--- library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10501 - loss:CosineSimilarityLoss base_model: BAAI/bge-m3 metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: 숙소가 기대했던 것 이상으로 좋았습니다. sentences: - 숙소가 생각보다 좋았어요. - 어떻게 해야 환풍기를 작동시킬 수 있어? - 우리 집 바로 옆에 슈퍼마켓이 있는데, 무엇보다도 조용해요. - source_sentence: 위치, 청결 상태, 주변 편의시설 모든게 좋았어요. sentences: - 집주인이 있기에 이 나라에서 잊을 수 없는 추억을 남겼습니다. - 두 개 방 모두에서 누우면 에펠탑이 보입니다! - 위치와 청결도 편의시설 크기 등등 모든게 좋습니다. - source_sentence: 인내심을 가지고 결실을 맺는다는 자세가 필요합니다. sentences: - 같은 날, 바이오 산업은 정부에게 바이오 전문가 공급 시설, 새로운 시장 창출을 위한 규제 완화, 세금과 같은 인센티브 확대 등을 제안했습니다. - 그런 점에서 매우 힘든 기간을 보내고 계십니다. - 접속 가능한 계정 네이트나 네이버 메일 중 하나만 알려줘 - source_sentence: 비가 올지 맑을지 오늘 날씨를 찾아봐줄래? sentences: - 이번 태풍 진행 방향은? - 제가 지메일을 몇 번 가입했는지 알려주실 수 있나요? - 할부지 덕분에 산타모니카에 있는 내내 행복했어요. - source_sentence: 티비 켜고 싶은데 말로 어떻게 명령해야하는 지 알려줘 sentences: - 가습기 어떻게 써? - 친절한 설명으로 많은 도움이 되었습니다. - 에어컨과 뜨거운 물 모두 좋았습니다. pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on BAAI/bge-m3 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.9599773741282561 name: Pearson Cosine - type: spearman_cosine value: 0.9215829115320294 name: Spearman Cosine - type: pearson_manhattan value: 0.9448530221078223 name: Pearson Manhattan - type: spearman_manhattan value: 0.9182945172058137 name: Spearman Manhattan - type: pearson_euclidean value: 0.9451692315193281 name: Pearson Euclidean - type: spearman_euclidean value: 0.9184981231098932 name: Spearman Euclidean - type: pearson_dot value: 0.9576506770371606 name: Pearson Dot - type: spearman_dot value: 0.9159848293826075 name: Spearman Dot - type: pearson_max value: 0.9599773741282561 name: Pearson Max - type: spearman_max value: 0.9215829115320294 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("jeonseonjin/embedding_BAAI-bge-m3") # Run inference sentences = [ '티비 켜고 싶은데 말로 어떻게 명령해야하는 지 알려줘', '가습기 어떻게 써?', '친절한 설명으로 많은 도움이 되었습니다.', ] 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 * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.96 | | spearman_cosine | 0.9216 | | pearson_manhattan | 0.9449 | | spearman_manhattan | 0.9183 | | pearson_euclidean | 0.9452 | | spearman_euclidean | 0.9185 | | pearson_dot | 0.9577 | | spearman_dot | 0.916 | | pearson_max | 0.96 | | **spearman_max** | **0.9216** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,501 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------------|:----------------------------------------------------|:------------------| | 공원에서 열리는 시장도 구경할 수 있었어요. | 공원에서 시장을 볼 수 있었어요. | 0.74 | | 베네치아에서 2박 3일 일정으로 머물렀습니다. | 저는 2박 3일 동안 베니스에 머물렀습니다. | 0.74 | | 메일로 홍보하는 학회 리스트 불러줘 | 보낸메일함의 메일은 주기적으로 백업하세요. 간헐적으로 하면 안됩니다. | 0.12 | * 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 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `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 - `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`: 1 - `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 - `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, '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_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | spearman_max | |:------:|:----:|:-------------:|:------------:| | 0 | 0 | - | 0.9196 | | 0.7610 | 500 | 0.024 | - | | 1.0 | 657 | - | 0.9216 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.40.1 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 2.19.0 - 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", } ```