Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+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
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 355,097 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                         | label                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | int                                                               |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 13.78 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>0: ~74.00%</li><li>1: ~2.60%</li><li>2: ~23.40%</li></ul> |
+* Samples:
+  | sentence1                                | sentence2                                        | label          |
+  |:-----------------------------------------|:-------------------------------------------------|:---------------|
+  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>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。</code>   | <code>0</code> |
+  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。</code>        | <code>0</code> |
+  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>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。</code> | <code>0</code> |
+* Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
+
+### 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
+<details><summary>Click to expand</summary>
+
+- `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
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| 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        |
+
+</details>
+
+### 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cd90f0a4c10d69741b2a85fcd3c127b517dbdf1dc6183ac77aa2001c913b71e
+size 444851048

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "normalized": false,
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+    },
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+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    }
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+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "do_subword_tokenize": true,
+  "do_word_tokenize": true,
+  "extra_special_tokens": {},
+  "jumanpp_kwargs": null,
+  "mask_token": "[MASK]",
+  "mecab_kwargs": {
+    "mecab_dic": "unidic_lite"
+  },
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "subword_tokenizer_type": "wordpiece",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "mecab"
+}