--- base_model: klue/roberta-base 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:10501 - loss:CosineSimilarityLoss widget: - source_sentence: 기업은 생존 문제에 직면하고, 자영업자와 소상공인의 고통은 이루 말할 수 없을 정도입니다. sentences: - 자유무역은 기업이 서로를 신뢰하고, 미래의 불확실성을 낮추는 안전장치입니다. - 국가 임상연구 승인, 시행기관 지정, 장기 추적조사 등 안전관리체계를 구축하고 치료 개발 및 임상연구 수행을 위한 RD 투자를 확대합니다. - 중심가와 거리가 조금 먼 점 빼고는 정말 모든게 너무 좋았던 숙소입니다! - source_sentence: 타이페이를 다시 간다면 여기 또 올거예요. sentences: - 사진으로 봤던것보다 훨씬 더 좋았습니다 - 겨울에 난방 온도 이십오도 이상으로 올리지마라고 경고했어 - 만약 내가 다시 타이페이에 간다면, 나는 여기에 다시 올 것입니다. - source_sentence: 호주의 좋은 가정집에서 묵는 느낌이었어요. sentences: - 어린이 교통사고 위험지역에 CCTV 2087대, 신호등 2146개를 올해 상반기 중으로 설치하고 옐로카펫과 노란발자국 등을 올해 하반기에 초등학교 100곳에 시범 설치한다. - 호주에 있는 좋은 집에서 지내는 것 같았어요. - 그러나 호텔업계 노사가 가장 어려운 시기에, 가장 모범적으로 함께 마음을 모았습니다. - source_sentence: 그들덕분에 우리는 4일간 편안히 쉴 수 있었습니다. sentences: - 그들 덕분에, 우리는 4일 동안 쉴 수 있었어요. - 주변에 두 개의 지하철역이 있습니다. 큰 공원, 큰 슈퍼마켓, 그리고 편의점이 있습니다. - 방은 쾌적하고 에어컨도 아주 잘 나와요. - source_sentence: 테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요. sentences: - 테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요. - 이쪽 주변에서 여행할 계획이라면 추천합니다! - 저희 할아버지는 매우 친절하고 친절하십니다. co2_eq_emissions: emissions: 7.379414346751554 energy_consumed: 0.016863301234344347 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700 ram_total_size: 62.56697463989258 hours_used: 0.057 hardware_used: 1 x NVIDIA GeForce RTX 4090 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: 0.34770704341988723 name: Pearson Cosine - type: spearman_cosine value: 0.35560473197486514 name: Spearman Cosine - type: pearson_manhattan value: 0.3673846313946801 name: Pearson Manhattan - type: spearman_manhattan value: 0.36460670798564826 name: Spearman Manhattan - type: pearson_euclidean value: 0.3607451203867209 name: Pearson Euclidean - type: spearman_euclidean value: 0.35482778401649034 name: Spearman Euclidean - type: pearson_dot value: 0.21251167982120983 name: Pearson Dot - type: spearman_dot value: 0.20063256899469895 name: Spearman Dot - type: pearson_max value: 0.3673846313946801 name: Pearson Max - type: spearman_max value: 0.36460670798564826 name: Spearman Max - type: pearson_cosine value: 0.961968864970919 name: Pearson Cosine - type: spearman_cosine value: 0.9196100863981246 name: Spearman Cosine - type: pearson_manhattan value: 0.9530332430579778 name: Pearson Manhattan - type: spearman_manhattan value: 0.9186168431687389 name: Spearman Manhattan - type: pearson_euclidean value: 0.9532923011007042 name: Pearson Euclidean - type: spearman_euclidean value: 0.9190754386835427 name: Spearman Euclidean - type: pearson_dot value: 0.9493179101338206 name: Pearson Dot - type: spearman_dot value: 0.8999468521869318 name: Spearman Dot - type: pearson_max value: 0.961968864970919 name: Pearson Max - type: spearman_max value: 0.9196100863981246 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 = [ '테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.', '테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.', '이쪽 주변에서 여행할 계획이라면 추천합니다!', ] 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 | 0.962 | | spearman_cosine | 0.9196 | | pearson_manhattan | 0.953 | | spearman_manhattan | 0.9186 | | pearson_euclidean | 0.9533 | | spearman_euclidean | 0.9191 | | pearson_dot | 0.9493 | | spearman_dot | 0.8999 | | pearson_max | 0.962 | | **spearman_max** | **0.9196** | ## 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 | |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------------------------| | 지하철 역 내려서 1분정도의 아주 가까운 거리입니다. | 지하철역에서 1분 정도 아주 가까운 거리입니다. | 0.86 | | 그것빼곤 2인여행자들에게는 좋은숙소에요! | 계단이 많다는거 빼곤 완벽한 숙소에요! | 0.27999999999999997 | | 이어 현금이 286만 가구(13.2%) 1조3007억원, 선불카드가 75만 가구(3.5%) 4990억원, 지역사랑상품권은 63만 가구(2.9%) 4171억원으로 각각 집계됐다. | 이어 현금 286만 가구(13.2%), 현금 1조337억 원, 선불카드 75만 가구(3.5%), 4990억 원, 지역사랑상품권 63만 가구(2.9%), 4171억 원 순이었습니다. | 0.86 | * 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 - `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 | 0.0283 | - | | 1.0 | 657 | - | 0.9075 | | 1.5221 | 1000 | 0.0082 | 0.9148 | | 2.0 | 1314 | - | 0.9148 | | 2.2831 | 1500 | 0.0047 | - | | 3.0 | 1971 | - | 0.9180 | | 3.0441 | 2000 | 0.0034 | 0.9168 | | 3.8052 | 2500 | 0.0027 | - | | 4.0 | 2628 | - | 0.9196 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.017 kWh - **Carbon Emitted**: 0.007 kg of CO2 - **Hours Used**: 0.057 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 4090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700 - **RAM Size**: 62.57 GB ### Framework Versions - Python: 3.9.0 - Sentence Transformers: 3.0.1 - Transformers: 4.44.1 - PyTorch: 2.3.1+cu121 - Accelerate: 0.33.0 - Datasets: 2.19.1 - 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", } ```