--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10501 - loss:CosineSimilarityLoss base_model: klue/roberta-base widget: - source_sentence: 이어 내년 4월부터 전자증명서는 건강보험자격확인서와 건강보험료 납부확인서 등 13종으로 늘어나고 사용처도 중앙부처는 물론 은행과 보험사 등으로도 확대된다. sentences: - 4대 보험료 납부유예 및 감면조치는 4월에 납부해야 하는 3월 보험료부터 적용된다. - 그 외에는 모든 것에 만족했습니다. - 영하의 추운 날씨에는 장갑 잊지 말고 꼭 끼렴. - source_sentence: 야생동물 질병관리를 전담할 국가기관인 국립야생동물질병관리원이 올해 광주광역시 광산구 삼거동 일원에 개원 예정이다. sentences: - 위치는 좋으나 생활하기 좀 불편합니다. - 역에서 매우 가깝고, 쇼핑몰과 쇼핑몰 사이에는 숙소가 있습니다. - 추후 인도네시아와도 화상회의 및 온라인 세미나를 개최할 예정이다. - source_sentence: 작은 먹거리는 숙소 들어오게 전에 사는걸 추천해요. sentences: - 제일 최근에 스팸이 도착한 시간을 알려줘 - 저는 당신이 숙소에 들어오기 전에 작은 음식을 사는 것을 추천합니다. - 올해는 황사 며칠동안 왔어? - source_sentence: 언제 만나는 것이 더 좋으실까요, 저녁 일곱시? 여덟시? sentences: - 이번주 일요일 약속 언제인지 궁금해. - 전자레인지와 가스레인지 중에 요리하고 싶은 걸로 알려줘 - 뜨거운물말고 찬물로 세탁하고 더운물로 헹궈야될 것 같지 않아? - source_sentence: 지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다 sentences: - 지금까지 가본 호텔보다 더 좋은 숙소였습니다. - ‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다. - 하루에 삼십분보단 한 시간 이상은 라디오 들어 pipeline_tag: sentence-similarity 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 co2_eq_emissions: emissions: 13.607209111220918 energy_consumed: 0.0310949426904377 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 12th Gen Intel(R) Core(TM) i5-12400 ram_total_size: 31.784194946289062 hours_used: 0.154 hardware_used: 1 x NVIDIA GeForce RTX 3060 model-index: - name: SentenceTransformer based on klue/roberta-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.34770715374416716 name: Pearson Cosine - type: spearman_cosine value: 0.35560473197486514 name: Spearman Cosine - type: pearson_manhattan value: 0.3673847148331908 name: Pearson Manhattan - type: spearman_manhattan value: 0.36460670798564826 name: Spearman Manhattan - type: pearson_euclidean value: 0.36074518113660536 name: Pearson Euclidean - type: spearman_euclidean value: 0.35482778401649034 name: Spearman Euclidean - type: pearson_dot value: 0.21251176317804726 name: Pearson Dot - type: spearman_dot value: 0.20063256899469895 name: Spearman Dot - type: pearson_max value: 0.3673847148331908 name: Pearson Max - type: spearman_max value: 0.36460670798564826 name: Spearman Max - type: pearson_cosine value: 0.9591996448990093 name: Pearson Cosine - type: spearman_cosine value: 0.9206205258325634 name: Spearman Cosine - type: pearson_manhattan value: 0.9531423622288514 name: Pearson Manhattan - type: spearman_manhattan value: 0.920406431818358 name: Spearman Manhattan - type: pearson_euclidean value: 0.9532828644532834 name: Pearson Euclidean - type: spearman_euclidean value: 0.9201721809761834 name: Spearman Euclidean - type: pearson_dot value: 0.9482313505749467 name: Pearson Dot - type: spearman_dot value: 0.9016036223997308 name: Spearman Dot - type: pearson_max value: 0.9591996448990093 name: Pearson Max - type: spearman_max value: 0.9206205258325634 name: Spearman Max --- # SentenceTransformer based on klue/roberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id") # Run inference sentences = [ '지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다', '지금까지 가본 호텔보다 더 좋은 숙소였습니다.', '‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다.', ] 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 #### Semantic Similarity * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.3477 | | spearman_cosine | 0.3556 | | pearson_manhattan | 0.3674 | | spearman_manhattan | 0.3646 | | pearson_euclidean | 0.3607 | | spearman_euclidean | 0.3548 | | pearson_dot | 0.2125 | | spearman_dot | 0.2006 | | pearson_max | 0.3674 | | **spearman_max** | **0.3646** | #### Semantic Similarity * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.9592 | | spearman_cosine | 0.9206 | | pearson_manhattan | 0.9531 | | spearman_manhattan | 0.9204 | | pearson_euclidean | 0.9533 | | spearman_euclidean | 0.9202 | | pearson_dot | 0.9482 | | spearman_dot | 0.9016 | | pearson_max | 0.9592 | | **spearman_max** | **0.9206** | ## 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 | |:----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:------------------| | 가스레인지 사용하지 않도록 유의해주세요 | 가스레인지 사용은 삼가주세요 | 0.74 | | 이번주하고 다음주 중에 언제 동기 모임이 있어? | 언제 자연어처리 학회 논문 접수가 마감되나요? | 0.02 | | 또한 각 부처는 생활방역 관련 업무를 종합·체계적으로 수행하기 위해 기관별로 생활방역 전담팀(TF)을 구성한다. | 또한 생활방지와 관련된 업무를 종합적이고 체계적으로 수행하기 위하여 각 부서별로 생활방역 전담 태스크포스(TF)를 구성하여야 합니다. | 0.72 | * 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`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `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`: 4 - `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} - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | spearman_max | |:------:|:----:|:-------------:|:------------:| | 0 | 0 | - | 0.3646 | | 0.7610 | 500 | 0.0278 | - | | 1.0 | 657 | - | 0.9187 | | 1.5221 | 1000 | 0.0085 | 0.9117 | | 2.0 | 1314 | - | 0.9201 | | 2.2831 | 1500 | 0.0044 | - | | 3.0 | 1971 | - | 0.9186 | | 3.0441 | 2000 | 0.0034 | 0.9199 | | 3.8052 | 2500 | 0.0027 | - | | 4.0 | 2628 | - | 0.9206 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.031 kWh - **Carbon Emitted**: 0.014 kg of CO2 - **Hours Used**: 0.154 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3060 - **CPU Model**: 12th Gen Intel(R) Core(TM) i5-12400 - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.12.4 - Sentence Transformers: 3.2.1 - Transformers: 4.45.2 - PyTorch: 2.4.0+cu121 - Accelerate: 0.29.3 - Datasets: 2.19.0 - Tokenizers: 0.20.1 ## 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", } ```