--- 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:574417 - loss:MultipleNegativesRankingLoss - loss:CosineSimilarityLoss widget: - source_sentence: 이집트 대통령 선거에서 가까운 여론조사 sentences: - 알 카에다 충돌, 폭발로 예멘에서 35명의 군인이 사망 - '보도자료 : 예멘 대통령 선거' - 반 파이프에 스케이트보드를 신은 남자 - source_sentence: 한 소년이 팽창식 슬라이드를 내려간다. sentences: - 빨간 옷을 입은 소년이 부풀릴 수 있는 놀이기구를 타고 내려간다. - 새들이 물속에서 헤엄치고 있다. - 여자는 녹색 후추를 썰었다. - source_sentence: 비상 차량들이 현장에 있다. sentences: - 구급차와 소방차가 현장에서 도움을 주려고 한다. - 유물을 보는 사람들이 있다. - 구급차와 소방차에 불이 붙었다. - source_sentence: 그들은 서로 가까이 있지 않다. sentences: - 그 품질은 레이저에 가깝다. - 그들은 샤토와 매우 가깝다. - 그들은 샤토와 서로 어느 정도 떨어져 있다. - source_sentence: 딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다. sentences: - 남자가 자고 있다. - 사람들이 말하고 있다. - 한 남자가 트럭을 보고 있다. model-index: - name: SentenceTransformer based on klue/roberta-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8650328554572645 name: Pearson Cosine - type: spearman_cosine value: 0.8667952293243948 name: Spearman Cosine - type: pearson_manhattan value: 0.8558437246473041 name: Pearson Manhattan - type: spearman_manhattan value: 0.860673936504169 name: Spearman Manhattan - type: pearson_euclidean value: 0.8562228685196989 name: Pearson Euclidean - type: spearman_euclidean value: 0.8612884653822855 name: Spearman Euclidean - type: pearson_dot value: 0.830160661850442 name: Pearson Dot - type: spearman_dot value: 0.8275972106510755 name: Spearman Dot - type: pearson_max value: 0.8650328554572645 name: Pearson Max - type: spearman_max value: 0.8667952293243948 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:** 128 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': 128, '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 = [ '딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다.', '한 남자가 트럭을 보고 있다.', '남자가 자고 있다.', ] 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 * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.865 | | spearman_cosine | 0.8668 | | pearson_manhattan | 0.8558 | | spearman_manhattan | 0.8607 | | pearson_euclidean | 0.8562 | | spearman_euclidean | 0.8613 | | pearson_dot | 0.8302 | | spearman_dot | 0.8276 | | pearson_max | 0.865 | | **spearman_max** | **0.8668** | ## Training Details ### Training Datasets #### Unnamed Dataset * Size: 568,640 training samples * Columns: sentence_0, sentence_1, and sentence_2 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:----------------------------------------|:-------------------------------------------------|:--------------------------------------| | 발생 부하가 함께 5% 적습니다. | 발생 부하의 5% 감소와 함께 11. | 발생 부하가 5% 증가합니다. | | 어떤 행사를 위해 음식과 옷을 배급하는 여성들. | 여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다. | 여자들이 사막에서 오토바이를 운전하고 있다. | | 어린 아이들은 그 지식을 얻을 필요가 있다. | 응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아. | 젊은 사람들은 배울 필요가 없다. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` #### Unnamed Dataset * Size: 5,777 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 | |:-----------------------------------------|:------------------------------------------|:--------------------------------| | 몰디브 대통령이 경찰의 반란 이후 사임하고, 시위 | 몰디브 대통령이 몇 주 동안의 시위 끝에 그만두다. | 0.6799999999999999 | | 사자가 밀폐된 지역을 걷고 있다. | 사자가 주위를 돌아다니고 있다. | 0.52 | | 한 소년이 노래를 부르고 피아노를 치고 있다. | 한 소년이 피아노를 치고 있다. | 0.6 | * 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 - `num_train_epochs`: 5 - `batch_sampler`: no_duplicates - `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`: 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`: 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`: 5 - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | sts-dev_spearman_max | |:------:|:----:|:-------------:|:--------------------:| | 0.3458 | 500 | 0.4123 | - | | 0.6916 | 1000 | 0.3009 | 0.8365 | | 1.0007 | 1447 | - | 0.8610 | | 1.0367 | 1500 | 0.259 | - | | 1.3824 | 2000 | 0.1301 | 0.8580 | | 1.7282 | 2500 | 0.0898 | - | | 2.0007 | 2894 | - | 0.8668 | ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.2.2+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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```