| --- |
| 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: 추운날이니 외출은 자제해주시기 바랍니다. |
| sentences: |
| - 추운날인데 외출하지마 |
| - 소·돼지에 대해서만 실시하던 축산물이력제가 1월 1일부터 닭·오리·계란까지 확대·시행된다. |
| - 광고메일함 비중이 에어비앤비가 더 높니 트립닷컴이 더 많니? |
| - source_sentence: 샤워기도 수압이 너무 약해서 불편해요. |
| sentences: |
| - 숙소 내부가 넓고 호스트도 1층에 있어 불편사항에 대한 피드백을 즉시 받으실 수 있습니다. |
| - 그외에 물놀이를 하기위한 준비물들 파라솔 비치의자 어린이비치의자 아이스박스 핸드케리어 비치타월 모레놀이도구 등등 필요한 모든것이 완벽했습니다. |
| - 샤워는 수압이 너무 약해서 불편해요. |
| - source_sentence: 조용한 분위기의 방을 구하시면 이 곳이 최고입니다! |
| sentences: |
| - 시험을 이번달에 본다고 했니 다음달에 본다고 했니? |
| - 조용한 방을 찾는다면, 이곳이 최고예요! |
| - 어른들과 만나는 자리에는 어른들보다 늦게 도착하지 말고 일찍 나가 있어라. |
| - source_sentence: 발코니쪽 창문은 3개중에 한개만 열수있습니다. |
| sentences: |
| - 많은 장비를 구매할 필요 없이 즐길 수 있습니다. |
| - 우리는 그 숙소에서 호바트의 최상의 상태를 유지할 수 있었습니다. |
| - 직장가입자의 급여명세서, 지역가입자의 건강보험 급여통지서를 확인하실 수 있습니다. |
| - source_sentence: 국민 추천으로 ‘금융규제 유연화로 선제적 금융권 지원역량 강화’도 우수 사례로 언급됐다. |
| sentences: |
| - 국민의 권고에 따라 '유연한 금융규제 등을 통해 선제적으로 금융분야 지원능력 강화'도 좋은 사례로 꼽혔습니다. |
| - 사진으로 보이는거 보다 숙소는 넓었고요 |
| - 저는 다음에 대만을 간다면 무조건 재방문 할 예정입니다! |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.9626619602187976 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9247880695962829 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.9555167285690431 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.923408354022865 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.9556439523907834 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.9235806565450854 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.957361957340705 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.9130155209197447 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.9626619602187976 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.9247880695962829 |
| name: Spearman Max |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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': 256, 'do_lower_case': True}) 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 = [ |
| '국민 추천으로 ‘금융규제 유연화로 선제적 금융권 지원역량 강화’도 우수 사례로 언급됐다.', |
| "국민의 권고에 따라 '유연한 금융규제 등을 통해 선제적으로 금융분야 지원능력 강화'도 좋은 사례로 꼽혔습니다.", |
| '사진으로 보이는거 보다 숙소는 넓었고요', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
| * Dataset: `sts-dev` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:-------------------|:-----------| |
| | pearson_cosine | 0.9627 | |
| | spearman_cosine | 0.9248 | |
| | pearson_manhattan | 0.9555 | |
| | spearman_manhattan | 0.9234 | |
| | pearson_euclidean | 0.9556 | |
| | spearman_euclidean | 0.9236 | |
| | pearson_dot | 0.9574 | |
| | spearman_dot | 0.913 | |
| | pearson_max | 0.9627 | |
| | **spearman_max** | **0.9248** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 10,501 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 20.16 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.75 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:------------------------------------|:------------------------------------------------|:-----------------| |
| | <code>단점을 꼽자면 엘베가 없다는 점 정도?</code> | <code>굳이 단점을 꼽자면 늦은 밤에는 역 근처가 살짝 무섭다는 거?</code> | <code>0.2</code> | |
| | <code>더울 때는 청량음료 말고 물 많이 마셔.</code> | <code>추울 때 손과 발은 내놓지 말자.</code> | <code>0.0</code> | |
| | <code>위치, 시설, 호스팅 모두 만족했습니다.</code> | <code>위치, 시설, 호스팅 모두 만족스러웠습니다.</code> | <code>1.0</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `num_train_epochs`: 7 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `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`: 7 |
| - `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 |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | sts-dev_spearman_max | |
| |:------:|:----:|:-------------:|:--------------------:| |
| | 1.0 | 329 | - | 0.9218 | |
| | 1.5198 | 500 | 0.0096 | - | |
| | 2.0 | 658 | - | 0.9218 | |
| | 3.0 | 987 | - | 0.9215 | |
| | 3.0395 | 1000 | 0.0064 | 0.9218 | |
| | 4.0 | 1316 | - | 0.9231 | |
| | 4.5593 | 1500 | 0.0055 | - | |
| | 5.0 | 1645 | - | 0.9231 | |
| | 6.0 | 1974 | - | 0.9235 | |
| | 6.0790 | 2000 | 0.0045 | 0.9226 | |
| | 7.0 | 2303 | - | 0.9248 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.1.1 |
| - Transformers: 4.44.2 |
| - PyTorch: 2.4.1+cu121 |
| - Accelerate: 0.34.2 |
| - Datasets: 3.0.1 |
| - 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", |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
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| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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