--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:355097 - loss:CategoricalContrastiveLoss widget: - 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：普通ｺﾝｸﾘｰﾄ。 - 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：段鼻ﾀｲﾙ。 - source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。 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：タイル。名称：手洗い水周りﾀｲﾙ（A）。 - 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：巾木磁器質ﾀｲﾙ。 sentences: - 科目：タイル。名称：段鼻磁器質ﾀｲﾙ。 - 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：海街ﾃﾞｯｷ床ﾀｲﾙ。 - source_sentence: 科目：コンクリート。名称：機械式移動座席基礎ｺﾝｸﾘｰﾄ。 sentences: - 科目：タイル。名称：#階廊下#ｽﾛｰﾌﾟ床磁器質ﾀｲﾙA。 - 科目：コンクリート。名称：構造体強度補正。 - 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。 - 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：EXP_J充填ｺﾝｸﾘｰﾄ。 - 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：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送基本料金。 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_3") # Run inference sentences = [ '科目：コンクリート。名称：EXP_J充填ｺﾝｸﾘｰﾄ。', '科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送基本料金。', '科目：コンクリート。名称：EXP_J充填ｺﾝｸﾘｰﾄ。', ] 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: 355,097 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 | * 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 | |:------:|:----:|:-------------:| | 0.0360 | 50 | 0.0445 | | 0.0720 | 100 | 0.0441 | | 0.1081 | 150 | 0.0409 | | 0.1441 | 200 | 0.0425 | | 0.1801 | 250 | 0.0374 | | 0.2161 | 300 | 0.0356 | | 0.2522 | 350 | 0.0345 | | 0.2882 | 400 | 0.0338 | | 0.3242 | 450 | 0.0312 | | 0.3602 | 500 | 0.0274 | | 0.3963 | 550 | 0.0281 | | 0.4323 | 600 | 0.0298 | | 0.4683 | 650 | 0.028 | | 0.5043 | 700 | 0.0282 | | 0.5403 | 750 | 0.0273 | | 0.5764 | 800 | 0.0244 | | 0.6124 | 850 | 0.0238 | | 0.6484 | 900 | 0.021 | | 0.6844 | 950 | 0.0206 | | 0.7205 | 1000 | 0.0234 | | 0.7565 | 1050 | 0.019 | | 0.7925 | 1100 | 0.0181 | | 0.8285 | 1150 | 0.0183 | | 0.8646 | 1200 | 0.0187 | | 0.9006 | 1250 | 0.0149 | | 0.9366 | 1300 | 0.017 | | 0.9726 | 1350 | 0.0158 | | 1.0086 | 1400 | 0.0133 | | 1.0447 | 1450 | 0.0124 | | 1.0807 | 1500 | 0.0143 | | 1.1167 | 1550 | 0.0131 | | 1.1527 | 1600 | 0.0119 | | 1.1888 | 1650 | 0.0112 | | 1.2248 | 1700 | 0.0117 | | 1.2608 | 1750 | 0.0107 | | 1.2968 | 1800 | 0.0099 | | 1.3329 | 1850 | 0.0112 | | 1.3689 | 1900 | 0.01 | | 1.4049 | 1950 | 0.0105 | | 1.4409 | 2000 | 0.0092 | | 1.4769 | 2050 | 0.0095 | | 1.5130 | 2100 | 0.0104 | | 1.5490 | 2150 | 0.0087 | | 1.5850 | 2200 | 0.0092 | | 1.6210 | 2250 | 0.0088 | | 1.6571 | 2300 | 0.0088 | | 1.6931 | 2350 | 0.0098 | | 1.7291 | 2400 | 0.0086 | | 1.7651 | 2450 | 0.0091 | | 1.8012 | 2500 | 0.0072 | | 1.8372 | 2550 | 0.0069 | | 1.8732 | 2600 | 0.0076 | | 1.9092 | 2650 | 0.0069 | | 1.9452 | 2700 | 0.0077 | | 1.9813 | 2750 | 0.0076 | | 2.0173 | 2800 | 0.0065 | | 2.0533 | 2850 | 0.0067 | | 2.0893 | 2900 | 0.0059 | | 2.1254 | 2950 | 0.0061 | | 2.1614 | 3000 | 0.0055 | | 2.1974 | 3050 | 0.0055 | | 2.2334 | 3100 | 0.0057 | | 2.2695 | 3150 | 0.0058 | | 2.3055 | 3200 | 0.0069 | | 2.3415 | 3250 | 0.0058 | | 2.3775 | 3300 | 0.0054 | | 2.4135 | 3350 | 0.0058 | | 2.4496 | 3400 | 0.0047 | | 2.4856 | 3450 | 0.0045 | | 2.5216 | 3500 | 0.0054 | | 2.5576 | 3550 | 0.0041 | | 2.5937 | 3600 | 0.0048 | | 2.6297 | 3650 | 0.0038 | | 2.6657 | 3700 | 0.0048 | | 2.7017 | 3750 | 0.0047 | | 2.7378 | 3800 | 0.005 | | 2.7738 | 3850 | 0.0046 | | 2.8098 | 3900 | 0.0045 | | 2.8458 | 3950 | 0.0042 | | 2.8818 | 4000 | 0.0049 | | 2.9179 | 4050 | 0.0043 | | 2.9539 | 4100 | 0.0042 | | 2.9899 | 4150 | 0.0039 | | 3.0259 | 4200 | 0.004 | | 3.0620 | 4250 | 0.0032 | | 3.0980 | 4300 | 0.0038 | | 3.1340 | 4350 | 0.0034 | | 3.1700 | 4400 | 0.0033 | | 3.2061 | 4450 | 0.0036 | | 3.2421 | 4500 | 0.0029 | | 3.2781 | 4550 | 0.0032 | | 3.3141 | 4600 | 0.0036 | | 3.3501 | 4650 | 0.0046 | | 3.3862 | 4700 | 0.0037 | | 3.4222 | 4750 | 0.0035 | | 3.4582 | 4800 | 0.0034 | | 3.4942 | 4850 | 0.0038 | | 3.5303 | 4900 | 0.0034 | | 3.5663 | 4950 | 0.0035 | | 3.6023 | 5000 | 0.0037 | | 3.6383 | 5050 | 0.0031 | | 3.6744 | 5100 | 0.0042 | | 3.7104 | 5150 | 0.0034 | | 3.7464 | 5200 | 0.0035 | | 3.7824 | 5250 | 0.0032 | | 3.8184 | 5300 | 0.0032 | | 3.8545 | 5350 | 0.0035 | | 3.8905 | 5400 | 0.003 | | 3.9265 | 5450 | 0.0033 | | 3.9625 | 5500 | 0.0037 | | 3.9986 | 5550 | 0.0028 |
### 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", } ```