--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:354235 - loss:CategoricalContrastiveLoss widget: - source_sentence: 科目：コンクリート。名称：免震上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。 - 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。 - 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：タイル。名称：ﾋﾟﾛﾃｨ床床ﾀｲﾙ張り。 - 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。 - 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。 - source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。 sentences: - 科目：コンクリート。名称：保護ｺﾝｸﾘｰﾄ。 - 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。 - 科目：タイル。名称：EV#･#床磁器質ﾀｲﾙA（材のみ）。 - source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。 sentences: - 科目：コンクリート。名称：体育館防振床浮き床ｺﾝｸﾘｰﾄ。 - 科目：タイル。名称：EXP_J上床磁器質ﾀｲﾙA。 - 科目：タイル。名称：床ﾀｲﾙB。 - source_sentence: 科目：タイル。名称：ﾀｲﾙ出隅ｺｰﾅｰ。 sentences: - 科目：タイル。名称：地流し床ﾀｲﾙ。 - 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。 - 科目：タイル。名称：段床ﾀｲﾙ。 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_4") # Run inference sentences = [ '科目：タイル。名称：ﾀｲﾙ出隅ｺｰﾅｰ。', '科目：タイル。名称：段床ﾀｲﾙ。', '科目：タイル。名称：地流し床ﾀｲﾙ。', ] 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: 354,235 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 | |:------:|:----:|:-------------:| | 0.0361 | 50 | 0.0492 | | 0.0723 | 100 | 0.0438 | | 0.1084 | 150 | 0.0385 | | 0.1445 | 200 | 0.0376 | | 0.1806 | 250 | 0.0388 | | 0.2168 | 300 | 0.0391 | | 0.2529 | 350 | 0.0337 | | 0.2890 | 400 | 0.0354 | | 0.3251 | 450 | 0.0322 | | 0.3613 | 500 | 0.0345 | | 0.3974 | 550 | 0.0278 | | 0.4335 | 600 | 0.0282 | | 0.4697 | 650 | 0.0261 | | 0.5058 | 700 | 0.0284 | | 0.5419 | 750 | 0.0264 | | 0.5780 | 800 | 0.0251 | | 0.6142 | 850 | 0.02 | | 0.6503 | 900 | 0.0253 | | 0.6864 | 950 | 0.0197 | | 0.7225 | 1000 | 0.0221 | | 0.7587 | 1050 | 0.0204 | | 0.7948 | 1100 | 0.0188 | | 0.8309 | 1150 | 0.0198 | | 0.8671 | 1200 | 0.0203 | | 0.9032 | 1250 | 0.0177 | | 0.9393 | 1300 | 0.0162 | | 0.9754 | 1350 | 0.0148 | | 1.0116 | 1400 | 0.014 | | 1.0477 | 1450 | 0.011 | | 1.0838 | 1500 | 0.0123 | | 1.1199 | 1550 | 0.0126 | | 1.1561 | 1600 | 0.01 | | 1.1922 | 1650 | 0.0124 | | 1.2283 | 1700 | 0.0107 | | 1.2645 | 1750 | 0.0107 | | 1.3006 | 1800 | 0.0118 | | 1.3367 | 1850 | 0.0103 | | 1.3728 | 1900 | 0.0102 | | 1.4090 | 1950 | 0.0104 | | 1.4451 | 2000 | 0.01 | | 1.4812 | 2050 | 0.0101 | | 1.5173 | 2100 | 0.0098 | | 1.5535 | 2150 | 0.0097 | | 1.5896 | 2200 | 0.0093 | | 1.6257 | 2250 | 0.0088 | | 1.6618 | 2300 | 0.0095 | | 1.6980 | 2350 | 0.0103 | | 1.7341 | 2400 | 0.0077 | | 1.7702 | 2450 | 0.0085 | | 1.8064 | 2500 | 0.0082 | | 1.8425 | 2550 | 0.0074 | | 1.8786 | 2600 | 0.0081 | | 1.9147 | 2650 | 0.0067 | | 1.9509 | 2700 | 0.0082 | | 1.9870 | 2750 | 0.0076 | | 2.0231 | 2800 | 0.0067 | | 2.0592 | 2850 | 0.0056 | | 2.0954 | 2900 | 0.0065 | | 2.1315 | 2950 | 0.0057 | | 2.1676 | 3000 | 0.0059 | | 2.2038 | 3050 | 0.0047 | | 2.2399 | 3100 | 0.0051 | | 2.2760 | 3150 | 0.0049 | | 2.3121 | 3200 | 0.0051 | | 2.3483 | 3250 | 0.0049 | | 2.3844 | 3300 | 0.0045 | | 2.4205 | 3350 | 0.0047 | | 2.4566 | 3400 | 0.0052 | | 2.4928 | 3450 | 0.004 | | 2.5289 | 3500 | 0.0057 | | 2.5650 | 3550 | 0.0046 | | 2.6012 | 3600 | 0.0052 | | 2.6373 | 3650 | 0.0049 | | 2.6734 | 3700 | 0.0046 | | 2.7095 | 3750 | 0.0056 | | 2.7457 | 3800 | 0.0054 | | 2.7818 | 3850 | 0.0037 | | 2.8179 | 3900 | 0.0044 | | 2.8540 | 3950 | 0.0037 | | 2.8902 | 4000 | 0.0049 | | 2.9263 | 4050 | 0.0044 | | 2.9624 | 4100 | 0.0046 | | 2.9986 | 4150 | 0.0041 | | 3.0347 | 4200 | 0.0044 | | 3.0708 | 4250 | 0.0035 | | 3.1069 | 4300 | 0.0029 | | 3.1431 | 4350 | 0.0035 | | 3.1792 | 4400 | 0.0031 | | 3.2153 | 4450 | 0.0038 | | 3.2514 | 4500 | 0.0039 | | 3.2876 | 4550 | 0.0034 | | 3.3237 | 4600 | 0.0043 | | 3.3598 | 4650 | 0.0042 | | 3.3960 | 4700 | 0.004 | | 3.4321 | 4750 | 0.0028 | | 3.4682 | 4800 | 0.0035 | | 3.5043 | 4850 | 0.0033 | | 3.5405 | 4900 | 0.0039 | | 3.5766 | 4950 | 0.0045 | | 3.6127 | 5000 | 0.0032 | | 3.6488 | 5050 | 0.0036 | | 3.6850 | 5100 | 0.0032 | | 3.7211 | 5150 | 0.0031 | | 3.7572 | 5200 | 0.0043 | | 3.7934 | 5250 | 0.0032 | | 3.8295 | 5300 | 0.0034 | | 3.8656 | 5350 | 0.0029 | | 3.9017 | 5400 | 0.0037 | | 3.9379 | 5450 | 0.0028 | | 3.9740 | 5500 | 0.0028 |
### Framework Versions - Python: 3.11.13 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - 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", } ```