Add new SentenceTransformer model with an openvino backend

Hello!

*This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.*

## Full Model Architecture:
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: OVModelForFeatureExtraction
(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})
)
```

## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_11",
revision=f"refs/pr/{pr_number}",
backend="openvino",
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

1_Pooling/config.json

@@ -1,10 +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
+{
+  "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

@@ -1,483 +1,483 @@
----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:72
-- loss:BatchAllTripletLoss
-base_model: cl-nagoya/sup-simcse-ja-base
-widget:
-- source_sentence: 打放し型枠（B種）
-  sentences:
-  - 埋込み（B種）（手間）
-  - 埋込み（C種）（手間）
-  - 盛土A種
-- source_sentence: 埋込み[B種]
-  sentences:
-  - 打放し型枠（A種）
-  - 盛土（C種）（手間）
-  - 埋戻し[C種]
-- source_sentence: 盛土[C種]
-  sentences:
-  - 埋込み[C種]
-  - 盛土（A種）
-  - 盛土[A種]
-- source_sentence: 埋戻し[A種]
-  sentences:
-  - 打放し型枠C種
-  - 打放し型枠（C種）（損料・手間）
-  - 盛土[B種]
-- source_sentence: 埋込み（B種）（損料・手間）
-  sentences:
-  - 埋戻し（A種）（損料）
-  - 埋戻し（C種）（損料・手間）
-  - 埋戻し（B種）（手間）
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
----
-
-# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base). 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:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 -->
-- **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-for-standard-name-v0_9_11")
-# Run inference
-sentences = [
-    '埋込み（B種）（損料・手間）',
-    '埋戻し（A種）（損料）',
-    '埋戻し（B種）（手間）',
-]
-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: 72 training samples
-* Columns: <code>sentence</code> and <code>label</code>
-* Approximate statistics based on the first 72 samples:
-  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-  | details | <ul><li>min: 11 tokens</li><li>mean: 16.21 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.50%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>14: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.50%</li><li>17: ~0.50%</li><li>18: ~0.50%</li><li>19: ~0.50%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>29: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.50%</li><li>32: ~0.50%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>35: ~0.50%</li><li>36: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.50%</li><li>42: ~0.50%</li><li>43: ~0.50%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.50%</li><li>49: ~0.50%</li><li>50: ~0.50%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.80%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.50%</li><li>62: ~0.50%</li><li>63: ~0.50%</li><li>64: ~0.50%</li><li>65: ~0.50%</li><li>66: ~0.50%</li><li>67: ~0.50%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.50%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>76: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.50%</li><li>81: ~0.50%</li><li>82: ~0.50%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>86: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.60%</li><li>89: ~0.50%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.50%</li><li>93: ~0.50%</li><li>94: ~0.50%</li><li>95: ~1.20%</li><li>96: ~1.70%</li><li>97: ~3.90%</li><li>98: ~0.50%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.60%</li><li>102: ~0.50%</li><li>103: ~0.50%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~1.20%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: ~0.50%</li><li>111: ~0.50%</li><li>112: ~0.50%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.50%</li><li>119: ~0.50%</li><li>120: ~0.50%</li><li>121: ~0.50%</li><li>122: ~0.50%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.50%</li><li>133: ~0.50%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.50%</li><li>137: ~0.50%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.50%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.50%</li><li>145: ~0.50%</li><li>146: ~0.70%</li><li>147: ~0.50%</li><li>148: ~3.10%</li><li>149: ~0.50%</li><li>150: ~2.30%</li><li>151: ~0.50%</li><li>152: ~0.50%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.50%</li><li>156: ~0.50%</li><li>157: ~0.50%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.50%</li><li>162: ~0.50%</li><li>163: ~0.50%</li><li>164: ~0.50%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.50%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.50%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.50%</li><li>175: ~0.50%</li><li>176: ~0.50%</li><li>177: ~0.10%</li></ul> |
-* Samples:
-  | sentence                                 | 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>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>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>0</code> |
-* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 512
-- `per_device_eval_batch_size`: 512
-- `learning_rate`: 1e-05
-- `weight_decay`: 0.01
-- `num_train_epochs`: 250
-- `warmup_ratio`: 0.1
-- `fp16`: True
-- `batch_sampler`: group_by_label
-
-#### 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`: 512
-- `per_device_eval_batch_size`: 512
-- `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`: 250
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
-- `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
-- `dispatch_batches`: None
-- `split_batches`: 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`: group_by_label
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-<details><summary>Click to expand</summary>
-
-| Epoch    | Step | Training Loss |
-|:--------:|:----:|:-------------:|
-| 10.0     | 10   | 1.6508        |
-| 20.0     | 20   | 1.2554        |
-| 30.0     | 30   | 0.8495        |
-| 40.0     | 40   | 0.7182        |
-| 50.0     | 50   | 0.6614        |
-| 60.0     | 60   | 0.575         |
-| 70.0     | 70   | 0.5027        |
-| 80.0     | 80   | 0.32          |
-| 90.0     | 90   | 0.1543        |
-| 100.0    | 100  | 0.0102        |
-| 110.0    | 110  | 0.012         |
-| 120.0    | 120  | 0.1164        |
-| 130.0    | 130  | 0.0           |
-| 140.0    | 140  | 0.0           |
-| 150.0    | 150  | 0.0           |
-| 160.0    | 160  | 0.0157        |
-| 170.0    | 170  | 0.0794        |
-| 180.0    | 180  | 0.0           |
-| 190.0    | 190  | 0.0           |
-| 200.0    | 200  | 0.0141        |
-| 210.0    | 210  | 0.0           |
-| 220.0    | 220  | 0.0           |
-| 230.0    | 230  | 0.1115        |
-| 240.0    | 240  | 0.0           |
-| 250.0    | 250  | 0.0           |
-| 260.0    | 260  | 0.0           |
-| 270.0    | 270  | 0.0           |
-| 280.0    | 280  | 0.0           |
-| 290.0    | 290  | 0.0           |
-| 300.0    | 300  | 0.0           |
-| 310.0    | 310  | 0.0           |
-| 320.0    | 320  | 0.0           |
-| 330.0    | 330  | 0.0           |
-| 340.0    | 340  | 0.0           |
-| 350.0    | 350  | 0.0           |
-| 360.0    | 360  | 0.0197        |
-| 370.0    | 370  | 0.0649        |
-| 380.0    | 380  | 0.0           |
-| 390.0    | 390  | 0.0           |
-| 400.0    | 400  | 0.0           |
-| 410.0    | 410  | 0.0           |
-| 420.0    | 420  | 0.0           |
-| 430.0    | 430  | 0.0           |
-| 440.0    | 440  | 0.0           |
-| 450.0    | 450  | 0.0           |
-| 460.0    | 460  | 0.0           |
-| 470.0    | 470  | 0.0           |
-| 480.0    | 480  | 0.0           |
-| 490.0    | 490  | 0.0           |
-| 500.0    | 500  | 0.0           |
-| 3.1842   | 100  | 0.6748        |
-| 6.3684   | 200  | 0.5883        |
-| 9.5526   | 300  | 0.5815        |
-| 12.7368  | 400  | 0.5338        |
-| 16.1053  | 500  | 0.5498        |
-| 19.2895  | 600  | 0.5359        |
-| 22.4737  | 700  | 0.5359        |
-| 25.6579  | 800  | 0.4893        |
-| 29.0263  | 900  | 0.4665        |
-| 32.2105  | 1000 | 0.4205        |
-| 35.3947  | 1100 | 0.4383        |
-| 38.5789  | 1200 | 0.4552        |
-| 41.7632  | 1300 | 0.4003        |
-| 45.1316  | 1400 | 0.3816        |
-| 48.3158  | 1500 | 0.3744        |
-| 51.5     | 1600 | 0.3504        |
-| 54.6842  | 1700 | 0.359         |
-| 58.0526  | 1800 | 0.3019        |
-| 61.2368  | 1900 | 0.3109        |
-| 64.4211  | 2000 | 0.3151        |
-| 67.6053  | 2100 | 0.3292        |
-| 70.7895  | 2200 | 0.2813        |
-| 74.1579  | 2300 | 0.2697        |
-| 77.3421  | 2400 | 0.1975        |
-| 80.5263  | 2500 | 0.2492        |
-| 83.7105  | 2600 | 0.2608        |
-| 87.0789  | 2700 | 0.2401        |
-| 90.2632  | 2800 | 0.2265        |
-| 93.4474  | 2900 | 0.2032        |
-| 96.6316  | 3000 | 0.2368        |
-| 99.8158  | 3100 | 0.2066        |
-| 103.1842 | 3200 | 0.1558        |
-| 106.3684 | 3300 | 0.2029        |
-| 109.5526 | 3400 | 0.244         |
-| 112.7368 | 3500 | 0.1894        |
-| 116.1053 | 3600 | 0.193         |
-| 119.2895 | 3700 | 0.1769        |
-| 122.4737 | 3800 | 0.1821        |
-| 125.6579 | 3900 | 0.0912        |
-| 129.0263 | 4000 | 0.1834        |
-| 132.2105 | 4100 | 0.1391        |
-| 135.3947 | 4200 | 0.1718        |
-| 138.5789 | 4300 | 0.1585        |
-| 141.7632 | 4400 | 0.1829        |
-| 145.1316 | 4500 | 0.1246        |
-| 148.3158 | 4600 | 0.1327        |
-| 151.5    | 4700 | 0.1396        |
-| 154.6842 | 4800 | 0.1028        |
-| 158.0526 | 4900 | 0.0907        |
-| 161.2368 | 5000 | 0.1179        |
-| 164.4211 | 5100 | 0.1496        |
-| 167.6053 | 5200 | 0.1156        |
-| 170.7895 | 5300 | 0.1148        |
-| 174.1579 | 5400 | 0.1275        |
-| 177.3421 | 5500 | 0.1354        |
-| 180.5263 | 5600 | 0.1334        |
-| 183.7105 | 5700 | 0.0874        |
-| 187.0789 | 5800 | 0.0922        |
-| 190.2632 | 5900 | 0.1109        |
-| 193.4474 | 6000 | 0.0708        |
-| 196.6316 | 6100 | 0.0943        |
-| 199.8158 | 6200 | 0.1164        |
-| 203.1842 | 6300 | 0.0785        |
-| 206.3684 | 6400 | 0.0853        |
-| 209.5526 | 6500 | 0.0674        |
-| 212.7368 | 6600 | 0.1009        |
-| 216.1053 | 6700 | 0.0846        |
-| 219.2895 | 6800 | 0.078         |
-| 222.4737 | 6900 | 0.0958        |
-| 225.6579 | 7000 | 0.0811        |
-| 229.0263 | 7100 | 0.0452        |
-| 232.2105 | 7200 | 0.0705        |
-| 235.3947 | 7300 | 0.0664        |
-| 238.5789 | 7400 | 0.0501        |
-| 241.7632 | 7500 | 0.0696        |
-| 245.1316 | 7600 | 0.0736        |
-| 248.3158 | 7700 | 0.08          |
-
-</details>
-
-### Framework Versions
-- Python: 3.11.11
-- Sentence Transformers: 3.4.1
-- Transformers: 4.50.2
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.5.2
-- Datasets: 3.5.0
-- 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",
-}
-```
-
-#### BatchAllTripletLoss
-```bibtex
-@misc{hermans2017defense,
-    title={In Defense of the Triplet Loss for Person Re-Identification},
-    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
-    year={2017},
-    eprint={1703.07737},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
-}
-```
-
-<!--
-## 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.*
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:72
+- loss:BatchAllTripletLoss
+base_model: cl-nagoya/sup-simcse-ja-base
+widget:
+- source_sentence: 打放し型枠（B種）
+  sentences:
+  - 埋込み（B種）（手間）
+  - 埋込み（C種）（手間）
+  - 盛土A種
+- source_sentence: 埋込み[B種]
+  sentences:
+  - 打放し型枠（A種）
+  - 盛土（C種）（手間）
+  - 埋戻し[C種]
+- source_sentence: 盛土[C種]
+  sentences:
+  - 埋込み[C種]
+  - 盛土（A種）
+  - 盛土[A種]
+- source_sentence: 埋戻し[A種]
+  sentences:
+  - 打放し型枠C種
+  - 打放し型枠（C種）（損料・手間）
+  - 盛土[B種]
+- source_sentence: 埋込み（B種）（損料・手間）
+  sentences:
+  - 埋戻し（A種）（損料）
+  - 埋戻し（C種）（損料・手間）
+  - 埋戻し（B種）（手間）
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base). 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:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 -->
+- **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-for-standard-name-v0_9_11")
+# Run inference
+sentences = [
+    '埋込み（B種）（損料・手間）',
+    '埋戻し（A種）（損料）',
+    '埋戻し（B種）（手間）',
+]
+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: 72 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 72 samples:
+  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 16.21 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.50%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>14: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.50%</li><li>17: ~0.50%</li><li>18: ~0.50%</li><li>19: ~0.50%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>29: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.50%</li><li>32: ~0.50%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>35: ~0.50%</li><li>36: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.50%</li><li>42: ~0.50%</li><li>43: ~0.50%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.50%</li><li>49: ~0.50%</li><li>50: ~0.50%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.80%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.50%</li><li>62: ~0.50%</li><li>63: ~0.50%</li><li>64: ~0.50%</li><li>65: ~0.50%</li><li>66: ~0.50%</li><li>67: ~0.50%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.50%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>76: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.50%</li><li>81: ~0.50%</li><li>82: ~0.50%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>86: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.60%</li><li>89: ~0.50%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.50%</li><li>93: ~0.50%</li><li>94: ~0.50%</li><li>95: ~1.20%</li><li>96: ~1.70%</li><li>97: ~3.90%</li><li>98: ~0.50%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.60%</li><li>102: ~0.50%</li><li>103: ~0.50%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~1.20%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: ~0.50%</li><li>111: ~0.50%</li><li>112: ~0.50%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.50%</li><li>119: ~0.50%</li><li>120: ~0.50%</li><li>121: ~0.50%</li><li>122: ~0.50%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.50%</li><li>133: ~0.50%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.50%</li><li>137: ~0.50%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.50%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.50%</li><li>145: ~0.50%</li><li>146: ~0.70%</li><li>147: ~0.50%</li><li>148: ~3.10%</li><li>149: ~0.50%</li><li>150: ~2.30%</li><li>151: ~0.50%</li><li>152: ~0.50%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.50%</li><li>156: ~0.50%</li><li>157: ~0.50%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.50%</li><li>162: ~0.50%</li><li>163: ~0.50%</li><li>164: ~0.50%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.50%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.50%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.50%</li><li>175: ~0.50%</li><li>176: ~0.50%</li><li>177: ~0.10%</li></ul> |
+* Samples:
+  | sentence                                 | 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>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>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>0</code> |
+* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 512
+- `per_device_eval_batch_size`: 512
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 250
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: group_by_label
+
+#### 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`: 512
+- `per_device_eval_batch_size`: 512
+- `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`: 250
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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`: group_by_label
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch    | Step | Training Loss |
+|:--------:|:----:|:-------------:|
+| 10.0     | 10   | 1.6508        |
+| 20.0     | 20   | 1.2554        |
+| 30.0     | 30   | 0.8495        |
+| 40.0     | 40   | 0.7182        |
+| 50.0     | 50   | 0.6614        |
+| 60.0     | 60   | 0.575         |
+| 70.0     | 70   | 0.5027        |
+| 80.0     | 80   | 0.32          |
+| 90.0     | 90   | 0.1543        |
+| 100.0    | 100  | 0.0102        |
+| 110.0    | 110  | 0.012         |
+| 120.0    | 120  | 0.1164        |
+| 130.0    | 130  | 0.0           |
+| 140.0    | 140  | 0.0           |
+| 150.0    | 150  | 0.0           |
+| 160.0    | 160  | 0.0157        |
+| 170.0    | 170  | 0.0794        |
+| 180.0    | 180  | 0.0           |
+| 190.0    | 190  | 0.0           |
+| 200.0    | 200  | 0.0141        |
+| 210.0    | 210  | 0.0           |
+| 220.0    | 220  | 0.0           |
+| 230.0    | 230  | 0.1115        |
+| 240.0    | 240  | 0.0           |
+| 250.0    | 250  | 0.0           |
+| 260.0    | 260  | 0.0           |
+| 270.0    | 270  | 0.0           |
+| 280.0    | 280  | 0.0           |
+| 290.0    | 290  | 0.0           |
+| 300.0    | 300  | 0.0           |
+| 310.0    | 310  | 0.0           |
+| 320.0    | 320  | 0.0           |
+| 330.0    | 330  | 0.0           |
+| 340.0    | 340  | 0.0           |
+| 350.0    | 350  | 0.0           |
+| 360.0    | 360  | 0.0197        |
+| 370.0    | 370  | 0.0649        |
+| 380.0    | 380  | 0.0           |
+| 390.0    | 390  | 0.0           |
+| 400.0    | 400  | 0.0           |
+| 410.0    | 410  | 0.0           |
+| 420.0    | 420  | 0.0           |
+| 430.0    | 430  | 0.0           |
+| 440.0    | 440  | 0.0           |
+| 450.0    | 450  | 0.0           |
+| 460.0    | 460  | 0.0           |
+| 470.0    | 470  | 0.0           |
+| 480.0    | 480  | 0.0           |
+| 490.0    | 490  | 0.0           |
+| 500.0    | 500  | 0.0           |
+| 3.1842   | 100  | 0.6748        |
+| 6.3684   | 200  | 0.5883        |
+| 9.5526   | 300  | 0.5815        |
+| 12.7368  | 400  | 0.5338        |
+| 16.1053  | 500  | 0.5498        |
+| 19.2895  | 600  | 0.5359        |
+| 22.4737  | 700  | 0.5359        |
+| 25.6579  | 800  | 0.4893        |
+| 29.0263  | 900  | 0.4665        |
+| 32.2105  | 1000 | 0.4205        |
+| 35.3947  | 1100 | 0.4383        |
+| 38.5789  | 1200 | 0.4552        |
+| 41.7632  | 1300 | 0.4003        |
+| 45.1316  | 1400 | 0.3816        |
+| 48.3158  | 1500 | 0.3744        |
+| 51.5     | 1600 | 0.3504        |
+| 54.6842  | 1700 | 0.359         |
+| 58.0526  | 1800 | 0.3019        |
+| 61.2368  | 1900 | 0.3109        |
+| 64.4211  | 2000 | 0.3151        |
+| 67.6053  | 2100 | 0.3292        |
+| 70.7895  | 2200 | 0.2813        |
+| 74.1579  | 2300 | 0.2697        |
+| 77.3421  | 2400 | 0.1975        |
+| 80.5263  | 2500 | 0.2492        |
+| 83.7105  | 2600 | 0.2608        |
+| 87.0789  | 2700 | 0.2401        |
+| 90.2632  | 2800 | 0.2265        |
+| 93.4474  | 2900 | 0.2032        |
+| 96.6316  | 3000 | 0.2368        |
+| 99.8158  | 3100 | 0.2066        |
+| 103.1842 | 3200 | 0.1558        |
+| 106.3684 | 3300 | 0.2029        |
+| 109.5526 | 3400 | 0.244         |
+| 112.7368 | 3500 | 0.1894        |
+| 116.1053 | 3600 | 0.193         |
+| 119.2895 | 3700 | 0.1769        |
+| 122.4737 | 3800 | 0.1821        |
+| 125.6579 | 3900 | 0.0912        |
+| 129.0263 | 4000 | 0.1834        |
+| 132.2105 | 4100 | 0.1391        |
+| 135.3947 | 4200 | 0.1718        |
+| 138.5789 | 4300 | 0.1585        |
+| 141.7632 | 4400 | 0.1829        |
+| 145.1316 | 4500 | 0.1246        |
+| 148.3158 | 4600 | 0.1327        |
+| 151.5    | 4700 | 0.1396        |
+| 154.6842 | 4800 | 0.1028        |
+| 158.0526 | 4900 | 0.0907        |
+| 161.2368 | 5000 | 0.1179        |
+| 164.4211 | 5100 | 0.1496        |
+| 167.6053 | 5200 | 0.1156        |
+| 170.7895 | 5300 | 0.1148        |
+| 174.1579 | 5400 | 0.1275        |
+| 177.3421 | 5500 | 0.1354        |
+| 180.5263 | 5600 | 0.1334        |
+| 183.7105 | 5700 | 0.0874        |
+| 187.0789 | 5800 | 0.0922        |
+| 190.2632 | 5900 | 0.1109        |
+| 193.4474 | 6000 | 0.0708        |
+| 196.6316 | 6100 | 0.0943        |
+| 199.8158 | 6200 | 0.1164        |
+| 203.1842 | 6300 | 0.0785        |
+| 206.3684 | 6400 | 0.0853        |
+| 209.5526 | 6500 | 0.0674        |
+| 212.7368 | 6600 | 0.1009        |
+| 216.1053 | 6700 | 0.0846        |
+| 219.2895 | 6800 | 0.078         |
+| 222.4737 | 6900 | 0.0958        |
+| 225.6579 | 7000 | 0.0811        |
+| 229.0263 | 7100 | 0.0452        |
+| 232.2105 | 7200 | 0.0705        |
+| 235.3947 | 7300 | 0.0664        |
+| 238.5789 | 7400 | 0.0501        |
+| 241.7632 | 7500 | 0.0696        |
+| 245.1316 | 7600 | 0.0736        |
+| 248.3158 | 7700 | 0.08          |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- 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",
+}
+```
+
+#### BatchAllTripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification},
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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

@@ -1,24 +1,25 @@
-{
-  "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": "4.50.2",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 32768
-}
+{
+  "_name_or_path": "C:/Project/Detomo/2025/meisai-check/meisai-api/meisaicheck-api/data/model\\Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_11-openvino-quantized",
+  "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": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -1,10 +1,10 @@
-{
-  "__version__": {
-    "sentence_transformers": "3.4.1",
-    "transformers": "4.50.2",
-    "pytorch": "2.6.0+cu124"
-  },
-  "prompts": {},
-  "default_prompt_name": null,
-  "similarity_fn_name": "cosine"
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
 }

modules.json

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

openvino/openvino_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a9c29920a3700c1f950c036804afceda176db9b775116f47e8473a38ab6d390
+size 442470560

sentence_bert_config.json

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

special_tokens_map.json

@@ -1,7 +1,37 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}
+{
+  "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

@@ -1,64 +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
-    },
-    "2": {
-      "content": "[CLS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "[SEP]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "[MASK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "clean_up_tokenization_spaces": true,
-  "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"
-}
+{
+  "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
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "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"
+}