--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:197418 - loss:CategoricalContrastiveLoss widget: - source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：多目的ﾎｰﾙ浮き床ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：ｼﾝﾀﾞｰｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0038。 - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。 - source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。 sentences: - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC30+ΔS（構造体補正）S18　粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 3018K免震層上部ｺﾝ。 - 科目：コンクリート。名称：多目的ﾎｰﾙ間柱基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0041。 - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。 - source_sentence: 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。 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：タイル。名称：床ﾀｲﾙK。 - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0039。 - 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：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15粗骨材20高性能AE減水剤･防水剤入。備考：刊-ｺﾝ 3315KB基礎部ｺﾝ。 - 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。摘要：Fc24 S18粗骨材20。備考：代価表 0123。 - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。 - source_sentence: 科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。 - 科目：タイル。名称：階段段鼻ﾉﾝｽﾘｯﾌﾟ役物ﾀｲﾙ。 - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表 0054。 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-v_1_0_7_5") # Run inference sentences = [ '科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。', '科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。', '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表 0054。', ] 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: 197,418 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：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 | 科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。 | 1 | | 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 | 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場捨てｺﾝ。 | 0 | | 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 | 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体 ﾎﾟﾝﾌﾟ打設100m3/回以上 S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 No.1 市場地上部ｺﾝ（1F）。 | 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`: 20 - `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`: 20 - `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} - `tp_size`: 0 - `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 | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0648 | 50 | 0.2993 | | 0.1295 | 100 | 0.1925 | | 0.1943 | 150 | 0.1197 | | 0.2591 | 200 | 0.1054 | | 0.3238 | 250 | 0.0849 | | 0.3886 | 300 | 0.0854 | | 0.4534 | 350 | 0.0716 | | 0.5181 | 400 | 0.0659 | | 0.5829 | 450 | 0.0641 | | 0.6477 | 500 | 0.0641 | | 0.7124 | 550 | 0.0619 | | 0.7772 | 600 | 0.0589 | | 0.8420 | 650 | 0.0564 | | 0.9067 | 700 | 0.0506 | | 0.9715 | 750 | 0.0513 | | 1.0363 | 800 | 0.0473 | | 1.1010 | 850 | 0.0451 | | 1.1658 | 900 | 0.044 | | 1.2306 | 950 | 0.0418 | | 1.2953 | 1000 | 0.042 | | 1.3601 | 1050 | 0.0337 | | 1.4249 | 1100 | 0.0337 | | 1.4896 | 1150 | 0.0354 | | 1.5544 | 1200 | 0.0353 | | 1.6192 | 1250 | 0.0353 | | 1.6839 | 1300 | 0.0323 | | 1.7487 | 1350 | 0.0297 | | 1.8135 | 1400 | 0.0331 | | 1.8782 | 1450 | 0.0303 | | 1.9430 | 1500 | 0.0286 | | 2.0078 | 1550 | 0.0265 | | 2.0725 | 1600 | 0.0257 | | 2.1373 | 1650 | 0.0195 | | 2.2021 | 1700 | 0.0225 | | 2.2668 | 1750 | 0.0206 | | 2.3316 | 1800 | 0.0231 | | 2.3964 | 1850 | 0.0225 | | 2.4611 | 1900 | 0.0203 | | 2.5259 | 1950 | 0.0207 | | 2.5907 | 2000 | 0.02 | | 2.6554 | 2050 | 0.0181 | | 2.7202 | 2100 | 0.0202 | | 2.7850 | 2150 | 0.0187 | | 2.8497 | 2200 | 0.0192 | | 2.9145 | 2250 | 0.0168 | | 2.9793 | 2300 | 0.0162 | | 3.0440 | 2350 | 0.0159 | | 3.1088 | 2400 | 0.0145 | | 3.1736 | 2450 | 0.0134 | | 3.2383 | 2500 | 0.0138 | | 3.3031 | 2550 | 0.0125 | | 3.3679 | 2600 | 0.0132 | | 3.4326 | 2650 | 0.0122 | | 3.4974 | 2700 | 0.0133 | | 3.5622 | 2750 | 0.0127 | | 3.6269 | 2800 | 0.0125 | | 3.6917 | 2850 | 0.0107 | | 3.7565 | 2900 | 0.0114 | | 3.8212 | 2950 | 0.0104 | | 3.8860 | 3000 | 0.0107 | | 3.9508 | 3050 | 0.0112 | | 4.0155 | 3100 | 0.0084 | | 4.0803 | 3150 | 0.0086 | | 4.1451 | 3200 | 0.0077 | | 4.2098 | 3250 | 0.0098 | | 4.2746 | 3300 | 0.0068 | | 4.3394 | 3350 | 0.0082 | | 4.4041 | 3400 | 0.0064 | | 4.4689 | 3450 | 0.0083 | | 4.5337 | 3500 | 0.0065 | | 4.5984 | 3550 | 0.0067 | | 4.6632 | 3600 | 0.0074 | | 4.7280 | 3650 | 0.0078 | | 4.7927 | 3700 | 0.0072 | | 4.8575 | 3750 | 0.0077 | | 4.9223 | 3800 | 0.007 | | 4.9870 | 3850 | 0.0067 | | 5.0518 | 3900 | 0.0057 | | 5.1166 | 3950 | 0.0054 | | 5.1813 | 4000 | 0.0046 | ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - 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", } ```