Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1546
+- loss:DualMarginContrastiveLoss
+- loss:CustomBatchAllTripletLoss
+widget:
+- source_sentence: 科目：塗装。名称：CL塗り。
+  sentences:
+  - 科目：建具。名称：SKW-#窓+扉。
+  - 科目：塗装。名称：VP塗り。
+  - 科目：建具。名称：SSD-#窓+扉。
+- source_sentence: 科目：塗装。名称：EP塗り。
+  sentences:
+  - 科目：建具。名称：HAW-#窓。
+  - 科目：建具。名称：SLW-#間仕切。
+  - 科目：塗装。名称：OS塗り。
+- source_sentence: 科目：塗装。名称：FSP塗り。
+  sentences:
+  - 科目：建具。名称：SP-#間仕切。
+  - 科目：建具。名称：XD-#扉。
+  - 科目：塗装。名称：WP塗り。
+- source_sentence: 科目：建具。名称：ACW-#窓。
+  sentences:
+  - 科目：建具。名称：GD-#窓+扉。
+  - 科目：建具。名称：GD-#用窓。
+  - 科目：建具。名称：WAW-#扉。
+- source_sentence: 科目：建具。名称：GCW-#窓。
+  sentences:
+  - 科目：建具。名称：STW-#窓。
+  - 科目：建具。名称：TDW-#窓+扉。
+  - 科目：建具。名称：AW-#窓。
+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-v0_9_13")
+# Run inference
+sentences = [
+    '科目：建具。名称：GCW-#窓。',
+    '科目：建具。名称：AW-#窓。',
+    '科目：建具。名称：STW-#窓。',
+]
+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: 1,546 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: 17.07 tokens</li><li>max: 27 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: ~0.30%</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: ~0.40%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.50%</li><li>19: ~0.30%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~0.30%</li><li>23: ~0.30%</li><li>24: ~0.30%</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: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</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: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.50%</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: ~0.60%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.30%</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: ~0.80%</li><li>87: ~0.60%</li><li>88: ~0.50%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.60%</li><li>92: ~8.00%</li><li>93: ~1.70%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~0.60%</li><li>97: ~0.30%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~1.20%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.30%</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: ~0.30%</li><li>115: ~0.30%</li><li>116: ~0.30%</li><li>117: ~0.30%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.50%</li><li>122: ~0.30%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.30%</li><li>126: ~0.30%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.40%</li><li>130: ~0.70%</li><li>131: ~0.30%</li><li>132: ~3.10%</li><li>133: ~0.30%</li><li>134: ~2.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.50%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.80%</li><li>145: ~0.30%</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: ~0.30%</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: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.60%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~0.70%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~1.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~0.30%</li><li>182: ~0.30%</li><li>183: ~0.30%</li><li>184: ~0.30%</li><li>185: ~1.10%</li><li>186: ~0.30%</li><li>187: ~0.30%</li><li>188: ~0.30%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~0.30%</li><li>192: ~0.30%</li><li>193: ~0.30%</li><li>194: ~1.50%</li><li>195: ~0.30%</li><li>196: ~0.30%</li><li>197: ~0.30%</li><li>198: ~0.30%</li><li>199: ~1.00%</li><li>200: ~0.30%</li><li>201: ~0.30%</li><li>202: ~0.30%</li><li>203: ~1.80%</li><li>204: ~0.30%</li><li>205: ~0.50%</li><li>206: ~0.70%</li><li>207: ~0.30%</li><li>208: ~0.30%</li><li>209: ~0.30%</li><li>210: ~0.30%</li><li>211: ~0.30%</li><li>212: ~0.30%</li><li>213: ~0.30%</li><li>214: ~0.30%</li><li>215: ~4.00%</li><li>216: ~0.30%</li><li>217: ~0.30%</li><li>218: ~0.30%</li><li>219: ~0.60%</li><li>220: ~0.30%</li><li>221: ~0.30%</li><li>222: ~0.70%</li><li>223: ~0.30%</li><li>224: ~0.30%</li><li>225: ~0.30%</li><li>226: ~0.60%</li><li>227: ~0.30%</li><li>228: ~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>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`: 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
+| Epoch    | Step | Training Loss |
+|:--------:|:----:|:-------------:|
+| 2.5      | 10   | 34.4458       |
+| 5.0      | 20   | 9.5341        |
+| 7.5      | 30   | 2.0511        |
+| 10.0     | 40   | 1.5025        |
+| 12.5     | 50   | 1.4347        |
+| 15.0     | 60   | 1.1549        |
+| 17.5     | 70   | 1.2308        |
+| 20.0     | 80   | 1.0908        |
+| 22.5     | 90   | 1.1238        |
+| 25.0     | 100  | 0.9793        |
+| 2.5      | 10   | 1.1269        |
+| 5.0      | 20   | 0.8895        |
+| 7.5      | 30   | 0.8496        |
+| 10.0     | 40   | 0.6124        |
+| 12.5     | 50   | 0.5591        |
+| 15.0     | 60   | 0.4262        |
+| 17.5     | 70   | 0.3892        |
+| 20.0     | 80   | 0.3309        |
+| 22.5     | 90   | 0.3195        |
+| 25.0     | 100  | 0.0781        |
+| 7.5455   | 200  | 0.072         |
+| 11.4242  | 300  | 0.073         |
+| 15.3030  | 400  | 0.0715        |
+| 19.1818  | 500  | 0.069         |
+| 23.0606  | 600  | 0.0682        |
+| 26.7273  | 700  | 0.0659        |
+| 30.6061  | 800  | 0.0628        |
+| 34.4848  | 900  | 0.0618        |
+| 38.3636  | 1000 | 0.0639        |
+| 42.2424  | 1100 | 0.0635        |
+| 46.1212  | 1200 | 0.0635        |
+| 49.7879  | 1300 | 0.0627        |
+| 53.6667  | 1400 | 0.0593        |
+| 57.5455  | 1500 | 0.0605        |
+| 61.4242  | 1600 | 0.055         |
+| 65.3030  | 1700 | 0.0556        |
+| 69.1818  | 1800 | 0.0589        |
+| 73.0606  | 1900 | 0.0585        |
+| 76.7273  | 2000 | 0.0568        |
+| 80.6061  | 2100 | 0.0521        |
+| 84.4848  | 2200 | 0.0559        |
+| 88.3636  | 2300 | 0.0508        |
+| 92.2424  | 2400 | 0.051         |
+| 96.1212  | 2500 | 0.0532        |
+| 99.7879  | 2600 | 0.0545        |
+| 103.6667 | 2700 | 0.0532        |
+| 107.5455 | 2800 | 0.0542        |
+| 111.4242 | 2900 | 0.052         |
+| 115.3030 | 3000 | 0.0497        |
+| 119.1818 | 3100 | 0.0486        |
+| 123.0606 | 3200 | 0.0562        |
+| 126.7273 | 3300 | 0.0544        |
+| 130.6061 | 3400 | 0.0516        |
+| 134.4848 | 3500 | 0.0491        |
+| 138.3636 | 3600 | 0.0578        |
+| 142.2424 | 3700 | 0.0508        |
+| 146.1212 | 3800 | 0.0533        |
+| 149.7879 | 3900 | 0.0487        |
+| 153.6667 | 4000 | 0.045         |
+| 157.5455 | 4100 | 0.0454        |
+| 161.4242 | 4200 | 0.0497        |
+| 165.3030 | 4300 | 0.0466        |
+| 169.1818 | 4400 | 0.045         |
+| 173.0606 | 4500 | 0.0477        |
+| 176.7273 | 4600 | 0.0421        |
+| 180.6061 | 4700 | 0.051         |
+| 184.4848 | 4800 | 0.0389        |
+| 188.3636 | 4900 | 0.0449        |
+| 192.2424 | 5000 | 0.0425        |
+| 196.1212 | 5100 | 0.0456        |
+| 199.7879 | 5200 | 0.0465        |
+| 203.6667 | 5300 | 0.0435        |
+| 207.5455 | 5400 | 0.04          |
+| 211.4242 | 5500 | 0.0405        |
+| 215.3030 | 5600 | 0.0432        |
+| 219.1818 | 5700 | 0.0394        |
+| 223.0606 | 5800 | 0.0511        |
+| 226.7273 | 5900 | 0.0462        |
+| 230.6061 | 6000 | 0.0397        |
+| 234.4848 | 6100 | 0.0413        |
+| 238.3636 | 6200 | 0.0443        |
+| 242.2424 | 6300 | 0.0377        |
+| 246.1212 | 6400 | 0.0437        |
+| 249.7879 | 6500 | 0.0407        |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.3
+- 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

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43c93fe5e0261a8bb3727391a9a2f164d0043456121a1578d09d213cc4741ada
+size 444851048

modules.json

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

sentence_bert_config.json

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

special_tokens_map.json

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

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "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"
+}