--- base_model: colorfulscoop/sbert-base-ja library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:401 - loss:CosineSimilarityLoss widget: - source_sentence: 昨夜何を食べたの？ sentences: - 昨日作ったのはチキンヌードル？ - 村長がどうしたの？ - 雲より高くってどこ？ - source_sentence: ナイトスタンドにある？ sentences: - わかんない - スリッパ履いた？ - だめじゃん - source_sentence: センパイ sentences: - 昨日の夜は暑かった - 調子はどう？ - トーチ - source_sentence: 村人はどんな呪文を使うの？ sentences: - スパイク - キミはどんな魔法を使うの？ - うさんくさい - source_sentence: 祭壇の些細な違和感ってどういう意味？ sentences: - 青いオーブがどこにあるか知ってる？ - 赤い染みが皿にあった - これが花 model-index: - name: SentenceTransformer based on colorfulscoop/sbert-base-ja results: - task: type: binary-classification name: Binary Classification dataset: name: custom arc semantics data jp type: custom-arc-semantics-data-jp metrics: - type: cosine_accuracy value: 0.8855721393034826 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6970740556716919 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7766990291262137 name: Cosine F1 - type: cosine_f1_threshold value: 0.6637545228004456 name: Cosine F1 Threshold - type: cosine_precision value: 0.8163265306122449 name: Cosine Precision - type: cosine_recall value: 0.7407407407407407 name: Cosine Recall - type: cosine_ap value: 0.6606381605892593 name: Cosine Ap - type: dot_accuracy value: 0.8805970149253731 name: Dot Accuracy - type: dot_accuracy_threshold value: 378.6933898925781 name: Dot Accuracy Threshold - type: dot_f1 value: 0.7647058823529411 name: Dot F1 - type: dot_f1_threshold value: 378.6933898925781 name: Dot F1 Threshold - type: dot_precision value: 0.8125 name: Dot Precision - type: dot_recall value: 0.7222222222222222 name: Dot Recall - type: dot_ap value: 0.6865123266544332 name: Dot Ap - type: manhattan_accuracy value: 0.8855721393034826 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 407.9349365234375 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.7766990291262137 name: Manhattan F1 - type: manhattan_f1_threshold value: 426.941650390625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8163265306122449 name: Manhattan Precision - type: manhattan_recall value: 0.7407407407407407 name: Manhattan Recall - type: manhattan_ap value: 0.6609390536301427 name: Manhattan Ap - type: euclidean_accuracy value: 0.8855721393034826 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 18.663713455200195 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.7766990291262137 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.35655975341797 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.8163265306122449 name: Euclidean Precision - type: euclidean_recall value: 0.7407407407407407 name: Euclidean Recall - type: euclidean_ap value: 0.6602743223356511 name: Euclidean Ap - type: max_accuracy value: 0.8855721393034826 name: Max Accuracy - type: max_accuracy_threshold value: 407.9349365234375 name: Max Accuracy Threshold - type: max_f1 value: 0.7766990291262137 name: Max F1 - type: max_f1_threshold value: 426.941650390625 name: Max F1 Threshold - type: max_precision value: 0.8163265306122449 name: Max Precision - type: max_recall value: 0.7407407407407407 name: Max Recall - type: max_ap value: 0.6865123266544332 name: Max Ap --- # SentenceTransformer based on colorfulscoop/sbert-base-ja This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. 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:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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: BertModel (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 = [ '祭壇の些細な違和感ってどういう意味？', '青いオーブがどこにあるか知ってる？', 'これが花', ] 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 #### Binary Classification * Dataset: `custom-arc-semantics-data-jp` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.8856 | | cosine_accuracy_threshold | 0.6971 | | cosine_f1 | 0.7767 | | cosine_f1_threshold | 0.6638 | | cosine_precision | 0.8163 | | cosine_recall | 0.7407 | | cosine_ap | 0.6606 | | dot_accuracy | 0.8806 | | dot_accuracy_threshold | 378.6934 | | dot_f1 | 0.7647 | | dot_f1_threshold | 378.6934 | | dot_precision | 0.8125 | | dot_recall | 0.7222 | | dot_ap | 0.6865 | | manhattan_accuracy | 0.8856 | | manhattan_accuracy_threshold | 407.9349 | | manhattan_f1 | 0.7767 | | manhattan_f1_threshold | 426.9417 | | manhattan_precision | 0.8163 | | manhattan_recall | 0.7407 | | manhattan_ap | 0.6609 | | euclidean_accuracy | 0.8856 | | euclidean_accuracy_threshold | 18.6637 | | euclidean_f1 | 0.7767 | | euclidean_f1_threshold | 19.3566 | | euclidean_precision | 0.8163 | | euclidean_recall | 0.7407 | | euclidean_ap | 0.6603 | | max_accuracy | 0.8856 | | max_accuracy_threshold | 407.9349 | | max_f1 | 0.7767 | | max_f1_threshold | 426.9417 | | max_precision | 0.8163 | | max_recall | 0.7407 | | **max_ap** | **0.6865** | ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 401 training samples * Columns: text1, text2, and label * Approximate statistics based on the first 401 samples: | | text1 | text2 | label | |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | text1 | text2 | label | |:-----------------------------------|:-------------------------|:---------------| | 雲より高くってどういう意味？ | 猫好き | 0 | | 花の囁きってなに？ | リリアンについて教えて | 0 | | リリアンってものの姿を変える魔法を使える？ | どんな魔法なの？ | 0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### csv * Dataset: csv * Size: 401 evaluation samples * Columns: text1, text2, and label * Approximate statistics based on the first 401 samples: | | text1 | text2 | label | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | text1 | text2 | label | |:-----------------------------|:------------------------------|:---------------| | 棚からトマトがなくなってたから | トマトが棚からなくなっていたから | 1 | | 欲しくない | 家の中へ行こう | 0 | | 昨日は何を作ったの？ | ビーフシチュー食べた？ | 0 | * 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`: epoch - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `warmup_ratio`: 0.4 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `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 - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.4 - `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`: True - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap | |:-----:|:----:|:-------------:|:------:|:-----------------------------------:| | 1.0 | 25 | 0.2393 | 0.2390 | 0.5655 | | 2.0 | 50 | 0.1724 | 0.1689 | 0.6075 | | 3.0 | 75 | 0.1046 | 0.1326 | 0.6478 | | 4.0 | 100 | 0.062 | 0.1183 | 0.6618 | | 5.0 | 125 | 0.0349 | 0.1158 | 0.6683 | | 6.0 | 150 | 0.0258 | 0.1142 | 0.6772 | | 7.0 | 175 | 0.0211 | 0.1168 | 0.6739 | | 8.0 | 200 | 0.0204 | 0.1180 | 0.6765 | | 9.0 | 225 | 0.0194 | 0.1178 | 0.6869 | | 10.0 | 250 | 0.0185 | 0.1180 | 0.6865 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.1.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - 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", } ```