Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "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:9054
+- loss:BatchAllTripletLoss
+base_model: cl-nagoya/sup-simcse-ja-base
+widget:
+- source_sentence: 科目:ﾕﾆｯﾄ及びその他｡名称:⑮傍聴席椅子#人掛け｡
+  sentences:
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:#階副議長室ﾛｰﾙｽｸﾘｰﾝ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:控室B#-#､B#-#ｶｳﾝﾀｰ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:記載ｶｳﾝﾀｰｻｲﾝ｡
+- source_sentence: 科目:ｺﾝｸﾘｰﾄ｡名称:多目的ﾎｰﾙ機械式移動座席基礎ｺﾝｸﾘｰﾄ｡
+  sentences:
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:床ｱｽﾌｧﾙﾄ舗装｡
+  - 科目:ﾀｲﾙ｡名称:立上りﾀｲﾙA｡
+  - 科目:ﾀｲﾙ｡名称:段床磁器質ﾀｲﾙ｡
+- source_sentence: 科目:ﾀｲﾙ｡名称:ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ張り｡
+  sentences:
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:ｶｰﾃﾝﾎﾞｯｸｽD｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:総合案内ｶｳﾝﾀｰ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:天井ｶｰﾃﾝﾚｰﾙ｡
+- source_sentence: 科目:ﾀｲﾙ｡名称:屋外階段踊場ﾀｲﾙ張り｡
+  sentences:
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:執務室#-#規格品ｶｳﾝﾀｰ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:受付ﾛﾋﾞｰｶｳﾝﾀｰ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:Sc-#踊場階数表示ｻｲﾝ｡
+- source_sentence: 科目:ﾀｲﾙ｡名称:床磁器質ﾀｲﾙ｡
+  sentences:
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:La-#開閉注意ｻｲﾝ｡
+  - 科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_6")
+# Run inference
+sentences = [
+    '科目:ﾀｲﾙ｡名称:床磁器質ﾀｲﾙ｡',
+    '科目:ﾕﾆｯﾄ及びその他｡名称:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡',
+    '科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡',
+]
+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: 9,054 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    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+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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+  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 17.79 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~0.20%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.20%</li><li>5: ~0.20%</li><li>6: ~0.20%</li><li>7: ~0.20%</li><li>8: ~0.20%</li><li>9: ~0.20%</li><li>10: ~0.30%</li><li>11: ~0.20%</li><li>12: ~0.20%</li><li>13: ~0.20%</li><li>14: ~0.20%</li><li>15: ~0.20%</li><li>16: ~0.20%</li><li>17: ~0.40%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.20%</li><li>21: ~0.20%</li><li>22: ~0.20%</li><li>23: ~0.20%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.20%</li><li>27: ~0.20%</li><li>28: ~0.20%</li><li>29: ~0.20%</li><li>30: ~0.20%</li><li>31: ~0.20%</li><li>32: ~0.20%</li><li>33: ~0.20%</li><li>34: ~0.20%</li><li>35: ~0.20%</li><li>36: ~0.20%</li><li>37: ~0.20%</li><li>38: ~0.20%</li><li>39: ~0.20%</li><li>40: ~0.20%</li><li>41: ~0.20%</li><li>42: ~0.20%</li><li>43: ~0.60%</li><li>44: ~0.70%</li><li>45: ~0.20%</li><li>46: ~0.20%</li><li>47: ~0.20%</li><li>48: ~0.20%</li><li>49: ~0.20%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: ~0.20%</li><li>54: ~0.20%</li><li>55: ~0.30%</li><li>56: ~0.40%</li><li>57: ~0.30%</li><li>58: ~0.20%</li><li>59: ~0.20%</li><li>60: ~0.20%</li><li>61: ~0.20%</li><li>62: ~0.20%</li><li>63: ~0.30%</li><li>64: ~0.20%</li><li>65: ~0.20%</li><li>66: ~0.20%</li><li>67: ~0.20%</li><li>68: ~0.40%</li><li>69: ~0.40%</li><li>70: ~0.20%</li><li>71: ~0.60%</li><li>72: ~0.20%</li><li>73: ~0.20%</li><li>74: ~0.20%</li><li>75: ~0.20%</li><li>76: ~0.20%</li><li>77: ~0.30%</li><li>78: ~0.20%</li><li>79: ~0.40%</li><li>80: ~0.20%</li><li>81: ~0.20%</li><li>82: ~0.50%</li><li>83: ~0.30%</li><li>84: ~0.60%</li><li>85: ~0.20%</li><li>86: ~0.30%</li><li>87: ~0.20%</li><li>88: ~0.20%</li><li>89: ~0.20%</li><li>90: ~0.20%</li><li>91: ~1.10%</li><li>92: ~1.70%</li><li>93: ~2.20%</li><li>94: ~0.50%</li><li>95: ~0.20%</li><li>96: ~0.20%</li><li>97: ~1.50%</li><li>98: ~0.20%</li><li>99: ~0.20%</li><li>100: ~0.20%</li><li>101: ~0.20%</li><li>102: ~0.30%</li><li>103: ~1.70%</li><li>104: ~0.20%</li><li>105: ~0.20%</li><li>106: ~0.40%</li><li>107: ~0.40%</li><li>108: ~0.20%</li><li>109: ~0.20%</li><li>110: ~0.20%</li><li>111: ~1.10%</li><li>112: ~0.20%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.20%</li><li>116: ~0.20%</li><li>117: ~0.20%</li><li>118: ~0.20%</li><li>119: ~0.50%</li><li>120: ~0.20%</li><li>121: ~0.20%</li><li>122: ~0.20%</li><li>123: ~0.20%</li><li>124: ~0.20%</li><li>125: ~0.20%</li><li>126: ~0.30%</li><li>127: ~0.20%</li><li>128: ~0.20%</li><li>129: ~0.20%</li><li>130: ~0.50%</li><li>131: ~0.20%</li><li>132: ~0.20%</li><li>133: ~0.20%</li><li>134: ~0.20%</li><li>135: ~0.20%</li><li>136: ~0.20%</li><li>137: ~0.20%</li><li>138: ~0.20%</li><li>139: ~0.20%</li><li>140: ~0.20%</li><li>141: ~1.80%</li><li>142: ~0.20%</li><li>143: ~0.20%</li><li>144: ~1.70%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.50%</li><li>148: ~0.50%</li><li>149: ~0.50%</li><li>150: ~0.20%</li><li>151: ~0.20%</li><li>152: ~0.20%</li><li>153: ~0.20%</li><li>154: ~0.20%</li><li>155: ~0.20%</li><li>156: ~0.20%</li><li>157: ~0.20%</li><li>158: ~0.60%</li><li>159: ~0.20%</li><li>160: ~0.20%</li><li>161: ~0.20%</li><li>162: ~0.20%</li><li>163: ~0.20%</li><li>164: ~0.50%</li><li>165: ~0.20%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>169: ~0.20%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~0.20%</li><li>173: ~0.20%</li><li>174: ~0.20%</li><li>175: ~0.20%</li><li>176: ~0.20%</li><li>177: ~0.60%</li><li>178: ~0.20%</li><li>179: ~0.20%</li><li>180: ~0.20%</li><li>181: ~0.20%</li><li>182: ~0.20%</li><li>183: ~0.40%</li><li>184: ~0.20%</li><li>185: ~0.20%</li><li>186: ~0.30%</li><li>187: ~0.20%</li><li>188: ~0.90%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~0.20%</li><li>192: ~0.30%</li><li>193: ~0.20%</li><li>194: ~0.80%</li><li>195: ~0.20%</li><li>196: ~0.20%</li><li>197: ~0.30%</li><li>198: ~0.20%</li><li>199: ~0.20%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.20%</li><li>203: ~1.20%</li><li>204: ~0.40%</li><li>205: ~0.20%</li><li>206: ~0.20%</li><li>207: ~0.20%</li><li>208: ~0.20%</li><li>209: ~1.00%</li><li>210: ~0.20%</li><li>211: ~0.30%</li><li>212: ~0.20%</li><li>213: ~1.10%</li><li>214: ~0.30%</li><li>215: ~0.20%</li><li>216: ~0.20%</li><li>217: ~0.20%</li><li>218: ~0.20%</li><li>219: ~0.20%</li><li>220: ~0.20%</li><li>221: ~0.20%</li><li>222: ~0.30%</li><li>223: ~0.20%</li><li>224: ~0.90%</li><li>225: ~4.70%</li><li>226: ~0.20%</li><li>227: ~0.20%</li><li>228: ~0.20%</li><li>229: ~0.70%</li><li>230: ~0.20%</li><li>231: ~0.80%</li><li>232: ~0.20%</li><li>233: ~0.40%</li><li>234: ~0.30%</li><li>235: ~0.40%</li><li>236: ~0.20%</li><li>237: ~0.30%</li><li>238: ~0.50%</li><li>239: ~0.30%</li><li>240: ~0.20%</li><li>241: ~0.20%</li><li>242: ~0.30%</li><li>243: ~0.30%</li><li>244: ~0.30%</li><li>245: ~0.60%</li><li>246: ~0.20%</li><li>247: ~0.20%</li><li>248: ~0.20%</li><li>249: ~0.30%</li><li>250: ~0.30%</li><li>251: ~1.90%</li><li>252: ~0.20%</li><li>253: ~0.20%</li><li>254: ~0.20%</li><li>255: ~0.20%</li><li>256: ~0.20%</li><li>257: ~0.50%</li><li>258: ~0.20%</li><li>259: ~0.30%</li><li>260: ~0.20%</li><li>261: ~0.20%</li><li>262: ~1.00%</li><li>263: ~0.20%</li><li>264: ~0.20%</li><li>265: ~0.20%</li><li>266: ~0.40%</li><li>267: ~0.20%</li><li>268: ~0.20%</li><li>269: ~0.20%</li><li>270: ~0.20%</li><li>271: ~0.20%</li><li>272: ~0.20%</li><li>273: ~0.20%</li><li>274: ~3.60%</li><li>275: ~0.20%</li><li>276: ~0.20%</li><li>277: ~0.40%</li><li>278: ~0.20%</li><li>279: ~0.20%</li><li>280: ~0.90%</li><li>281: ~0.40%</li><li>282: ~0.20%</li><li>283: ~2.30%</li><li>284: ~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:ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送｡</code> | <code>1</code> |
+* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 512
+- `per_device_eval_batch_size`: 512
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 500
+- `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`: 500
+- `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}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: group_by_label
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch    | Step | Training Loss |
+|:--------:|:----:|:-------------:|
+| 2.8889   | 50   | 0.7963        |
+| 5.8333   | 100  | 0.7067        |
+| 8.7778   | 150  | 0.6532        |
+| 11.7222  | 200  | 0.6806        |
+| 14.6667  | 250  | 0.652         |
+| 17.6111  | 300  | 0.6508        |
+| 20.5556  | 350  | 0.6566        |
+| 23.5     | 400  | 0.6237        |
+| 26.4444  | 450  | 0.6363        |
+| 29.3889  | 500  | 0.6554        |
+| 32.3333  | 550  | 0.6007        |
+| 35.2778  | 600  | 0.6016        |
+| 38.2222  | 650  | 0.5687        |
+| 2.8889   | 50   | 0.5655        |
+| 5.8333   | 100  | 0.6139        |
+| 8.7778   | 150  | 0.514         |
+| 11.7222  | 200  | 0.5867        |
+| 14.6667  | 250  | 0.5699        |
+| 17.6111  | 300  | 0.5472        |
+| 20.5556  | 350  | 0.5793        |
+| 23.5     | 400  | 0.5196        |
+| 26.4444  | 450  | 0.5572        |
+| 29.3889  | 500  | 0.5279        |
+| 32.3333  | 550  | 0.5095        |
+| 35.2778  | 600  | 0.4488        |
+| 38.2222  | 650  | 0.4189        |
+| 41.1667  | 700  | 0.5164        |
+| 44.1111  | 750  | 0.591         |
+| 47.0556  | 800  | 0.52          |
+| 49.9444  | 850  | 0.5235        |
+| 52.8889  | 900  | 0.5317        |
+| 55.8333  | 950  | 0.5517        |
+| 58.7778  | 1000 | 0.5618        |
+| 61.7222  | 1050 | 0.5318        |
+| 64.6667  | 1100 | 0.4685        |
+| 67.6111  | 1150 | 0.4836        |
+| 70.5556  | 1200 | 0.5426        |
+| 73.5     | 1250 | 0.5356        |
+| 76.4444  | 1300 | 0.4231        |
+| 79.3889  | 1350 | 0.5104        |
+| 82.3333  | 1400 | 0.4944        |
+| 85.2778  | 1450 | 0.5301        |
+| 88.2222  | 1500 | 0.4499        |
+| 91.1667  | 1550 | 0.4745        |
+| 94.1111  | 1600 | 0.4432        |
+| 97.0556  | 1650 | 0.3892        |
+| 99.9444  | 1700 | 0.4429        |
+| 102.8889 | 1750 | 0.4973        |
+| 105.8333 | 1800 | 0.5222        |
+| 108.7778 | 1850 | 0.4502        |
+| 111.7222 | 1900 | 0.4073        |
+| 114.6667 | 1950 | 0.408         |
+| 117.6111 | 2000 | 0.403         |
+| 120.5556 | 2050 | 0.4122        |
+| 123.5    | 2100 | 0.4357        |
+| 126.4444 | 2150 | 0.4765        |
+| 129.3889 | 2200 | 0.4069        |
+| 132.3333 | 2250 | 0.388         |
+| 135.2778 | 2300 | 0.341         |
+| 138.2222 | 2350 | 0.333         |
+| 141.1667 | 2400 | 0.4587        |
+| 144.1111 | 2450 | 0.355         |
+| 147.0556 | 2500 | 0.3552        |
+| 149.9444 | 2550 | 0.3804        |
+| 152.8889 | 2600 | 0.3692        |
+| 155.8333 | 2650 | 0.3367        |
+| 158.7778 | 2700 | 0.3662        |
+| 161.7222 | 2750 | 0.3089        |
+| 164.6667 | 2800 | 0.3016        |
+| 167.6111 | 2850 | 0.3252        |
+| 170.5556 | 2900 | 0.3409        |
+| 173.5    | 2950 | 0.3128        |
+| 176.4444 | 3000 | 0.3287        |
+| 179.3889 | 3050 | 0.3148        |
+| 182.3333 | 3100 | 0.3843        |
+| 185.2778 | 3150 | 0.2281        |
+| 188.2222 | 3200 | 0.2973        |
+| 191.1667 | 3250 | 0.2891        |
+| 194.1111 | 3300 | 0.3623        |
+| 197.0556 | 3350 | 0.3626        |
+| 199.9444 | 3400 | 0.2931        |
+| 202.8889 | 3450 | 0.2755        |
+| 205.8333 | 3500 | 0.2849        |
+| 208.7778 | 3550 | 0.2608        |
+| 211.7222 | 3600 | 0.3081        |
+| 214.6667 | 3650 | 0.2724        |
+| 217.6111 | 3700 | 0.2583        |
+| 220.5556 | 3750 | 0.3132        |
+| 223.5    | 3800 | 0.196         |
+| 226.4444 | 3850 | 0.2554        |
+| 229.3889 | 3900 | 0.2           |
+| 232.3333 | 3950 | 0.2936        |
+| 235.2778 | 4000 | 0.2326        |
+| 238.2222 | 4050 | 0.2031        |
+| 241.1667 | 4100 | 0.2492        |
+| 244.1111 | 4150 | 0.2234        |
+| 247.0556 | 4200 | 0.3034        |
+| 249.9444 | 4250 | 0.2325        |
+| 252.8889 | 4300 | 0.2453        |
+| 255.8333 | 4350 | 0.2848        |
+| 258.7778 | 4400 | 0.2447        |
+| 261.7222 | 4450 | 0.2599        |
+| 264.6667 | 4500 | 0.2073        |
+| 267.6111 | 4550 | 0.2134        |
+| 270.5556 | 4600 | 0.1886        |
+| 273.5    | 4650 | 0.1229        |
+| 276.4444 | 4700 | 0.2147        |
+| 279.3889 | 4750 | 0.1993        |
+| 282.3333 | 4800 | 0.1814        |
+| 285.2778 | 4850 | 0.202         |
+| 288.2222 | 4900 | 0.1947        |
+| 291.1667 | 4950 | 0.14          |
+| 294.1111 | 5000 | 0.2394        |
+| 297.0556 | 5050 | 0.1798        |
+| 299.9444 | 5100 | 0.1534        |
+| 302.8889 | 5150 | 0.2622        |
+| 305.8333 | 5200 | 0.1636        |
+| 308.7778 | 5250 | 0.1966        |
+| 311.7222 | 5300 | 0.1365        |
+| 314.6667 | 5350 | 0.1501        |
+| 317.6111 | 5400 | 0.1494        |
+| 320.5556 | 5450 | 0.1341        |
+| 323.5    | 5500 | 0.1791        |
+| 326.4444 | 5550 | 0.1609        |
+| 329.3889 | 5600 | 0.2268        |
+| 332.3333 | 5650 | 0.2145        |
+| 335.2778 | 5700 | 0.095         |
+| 338.2222 | 5750 | 0.1161        |
+| 341.1667 | 5800 | 0.1615        |
+| 344.1111 | 5850 | 0.1261        |
+| 347.0556 | 5900 | 0.2022        |
+| 349.9444 | 5950 | 0.1503        |
+| 352.8889 | 6000 | 0.1473        |
+| 355.8333 | 6050 | 0.1703        |
+| 358.7778 | 6100 | 0.1441        |
+| 361.7222 | 6150 | 0.1439        |
+| 364.6667 | 6200 | 0.1192        |
+| 367.6111 | 6250 | 0.1312        |
+| 370.5556 | 6300 | 0.0933        |
+| 373.5    | 6350 | 0.1281        |
+| 376.4444 | 6400 | 0.1516        |
+| 379.3889 | 6450 | 0.1819        |
+| 382.3333 | 6500 | 0.1877        |
+| 385.2778 | 6550 | 0.1372        |
+| 388.2222 | 6600 | 0.1551        |
+| 391.1667 | 6650 | 0.1343        |
+| 394.1111 | 6700 | 0.2394        |
+| 397.0556 | 6750 | 0.1882        |
+| 399.9444 | 6800 | 0.1786        |
+| 402.8889 | 6850 | 0.125         |
+| 405.8333 | 6900 | 0.1059        |
+| 408.7778 | 6950 | 0.1414        |
+| 411.7222 | 7000 | 0.0593        |
+| 414.6667 | 7050 | 0.1037        |
+| 417.6111 | 7100 | 0.098         |
+| 420.5556 | 7150 | 0.1457        |
+| 423.5    | 7200 | 0.1193        |
+| 426.4444 | 7250 | 0.1061        |
+| 429.3889 | 7300 | 0.1305        |
+| 432.3333 | 7350 | 0.1416        |
+| 435.2778 | 7400 | 0.1117        |
+| 438.2222 | 7450 | 0.1003        |
+| 441.1667 | 7500 | 0.1217        |
+| 444.1111 | 7550 | 0.0872        |
+| 447.0556 | 7600 | 0.1219        |
+| 449.9444 | 7650 | 0.1061        |
+| 452.8889 | 7700 | 0.1559        |
+| 455.8333 | 7750 | 0.1599        |
+| 458.7778 | 7800 | 0.1436        |
+| 461.7222 | 7850 | 0.1207        |
+| 464.6667 | 7900 | 0.1272        |
+| 467.6111 | 7950 | 0.1048        |
+| 470.5556 | 8000 | 0.1216        |
+| 473.5    | 8050 | 0.133         |
+| 476.4444 | 8100 | 0.0971        |
+| 479.3889 | 8150 | 0.154         |
+| 482.3333 | 8200 | 0.0697        |
+| 485.2778 | 8250 | 0.136         |
+| 488.2222 | 8300 | 0.1315        |
+| 491.1667 | 8350 | 0.1103        |
+| 494.1111 | 8400 | 0.1065        |
+| 497.0556 | 8450 | 0.0784        |
+| 499.9444 | 8500 | 0.134         |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### BatchAllTripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification},
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "cl-nagoya/sup-simcse-ja-base",
+  "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": "4.49.0",
+  "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": "4.49.0",
+    "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 sha256:8da06c1239f6071803b2640c30e7276904b2cfb917c33fbc22fdea6008e3c1f3
+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 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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"
+}