--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:210384 - loss:CategoricalContrastiveLoss widget: - 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：ｺﾝｸﾘｰﾄ打設手間･ﾎﾟﾝﾌﾟ圧送。 - 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 0059。 - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟ18高性能AE減水剤。備考：代価表 0032。 - source_sentence: 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：多目的ﾎｰﾙ機械式移動座席基礎ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S15粗骨材20。備考：刊-ｺﾝ 2115嵩上げｺﾝ。 - 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 0064。 - source_sentence: 科目：コンクリート。名称：防振床浮き床ｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：設備基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0036。 - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ。摘要：FC30 S15高性能AE減水剤。備考：代価表 0106。 - 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。 - source_sentence: 科目：コンクリート。名称：EXP_J充填ｺﾝｸﾘｰﾄ。 sentences: - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：基礎部ｽﾛｰﾌﾟｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0048。 - 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ。摘要：FC36 S15粗骨材20 高性能AE減水剤躯体防水材 ﾍﾞｽﾄﾝA同等品以上。備考：代価表 0105。 - source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。 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：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝18N/mm2 Ｓ＝１８ 徳島１。備考：B1-111111 H2906BD 個別嵩上げｺﾝｸﾘｰﾄ。 - 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_10") # Run inference sentences = [ '科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。', '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: 210,384 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 市場免震層下部ｺﾝ。 | 2 | | 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 市場湧水ﾏｯﾄ保護ｺﾝ。 | 2 | * 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`: 10 - `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`: 10 - `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 | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0608 | 50 | 0.3009 | | 0.1217 | 100 | 0.1359 | | 0.1825 | 150 | 0.095 | | 0.2433 | 200 | 0.0808 | | 0.3041 | 250 | 0.0724 | | 0.3650 | 300 | 0.0757 | | 0.4258 | 350 | 0.0608 | | 0.4866 | 400 | 0.0607 | | 0.5474 | 450 | 0.0549 | | 0.6083 | 500 | 0.051 | | 0.6691 | 550 | 0.0517 | | 0.7299 | 600 | 0.0432 | | 0.7908 | 650 | 0.0436 | | 0.8516 | 700 | 0.0418 | | 0.9124 | 750 | 0.04 | | 0.9732 | 800 | 0.0391 | | 1.0341 | 850 | 0.038 | | 1.0949 | 900 | 0.0352 | | 1.1557 | 950 | 0.0329 | | 1.2165 | 1000 | 0.029 | | 1.2774 | 1050 | 0.0283 | | 1.3382 | 1100 | 0.03 | | 1.3990 | 1150 | 0.029 | | 1.4599 | 1200 | 0.0274 | | 1.5207 | 1250 | 0.0261 | | 1.5815 | 1300 | 0.0248 | | 1.6423 | 1350 | 0.0267 | | 1.7032 | 1400 | 0.0234 | | 1.7640 | 1450 | 0.0218 | | 1.8248 | 1500 | 0.0217 | | 1.8856 | 1550 | 0.0195 | | 1.9465 | 1600 | 0.022 | | 2.0073 | 1650 | 0.0195 | | 2.0681 | 1700 | 0.0165 | | 2.1290 | 1750 | 0.0155 | | 2.1898 | 1800 | 0.0156 | | 2.2506 | 1850 | 0.0148 | | 2.3114 | 1900 | 0.0135 | | 2.3723 | 1950 | 0.0122 | | 2.4331 | 2000 | 0.0145 | | 2.4939 | 2050 | 0.0138 | | 2.5547 | 2100 | 0.0133 | | 2.6156 | 2150 | 0.0137 | | 2.6764 | 2200 | 0.0118 | | 2.7372 | 2250 | 0.0132 | | 2.7981 | 2300 | 0.0132 | | 2.8589 | 2350 | 0.0129 | | 2.9197 | 2400 | 0.0109 | | 2.9805 | 2450 | 0.0115 | | 3.0414 | 2500 | 0.0083 | | 3.1022 | 2550 | 0.0082 | | 3.1630 | 2600 | 0.0096 | | 3.2238 | 2650 | 0.0081 | | 3.2847 | 2700 | 0.0081 | | 3.3455 | 2750 | 0.0083 | | 3.4063 | 2800 | 0.01 | | 3.4672 | 2850 | 0.0077 | | 3.5280 | 2900 | 0.0081 | | 3.5888 | 2950 | 0.0088 | | 3.6496 | 3000 | 0.0088 | | 3.7105 | 3050 | 0.0079 | | 3.7713 | 3100 | 0.0075 | | 3.8321 | 3150 | 0.0079 | | 3.8929 | 3200 | 0.0066 | | 3.9538 | 3250 | 0.0081 | | 4.0146 | 3300 | 0.0062 | | 4.0754 | 3350 | 0.0058 | | 4.1363 | 3400 | 0.0055 | | 4.1971 | 3450 | 0.0061 | | 4.2579 | 3500 | 0.006 | | 4.3187 | 3550 | 0.0057 | | 4.3796 | 3600 | 0.0057 | | 4.4404 | 3650 | 0.0061 | | 4.5012 | 3700 | 0.0056 | | 4.5620 | 3750 | 0.005 | | 4.6229 | 3800 | 0.005 | | 4.6837 | 3850 | 0.0054 | | 4.7445 | 3900 | 0.0045 | | 4.8054 | 3950 | 0.0062 | | 4.8662 | 4000 | 0.0052 | ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.52.2 - 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", } ```