 0.9130 | | 6.8783 | 3900 | - | 2.7566 | 0.9120 | | 6.9665 | 3950 | - | 2.5488 | 0.9127 | | 7.0547 | 4000 | 0.4287 | 2.8512 | 0.9127 | | 7.1429 | 4050 | - | 2.7361 | 0.9128 | | 7.2310 | 4100 | - | 2.7434 | 0.9135 | | 7.3192 | 4150 | - | 2.9410 | 0.9129 | | 7.4074 | 4200 | - | 2.9452 | 0.9126 | | 7.4956 | 4250 | - | 2.8665 | 0.9140 | | 7.5838 | 4300 | - | 2.8215 | 0.9145 | | 7.6720 | 4350 | - | 2.6978 | 0.9147 | | 7.7601 | 4400 | - | 2.8445 | 0.9143 | | 7.8483 | 4450 | - | 2.6041 | 0.9155 | | 7.9365 | 4500 | 0.3099 | 2.7219 | 0.9155 | | 8.0247 | 4550 | - | 2.7180 | 0.9160 | | 8.1129 | 4600 | - | 2.6906 | 0.9160 | | 8.2011 | 4650 | - | 2.8628 | 0.9156 | | 8.2892 | 4700 | - | 2.7820 | 0.9158 | | 8.3774 | 4750 | - | 2.8457 | 0.9157 | | 8.4656 | 4800 | - | 2.7286 | 0.9160 | | 8.5538 | 4850 | - | 2.7131 | 0.9164 | | 8.6420 | 4900 | - | 2.8368 | 0.9165 | | 8.7302 | 4950 | - | 2.8033 | 0.9167 | | 8.8183 | 5000 | 0.2342 | 2.7307 | 0.9169 | | 8.9065 | 5050 | - | 2.8483 | 0.9167 | | 8.9947 | 5100 | - | 2.9736 | 0.9167 | | 9.0829 | 5150 | - | 2.9151 | 0.9168 | | 9.1711 | 5200 | - | 2.9375 | 0.9167 | | 9.2593 | 5250 | - | 2.9968 | 0.9168 | | 9.3474 | 5300 | - | 3.0024 | 0.9167 | | 9.4356 | 5350 | - | 2.9444 | 0.9167 | | 9.5238 | 5400 | - | 2.9477 | 0.9167 | | 9.6120 | 5450 | - | 2.9205 | 0.9168 | | **9.7002** | **5500** | **0.1639** | **2.9286** | **0.9167** | | 9.7884 | 5550 | - | 2.9421 | 0.9168 | | 9.8765 | 5600 | - | 2.9733 | 0.9168 | | 9.9647 | 5650 | - | 2.9777 | 0.9169 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```