--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:327543 - loss:CategoricalContrastiveLoss widget: - source_sentence: 科目：コンクリート。名称：免震下部鉄筋ｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ打設手間。 - 科目：コンクリート。名称：嵩上げｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：構造体強度補正。 - source_sentence: 科目：タイル。名称：段床ﾀｲﾙ。 sentences: - 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。 - 科目：タイル。名称：外壁ｶﾞﾗｽﾓｻﾞｲｸﾀｲﾙ張り。 - 科目：タイル。名称：地流しﾗｲﾆﾝｸﾞ壁ﾀｲﾙ。 - source_sentence: 科目：タイル。名称：段床磁器質ﾀｲﾙ。 sentences: - 科目：コンクリート。名称：構造体強度補正。 - 科目：タイル。名称：ﾊﾟﾝﾄﾞﾘｰ･ﾗｳﾝｼﾞ流し台側壁ﾓｻﾞｲｸﾀｲﾙ-#。 - 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。 - source_sentence: 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ立上り床ﾀｲﾙ。 sentences: - 科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。 - 科目：タイル。名称：床ﾀｲﾙ張り。 - source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。 sentences: - 科目：タイル。名称：外壁ｶﾞﾗｽﾓｻﾞｲｸﾀｲﾙ張り。 - 科目：コンクリート。名称：底盤ｺﾝｸﾘｰﾄ打設手間。 - 科目：コンクリート。名称：B#F/B#FLｺﾝｸﾘｰﾄ打設手間。 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # 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 - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **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: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8") # Run inference sentences = [ '科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。', '科目：コンクリート。名称：底盤ｺﾝｸﾘｰﾄ打設手間。', '科目：タイル。名称：外壁ｶﾞﾗｽﾓｻﾞｲｸﾀｲﾙ張り。', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 327,543 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-----------------------------------------|:-------------------------------------------------|:---------------| | 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。 | 0 | | 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部（外周基礎梁）ｺﾝｸﾘｰﾄ打設手間。 | 0 | | 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。 | 0 | * Loss: sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 4 - `warmup_ratio`: 0.2 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `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`: 1e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `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 - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0391 | 50 | 0.0432 | | 0.0781 | 100 | 0.0449 | | 0.1172 | 150 | 0.0429 | | 0.1562 | 200 | 0.0397 | | 0.1953 | 250 | 0.0395 | | 0.2344 | 300 | 0.0312 | | 0.2734 | 350 | 0.0347 | | 0.3125 | 400 | 0.0303 | | 0.3516 | 450 | 0.0298 | | 0.3906 | 500 | 0.0321 | | 0.4297 | 550 | 0.0266 | | 0.4688 | 600 | 0.0254 | | 0.5078 | 650 | 0.0267 | | 0.5469 | 700 | 0.0244 | | 0.5859 | 750 | 0.0238 | | 0.625 | 800 | 0.0229 | | 0.6641 | 850 | 0.023 | | 0.7031 | 900 | 0.0189 | | 0.7422 | 950 | 0.0207 | | 0.7812 | 1000 | 0.0201 | | 0.8203 | 1050 | 0.0188 | | 0.8594 | 1100 | 0.0153 | | 0.8984 | 1150 | 0.0168 | | 0.9375 | 1200 | 0.014 | | 0.9766 | 1250 | 0.0155 | | 1.0156 | 1300 | 0.0141 | | 1.0547 | 1350 | 0.0139 | | 1.0938 | 1400 | 0.0121 | | 1.1328 | 1450 | 0.0121 | | 1.1719 | 1500 | 0.0109 | | 1.2109 | 1550 | 0.0116 | | 1.25 | 1600 | 0.0119 | | 1.2891 | 1650 | 0.0102 | | 1.3281 | 1700 | 0.0095 | | 1.3672 | 1750 | 0.0089 | | 1.4062 | 1800 | 0.0109 | | 1.4453 | 1850 | 0.0094 | | 1.4844 | 1900 | 0.0094 | | 1.5234 | 1950 | 0.0089 | | 1.5625 | 2000 | 0.0088 | | 1.6016 | 2050 | 0.0081 | | 1.6406 | 2100 | 0.0082 | | 1.6797 | 2150 | 0.0072 | | 1.7188 | 2200 | 0.0075 | | 1.7578 | 2250 | 0.0078 | | 1.7969 | 2300 | 0.0081 | | 1.8359 | 2350 | 0.0079 | | 1.875 | 2400 | 0.008 | | 1.9141 | 2450 | 0.0079 | | 1.9531 | 2500 | 0.0071 | | 1.9922 | 2550 | 0.0089 | | 2.0312 | 2600 | 0.0063 | | 2.0703 | 2650 | 0.0055 | | 2.1094 | 2700 | 0.0053 | | 2.1484 | 2750 | 0.0053 | | 2.1875 | 2800 | 0.0054 | | 2.2266 | 2850 | 0.0046 | | 2.2656 | 2900 | 0.005 | | 2.3047 | 2950 | 0.0053 | | 2.3438 | 3000 | 0.0047 | | 2.3828 | 3050 | 0.0052 | | 2.4219 | 3100 | 0.0049 | | 2.4609 | 3150 | 0.0055 | | 2.5 | 3200 | 0.0047 | | 2.5391 | 3250 | 0.0048 | | 2.5781 | 3300 | 0.0046 | | 2.6172 | 3350 | 0.0049 | | 2.6562 | 3400 | 0.0049 | | 2.6953 | 3450 | 0.0051 | | 2.7344 | 3500 | 0.0045 | | 2.7734 | 3550 | 0.0044 | | 2.8125 | 3600 | 0.0049 | | 2.8516 | 3650 | 0.0048 | | 2.8906 | 3700 | 0.0047 | | 2.9297 | 3750 | 0.0044 | | 2.9688 | 3800 | 0.0041 | | 3.0078 | 3850 | 0.0039 | | 3.0469 | 3900 | 0.0038 | | 3.0859 | 3950 | 0.0033 | | 3.125 | 4000 | 0.0037 | | 3.1641 | 4050 | 0.0036 | | 3.2031 | 4100 | 0.004 | | 3.2422 | 4150 | 0.0036 | | 3.2812 | 4200 | 0.0038 | | 3.3203 | 4250 | 0.004 | | 3.3594 | 4300 | 0.004 | | 3.3984 | 4350 | 0.0039 | | 3.4375 | 4400 | 0.0031 | | 3.4766 | 4450 | 0.0031 | | 3.5156 | 4500 | 0.0038 | | 3.5547 | 4550 | 0.0031 | | 3.5938 | 4600 | 0.0029 | | 3.6328 | 4650 | 0.0031 | | 3.6719 | 4700 | 0.003 | | 3.7109 | 4750 | 0.0036 | | 3.75 | 4800 | 0.0035 | | 3.7891 | 4850 | 0.0029 | | 3.8281 | 4900 | 0.0033 | | 3.8672 | 4950 | 0.0031 | | 3.9062 | 5000 | 0.0036 | | 3.9453 | 5050 | 0.0037 | | 3.9844 | 5100 | 0.0031 |
### Framework Versions - Python: 3.11.13 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.7.0 - Datasets: 2.14.4 - Tokenizers: 0.21.1 ## 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", } ```