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-nss-v_1_0_3",
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

@@ -1,496 +1,496 @@
----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:11961
-- loss:CustomBatchAllTripletLoss
-widget:
-- source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ（地上部）。
-  sentences:
-  - 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ床床ﾀｲﾙA。
-  - 科目：ユニット及びその他。名称：通用口ｻｲﾝ。
-  - 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：床ﾀｲﾙC。
-  sentences:
-  - 科目：ユニット及びその他。名称：市章･国旗ｻｲﾝ。
-  - 科目：ユニット及びその他。名称：ﾊﾞｯｸﾔｰﾄﾞ室名ｻｲﾝ。
-  - 科目：ユニット及びその他。名称：P-#市章･国旗ｻｲﾝ。
-- source_sentence: 科目：ユニット及びその他。名称：B-#立入禁止ｻｲﾝ。
-  sentences:
-  - 科目：ユニット及びその他。名称：Co-#入口ｻｲﾝ。
-  - 科目：ユニット及びその他。名称：#~#F一般EVﾎｰﾙｶｳﾝﾀｰ。
-  - 科目：ユニット及びその他。名称： MWC､WWC姿見鏡。
-- source_sentence: 科目：ユニット及びその他。名称：#FNICUｶｳﾝﾀｰ。
-  sentences:
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15　粗骨材20AE減水剤遅延型･防水剤入。備考：刊-ｺﾝ
-    3315TB基礎部ﾏｽｺﾝ。
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表    0065。
-  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝24N/mm2   Ｓ＝１５ 徳島１。備考：B1-111111 H2906BD     個別基礎部躯体ｺﾝｸﾘｰﾄ。
-- source_sentence: 科目：ユニット及びその他。名称：HWC荷物棚。
-  sentences:
-  - 科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。
-  - 科目：タイル。名称：階段蹴上ﾀｲﾙP。
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。
-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-v_1_0_3")
-# Run inference
-sentences = [
-    '科目：ユニット及びその他。名称：HWC荷物棚。',
-    '科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。',
-    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。',
-]
-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: 11,961 training samples
-* Columns: <code>sentence</code> and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence                                                                          | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | type    | string                                                                            | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
-  | details | <ul><li>min: 11 tokens</li><li>mean: 18.2 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~1.10%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.40%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.30%</li><li>20: ~0.90%</li><li>21: ~0.30%</li><li>22: ~0.40%</li><li>23: ~0.30%</li><li>24: ~1.10%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.30%</li><li>41: ~0.30%</li><li>42: ~0.40%</li><li>43: ~0.30%</li><li>44: ~0.30%</li><li>45: ~0.30%</li><li>46: ~0.60%</li><li>47: ~0.70%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.30%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.50%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.30%</li><li>70: ~0.30%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.80%</li><li>78: ~0.60%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.30%</li><li>85: ~0.30%</li><li>86: ~0.50%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.80%</li><li>92: ~0.60%</li><li>93: ~0.50%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~16.50%</li><li>97: ~0.30%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.50%</li><li>106: ~0.30%</li><li>107: ~0.30%</li><li>108: ~0.30%</li><li>109: ~0.30%</li><li>110: ~0.30%</li><li>111: ~0.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.70%</li><li>115: ~0.30%</li><li>116: ~0.30%</li><li>117: ~0.30%</li><li>118: ~0.40%</li><li>119: ~2.10%</li><li>120: ~2.10%</li><li>121: ~0.30%</li><li>122: ~0.30%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.40%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.80%</li><li>131: ~0.30%</li><li>132: ~0.30%</li><li>133: ~0.30%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.50%</li><li>144: ~0.30%</li><li>145: ~0.40%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.30%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~0.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.70%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~1.30%</li><li>169: ~0.30%</li><li>170: ~0.40%</li><li>171: ~0.30%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~1.50%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~0.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~0.30%</li><li>182: ~1.60%</li><li>183: ~0.30%</li><li>184: ~0.30%</li><li>185: ~7.20%</li><li>186: ~0.30%</li><li>187: ~1.00%</li><li>188: ~0.30%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~1.80%</li><li>192: ~0.30%</li><li>193: ~0.50%</li><li>194: ~0.70%</li><li>195: ~0.30%</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>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
-
-### 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`: 200
-- `warmup_ratio`: 0.15
-- `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`: 200
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.15
-- `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
-- `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 |
-|:--------:|:----:|:-------------:|
-| 2.3333   | 50   | 0.0589        |
-| 4.6667   | 100  | 0.0668        |
-| 7.125    | 150  | 0.0677        |
-| 9.4583   | 200  | 0.0655        |
-| 11.7917  | 250  | 0.062         |
-| 14.25    | 300  | 0.0601        |
-| 16.5833  | 350  | 0.0604        |
-| 19.0417  | 400  | 0.0602        |
-| 21.375   | 450  | 0.0546        |
-| 23.7083  | 500  | 0.0575        |
-| 26.1667  | 550  | 0.0569        |
-| 28.5     | 600  | 0.0533        |
-| 30.8333  | 650  | 0.0527        |
-| 33.2917  | 700  | 0.0518        |
-| 35.625   | 750  | 0.0487        |
-| 38.0833  | 800  | 0.0514        |
-| 40.4167  | 850  | 0.0469        |
-| 42.75    | 900  | 0.0464        |
-| 45.2083  | 950  | 0.0481        |
-| 47.5417  | 1000 | 0.0502        |
-| 49.875   | 1050 | 0.0511        |
-| 52.3333  | 1100 | 0.0449        |
-| 54.6667  | 1150 | 0.0439        |
-| 57.125   | 1200 | 0.0443        |
-| 59.4583  | 1250 | 0.0445        |
-| 61.7917  | 1300 | 0.0455        |
-| 64.25    | 1350 | 0.0417        |
-| 66.5833  | 1400 | 0.0397        |
-| 69.0417  | 1450 | 0.0392        |
-| 71.375   | 1500 | 0.0411        |
-| 73.7083  | 1550 | 0.0375        |
-| 76.1667  | 1600 | 0.0444        |
-| 78.5     | 1650 | 0.0353        |
-| 80.8333  | 1700 | 0.0402        |
-| 83.2917  | 1750 | 0.0353        |
-| 85.625   | 1800 | 0.0354        |
-| 88.0833  | 1850 | 0.0347        |
-| 90.4167  | 1900 | 0.0368        |
-| 92.75    | 1950 | 0.0353        |
-| 95.2083  | 2000 | 0.0374        |
-| 97.5417  | 2050 | 0.0375        |
-| 99.875   | 2100 | 0.0324        |
-| 1.7576   | 50   | 0.0365        |
-| 3.7576   | 100  | 0.0372        |
-| 5.7576   | 150  | 0.0392        |
-| 7.7576   | 200  | 0.0392        |
-| 9.7576   | 250  | 0.0386        |
-| 11.7576  | 300  | 0.0402        |
-| 13.7576  | 350  | 0.0342        |
-| 15.7576  | 400  | 0.037         |
-| 17.7576  | 450  | 0.0355        |
-| 19.7576  | 500  | 0.0341        |
-| 21.7576  | 550  | 0.0354        |
-| 23.7576  | 600  | 0.0322        |
-| 25.7576  | 650  | 0.0361        |
-| 27.7576  | 700  | 0.0316        |
-| 29.7576  | 750  | 0.0338        |
-| 31.7576  | 800  | 0.0311        |
-| 33.7576  | 850  | 0.0288        |
-| 35.7576  | 900  | 0.0311        |
-| 37.7576  | 950  | 0.0307        |
-| 39.7576  | 1000 | 0.0288        |
-| 41.7576  | 1050 | 0.0324        |
-| 43.7576  | 1100 | 0.0276        |
-| 45.7576  | 1150 | 0.0304        |
-| 47.7576  | 1200 | 0.0267        |
-| 49.7576  | 1250 | 0.0272        |
-| 51.7576  | 1300 | 0.0269        |
-| 53.7576  | 1350 | 0.0264        |
-| 55.7576  | 1400 | 0.0324        |
-| 57.7576  | 1450 | 0.0278        |
-| 59.7576  | 1500 | 0.0315        |
-| 61.7576  | 1550 | 0.0285        |
-| 63.7576  | 1600 | 0.0241        |
-| 65.7576  | 1650 | 0.0288        |
-| 67.7576  | 1700 | 0.0263        |
-| 69.7576  | 1750 | 0.0295        |
-| 71.7576  | 1800 | 0.0238        |
-| 73.7576  | 1850 | 0.0214        |
-| 75.7576  | 1900 | 0.0281        |
-| 77.7576  | 1950 | 0.0269        |
-| 79.7576  | 2000 | 0.0268        |
-| 81.7576  | 2050 | 0.0242        |
-| 83.7576  | 2100 | 0.0226        |
-| 85.7576  | 2150 | 0.0249        |
-| 87.7576  | 2200 | 0.0254        |
-| 89.7576  | 2250 | 0.0226        |
-| 91.7576  | 2300 | 0.0181        |
-| 93.7576  | 2350 | 0.019         |
-| 95.7576  | 2400 | 0.0207        |
-| 97.7576  | 2450 | 0.0205        |
-| 99.7576  | 2500 | 0.0241        |
-| 101.7576 | 2550 | 0.0219        |
-| 103.7576 | 2600 | 0.0237        |
-| 105.7576 | 2650 | 0.0194        |
-| 107.7576 | 2700 | 0.0184        |
-| 109.7576 | 2750 | 0.0206        |
-| 111.7576 | 2800 | 0.0189        |
-| 113.7576 | 2850 | 0.0216        |
-| 115.7576 | 2900 | 0.0234        |
-| 117.7576 | 2950 | 0.0192        |
-| 119.7576 | 3000 | 0.0193        |
-| 121.7576 | 3050 | 0.0211        |
-| 123.7576 | 3100 | 0.0161        |
-| 125.7576 | 3150 | 0.022         |
-| 127.7576 | 3200 | 0.0176        |
-| 129.7576 | 3250 | 0.0227        |
-| 131.7576 | 3300 | 0.0224        |
-| 133.7576 | 3350 | 0.0172        |
-| 135.7576 | 3400 | 0.0168        |
-| 137.7576 | 3450 | 0.0165        |
-| 139.7576 | 3500 | 0.016         |
-| 141.7576 | 3550 | 0.0143        |
-| 143.7576 | 3600 | 0.0165        |
-| 145.7576 | 3650 | 0.0202        |
-| 147.7576 | 3700 | 0.0118        |
-| 149.7576 | 3750 | 0.0163        |
-| 151.7576 | 3800 | 0.0188        |
-| 153.7576 | 3850 | 0.0137        |
-| 155.7576 | 3900 | 0.0172        |
-| 157.7576 | 3950 | 0.0175        |
-| 159.7576 | 4000 | 0.0204        |
-| 161.7576 | 4050 | 0.0175        |
-| 163.7576 | 4100 | 0.0169        |
-| 165.7576 | 4150 | 0.0184        |
-| 167.7576 | 4200 | 0.0176        |
-| 169.7576 | 4250 | 0.0102        |
-| 171.7576 | 4300 | 0.014         |
-| 173.7576 | 4350 | 0.0164        |
-| 175.7576 | 4400 | 0.0203        |
-| 177.7576 | 4450 | 0.0099        |
-| 179.7576 | 4500 | 0.0143        |
-| 181.7576 | 4550 | 0.0182        |
-| 183.7576 | 4600 | 0.009         |
-| 185.7576 | 4650 | 0.0157        |
-| 187.7576 | 4700 | 0.015         |
-| 189.7576 | 4750 | 0.0168        |
-| 191.7576 | 4800 | 0.0172        |
-| 193.7576 | 4850 | 0.0154        |
-| 195.7576 | 4900 | 0.0162        |
-| 197.7576 | 4950 | 0.0143        |
-| 199.7576 | 5000 | 0.0156        |
-
-</details>
-
-### Framework Versions
-- Python: 3.11.12
-- Sentence Transformers: 3.4.1
-- Transformers: 4.51.3
-- 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",
-}
-```
-
-#### CustomBatchAllTripletLoss
-```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:11961
+- loss:CustomBatchAllTripletLoss
+widget:
+- source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ（地上部）。
+  sentences:
+  - 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ床床ﾀｲﾙA。
+  - 科目：ユニット及びその他。名称：通用口ｻｲﾝ。
+  - 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：床ﾀｲﾙC。
+  sentences:
+  - 科目：ユニット及びその他。名称：市章･国旗ｻｲﾝ。
+  - 科目：ユニット及びその他。名称：ﾊﾞｯｸﾔｰﾄﾞ室名ｻｲﾝ。
+  - 科目：ユニット及びその他。名称：P-#市章･国旗ｻｲﾝ。
+- source_sentence: 科目：ユニット及びその他。名称：B-#立入禁止ｻｲﾝ。
+  sentences:
+  - 科目：ユニット及びその他。名称：Co-#入口ｻｲﾝ。
+  - 科目：ユニット及びその他。名称：#~#F一般EVﾎｰﾙｶｳﾝﾀｰ。
+  - 科目：ユニット及びその他。名称： MWC､WWC姿見鏡。
+- source_sentence: 科目：ユニット及びその他。名称：#FNICUｶｳﾝﾀｰ。
+  sentences:
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15　粗骨材20AE減水剤遅延型･防水剤入。備考：刊-ｺﾝ
+    3315TB基礎部ﾏｽｺﾝ。
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表    0065。
+  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝24N/mm2   Ｓ＝１５ 徳島１。備考：B1-111111 H2906BD     個別基礎部躯体ｺﾝｸﾘｰﾄ。
+- source_sentence: 科目：ユニット及びその他。名称：HWC荷物棚。
+  sentences:
+  - 科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。
+  - 科目：タイル。名称：階段蹴上ﾀｲﾙP。
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。
+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-v_1_0_3")
+# Run inference
+sentences = [
+    '科目：ユニット及びその他。名称：HWC荷物棚。',
+    '科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。',
+    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。',
+]
+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: 11,961 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                          | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | type    | string                                                                            | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 18.2 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~1.10%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.40%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.30%</li><li>20: ~0.90%</li><li>21: ~0.30%</li><li>22: ~0.40%</li><li>23: ~0.30%</li><li>24: ~1.10%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.30%</li><li>41: ~0.30%</li><li>42: ~0.40%</li><li>43: ~0.30%</li><li>44: ~0.30%</li><li>45: ~0.30%</li><li>46: ~0.60%</li><li>47: ~0.70%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.30%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.50%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.30%</li><li>70: ~0.30%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.80%</li><li>78: ~0.60%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.30%</li><li>85: ~0.30%</li><li>86: ~0.50%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.80%</li><li>92: ~0.60%</li><li>93: ~0.50%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~16.50%</li><li>97: ~0.30%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.50%</li><li>106: ~0.30%</li><li>107: ~0.30%</li><li>108: ~0.30%</li><li>109: ~0.30%</li><li>110: ~0.30%</li><li>111: ~0.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.70%</li><li>115: ~0.30%</li><li>116: ~0.30%</li><li>117: ~0.30%</li><li>118: ~0.40%</li><li>119: ~2.10%</li><li>120: ~2.10%</li><li>121: ~0.30%</li><li>122: ~0.30%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.40%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.80%</li><li>131: ~0.30%</li><li>132: ~0.30%</li><li>133: ~0.30%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.50%</li><li>144: ~0.30%</li><li>145: ~0.40%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.30%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~0.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.70%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~1.30%</li><li>169: ~0.30%</li><li>170: ~0.40%</li><li>171: ~0.30%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~1.50%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~0.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~0.30%</li><li>182: ~1.60%</li><li>183: ~0.30%</li><li>184: ~0.30%</li><li>185: ~7.20%</li><li>186: ~0.30%</li><li>187: ~1.00%</li><li>188: ~0.30%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~1.80%</li><li>192: ~0.30%</li><li>193: ~0.50%</li><li>194: ~0.70%</li><li>195: ~0.30%</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>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
+
+### 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`: 200
+- `warmup_ratio`: 0.15
+- `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`: 200
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.15
+- `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
+- `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 |
+|:--------:|:----:|:-------------:|
+| 2.3333   | 50   | 0.0589        |
+| 4.6667   | 100  | 0.0668        |
+| 7.125    | 150  | 0.0677        |
+| 9.4583   | 200  | 0.0655        |
+| 11.7917  | 250  | 0.062         |
+| 14.25    | 300  | 0.0601        |
+| 16.5833  | 350  | 0.0604        |
+| 19.0417  | 400  | 0.0602        |
+| 21.375   | 450  | 0.0546        |
+| 23.7083  | 500  | 0.0575        |
+| 26.1667  | 550  | 0.0569        |
+| 28.5     | 600  | 0.0533        |
+| 30.8333  | 650  | 0.0527        |
+| 33.2917  | 700  | 0.0518        |
+| 35.625   | 750  | 0.0487        |
+| 38.0833  | 800  | 0.0514        |
+| 40.4167  | 850  | 0.0469        |
+| 42.75    | 900  | 0.0464        |
+| 45.2083  | 950  | 0.0481        |
+| 47.5417  | 1000 | 0.0502        |
+| 49.875   | 1050 | 0.0511        |
+| 52.3333  | 1100 | 0.0449        |
+| 54.6667  | 1150 | 0.0439        |
+| 57.125   | 1200 | 0.0443        |
+| 59.4583  | 1250 | 0.0445        |
+| 61.7917  | 1300 | 0.0455        |
+| 64.25    | 1350 | 0.0417        |
+| 66.5833  | 1400 | 0.0397        |
+| 69.0417  | 1450 | 0.0392        |
+| 71.375   | 1500 | 0.0411        |
+| 73.7083  | 1550 | 0.0375        |
+| 76.1667  | 1600 | 0.0444        |
+| 78.5     | 1650 | 0.0353        |
+| 80.8333  | 1700 | 0.0402        |
+| 83.2917  | 1750 | 0.0353        |
+| 85.625   | 1800 | 0.0354        |
+| 88.0833  | 1850 | 0.0347        |
+| 90.4167  | 1900 | 0.0368        |
+| 92.75    | 1950 | 0.0353        |
+| 95.2083  | 2000 | 0.0374        |
+| 97.5417  | 2050 | 0.0375        |
+| 99.875   | 2100 | 0.0324        |
+| 1.7576   | 50   | 0.0365        |
+| 3.7576   | 100  | 0.0372        |
+| 5.7576   | 150  | 0.0392        |
+| 7.7576   | 200  | 0.0392        |
+| 9.7576   | 250  | 0.0386        |
+| 11.7576  | 300  | 0.0402        |
+| 13.7576  | 350  | 0.0342        |
+| 15.7576  | 400  | 0.037         |
+| 17.7576  | 450  | 0.0355        |
+| 19.7576  | 500  | 0.0341        |
+| 21.7576  | 550  | 0.0354        |
+| 23.7576  | 600  | 0.0322        |
+| 25.7576  | 650  | 0.0361        |
+| 27.7576  | 700  | 0.0316        |
+| 29.7576  | 750  | 0.0338        |
+| 31.7576  | 800  | 0.0311        |
+| 33.7576  | 850  | 0.0288        |
+| 35.7576  | 900  | 0.0311        |
+| 37.7576  | 950  | 0.0307        |
+| 39.7576  | 1000 | 0.0288        |
+| 41.7576  | 1050 | 0.0324        |
+| 43.7576  | 1100 | 0.0276        |
+| 45.7576  | 1150 | 0.0304        |
+| 47.7576  | 1200 | 0.0267        |
+| 49.7576  | 1250 | 0.0272        |
+| 51.7576  | 1300 | 0.0269        |
+| 53.7576  | 1350 | 0.0264        |
+| 55.7576  | 1400 | 0.0324        |
+| 57.7576  | 1450 | 0.0278        |
+| 59.7576  | 1500 | 0.0315        |
+| 61.7576  | 1550 | 0.0285        |
+| 63.7576  | 1600 | 0.0241        |
+| 65.7576  | 1650 | 0.0288        |
+| 67.7576  | 1700 | 0.0263        |
+| 69.7576  | 1750 | 0.0295        |
+| 71.7576  | 1800 | 0.0238        |
+| 73.7576  | 1850 | 0.0214        |
+| 75.7576  | 1900 | 0.0281        |
+| 77.7576  | 1950 | 0.0269        |
+| 79.7576  | 2000 | 0.0268        |
+| 81.7576  | 2050 | 0.0242        |
+| 83.7576  | 2100 | 0.0226        |
+| 85.7576  | 2150 | 0.0249        |
+| 87.7576  | 2200 | 0.0254        |
+| 89.7576  | 2250 | 0.0226        |
+| 91.7576  | 2300 | 0.0181        |
+| 93.7576  | 2350 | 0.019         |
+| 95.7576  | 2400 | 0.0207        |
+| 97.7576  | 2450 | 0.0205        |
+| 99.7576  | 2500 | 0.0241        |
+| 101.7576 | 2550 | 0.0219        |
+| 103.7576 | 2600 | 0.0237        |
+| 105.7576 | 2650 | 0.0194        |
+| 107.7576 | 2700 | 0.0184        |
+| 109.7576 | 2750 | 0.0206        |
+| 111.7576 | 2800 | 0.0189        |
+| 113.7576 | 2850 | 0.0216        |
+| 115.7576 | 2900 | 0.0234        |
+| 117.7576 | 2950 | 0.0192        |
+| 119.7576 | 3000 | 0.0193        |
+| 121.7576 | 3050 | 0.0211        |
+| 123.7576 | 3100 | 0.0161        |
+| 125.7576 | 3150 | 0.022         |
+| 127.7576 | 3200 | 0.0176        |
+| 129.7576 | 3250 | 0.0227        |
+| 131.7576 | 3300 | 0.0224        |
+| 133.7576 | 3350 | 0.0172        |
+| 135.7576 | 3400 | 0.0168        |
+| 137.7576 | 3450 | 0.0165        |
+| 139.7576 | 3500 | 0.016         |
+| 141.7576 | 3550 | 0.0143        |
+| 143.7576 | 3600 | 0.0165        |
+| 145.7576 | 3650 | 0.0202        |
+| 147.7576 | 3700 | 0.0118        |
+| 149.7576 | 3750 | 0.0163        |
+| 151.7576 | 3800 | 0.0188        |
+| 153.7576 | 3850 | 0.0137        |
+| 155.7576 | 3900 | 0.0172        |
+| 157.7576 | 3950 | 0.0175        |
+| 159.7576 | 4000 | 0.0204        |
+| 161.7576 | 4050 | 0.0175        |
+| 163.7576 | 4100 | 0.0169        |
+| 165.7576 | 4150 | 0.0184        |
+| 167.7576 | 4200 | 0.0176        |
+| 169.7576 | 4250 | 0.0102        |
+| 171.7576 | 4300 | 0.014         |
+| 173.7576 | 4350 | 0.0164        |
+| 175.7576 | 4400 | 0.0203        |
+| 177.7576 | 4450 | 0.0099        |
+| 179.7576 | 4500 | 0.0143        |
+| 181.7576 | 4550 | 0.0182        |
+| 183.7576 | 4600 | 0.009         |
+| 185.7576 | 4650 | 0.0157        |
+| 187.7576 | 4700 | 0.015         |
+| 189.7576 | 4750 | 0.0168        |
+| 191.7576 | 4800 | 0.0172        |
+| 193.7576 | 4850 | 0.0154        |
+| 195.7576 | 4900 | 0.0162        |
+| 197.7576 | 4950 | 0.0143        |
+| 199.7576 | 5000 | 0.0156        |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- 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",
+}
+```
+
+#### CustomBatchAllTripletLoss
+```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": "4.51.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 32768
-}
+{
+  "_name_or_path": "Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_3",
+  "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": "4.51.3",
-    "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:da10d8a2e0d55b27be9d900701584dd6ed9d8445a3d06e9471716ab0291077ea
+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

@@ -1,37 +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
-  }
-}
+{
+  "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"
+}