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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:8788 |
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- loss:BatchAllTripletLoss |
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base_model: cl-nagoya/sup-simcse-ja-base |
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widget: |
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- source_sentence: 科目：ユニット及びその他。名称：ピクチャーレールA。 |
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sentences: |
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- 科目：ユニット及びその他。名称：床ゴムチップ舗装。 |
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- 科目：ユニット及びその他。名称：講堂スピーカー戸。 |
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- 科目：ユニット及びその他。名称：C7三槽シンク。 |
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- source_sentence: 科目：ユニット及びその他。名称：A-#小児プレイルームアート。 |
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sentences: |
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- 科目：ユニット及びその他。名称：F-#階ひまわり学級職員室ミニキッチン。 |
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- 科目：ユニット及びその他。名称：連絡通路梁用バトントラス。 |
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- 科目：ユニット及びその他。名称：体育館サブバレーボールコートライン。 |
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- source_sentence: 科目：ユニット及びその他。名称：厨房カウンター。 |
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sentences: |
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- 科目：コンクリート。名称：地上部暑中コンクリート。 |
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- 科目：コンクリート。名称：免震EXP.J用充填コンクリート。 |
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- 科目：コンクリート。名称：基礎コンクリート。 |
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- source_sentence: 科目：ユニット及びその他。名称：1F電話コーナーカウンター。 |
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sentences: |
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- 科目：ユニット及びその他。名称：1・2階男子・女子更衣室カーテンレール。 |
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- 科目：コンクリート。名称：鉄筋コンクリート（免震下部）。 |
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- 科目：タイル。名称：EXP.J上床磁器質タイルA。 |
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- source_sentence: 科目：ユニット及びその他。名称：4F透析室カウンター。 |
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sentences: |
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- 科目：ユニット及びその他。名称：2F初療1、2カウンター。 |
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- 科目：ユニット及びその他。名称：5Fファミリールームカウンター。 |
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- 科目：ユニット及びその他。名称：9Fスタッフステーション1カウンター。 |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v1_1") |
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# Run inference |
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sentences = [ |
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'科目：ユニット及びその他。名称：4F透析室カウンター。', |
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'科目：ユニット及びその他。名称：2F初療1、2カウンター。', |
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'科目：ユニット及びその他。名称：9Fスタッフステーション1カウンター。', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 8,788 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 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| type | string | int | |
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| details | <ul><li>min: 13 tokens</li><li>mean: 23.19 tokens</li><li>max: 41 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: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.20%</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: ~0.40%</li><li>17: ~0.20%</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: ~0.60%</li><li>43: ~0.70%</li><li>44: ~0.20%</li><li>45: ~0.30%</li><li>46: ~0.20%</li><li>47: ~0.20%</li><li>48: ~0.30%</li><li>49: ~0.20%</li><li>50: ~0.20%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: ~0.30%</li><li>54: ~0.40%</li><li>55: ~0.30%</li><li>56: ~0.20%</li><li>57: ~0.20%</li><li>58: ~0.20%</li><li>59: ~0.20%</li><li>60: ~0.20%</li><li>61: ~0.30%</li><li>62: ~0.20%</li><li>63: ~0.20%</li><li>64: ~0.20%</li><li>65: ~0.20%</li><li>66: ~0.40%</li><li>67: ~0.40%</li><li>68: ~0.20%</li><li>69: ~0.60%</li><li>70: ~0.20%</li><li>71: ~0.20%</li><li>72: ~0.20%</li><li>73: ~0.20%</li><li>74: ~0.20%</li><li>75: ~0.30%</li><li>76: ~0.20%</li><li>77: ~0.40%</li><li>78: ~0.20%</li><li>79: ~0.20%</li><li>80: ~0.50%</li><li>81: ~0.30%</li><li>82: ~0.60%</li><li>83: ~0.20%</li><li>84: ~0.30%</li><li>85: ~0.20%</li><li>86: ~0.20%</li><li>87: ~0.20%</li><li>88: ~0.20%</li><li>89: ~1.10%</li><li>90: ~1.70%</li><li>91: ~2.20%</li><li>92: ~0.50%</li><li>93: ~0.20%</li><li>94: ~0.20%</li><li>95: ~1.60%</li><li>96: ~0.20%</li><li>97: ~0.20%</li><li>98: ~0.20%</li><li>99: ~0.20%</li><li>100: ~0.30%</li><li>101: ~1.70%</li><li>102: ~0.20%</li><li>103: ~0.20%</li><li>104: ~0.40%</li><li>105: ~0.40%</li><li>106: ~0.20%</li><li>107: ~0.20%</li><li>108: ~0.20%</li><li>109: ~1.10%</li><li>110: ~0.20%</li><li>111: ~0.40%</li><li>112: ~0.50%</li><li>113: ~0.20%</li><li>114: ~0.20%</li><li>115: ~0.20%</li><li>116: ~0.20%</li><li>117: ~0.50%</li><li>118: ~0.20%</li><li>119: ~0.20%</li><li>120: ~0.20%</li><li>121: ~0.20%</li><li>122: ~0.20%</li><li>123: ~0.20%</li><li>124: ~0.30%</li><li>125: ~0.20%</li><li>126: ~0.20%</li><li>127: ~0.20%</li><li>128: ~0.40%</li><li>129: ~0.20%</li><li>130: ~0.20%</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: ~0.30%</li><li>139: ~0.20%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.20%</li><li>143: ~0.20%</li><li>144: ~0.20%</li><li>145: ~0.20%</li><li>146: ~0.20%</li><li>147: ~0.20%</li><li>148: ~0.20%</li><li>149: ~0.20%</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: ~0.60%</li><li>157: ~0.20%</li><li>158: ~1.50%</li><li>159: ~0.20%</li><li>160: ~0.20%</li><li>161: ~0.20%</li><li>162: ~0.20%</li><li>163: ~0.50%</li><li>164: ~0.20%</li><li>165: ~0.20%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~0.20%</li><li>172: ~0.20%</li><li>173: ~1.30%</li><li>174: ~0.20%</li><li>175: ~0.20%</li><li>176: ~0.20%</li><li>177: ~0.20%</li><li>178: ~0.60%</li><li>179: ~0.20%</li><li>180: ~0.20%</li><li>181: ~0.20%</li><li>182: ~0.20%</li><li>183: ~0.20%</li><li>184: ~0.20%</li><li>185: ~0.30%</li><li>186: ~0.20%</li><li>187: ~0.20%</li><li>188: ~0.30%</li><li>189: ~0.20%</li><li>190: ~0.90%</li><li>191: ~0.30%</li><li>192: ~0.30%</li><li>193: ~0.20%</li><li>194: ~0.30%</li><li>195: ~0.20%</li><li>196: ~0.80%</li><li>197: ~0.20%</li><li>198: ~0.20%</li><li>199: ~0.30%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.20%</li><li>203: ~0.20%</li><li>204: ~0.20%</li><li>205: ~1.20%</li><li>206: ~0.40%</li><li>207: ~0.20%</li><li>208: ~0.20%</li><li>209: ~0.20%</li><li>210: ~0.20%</li><li>211: ~0.30%</li><li>212: ~0.20%</li><li>213: ~0.80%</li><li>214: ~0.30%</li><li>215: ~0.20%</li><li>216: ~1.10%</li><li>217: ~0.30%</li><li>218: ~0.20%</li><li>219: ~0.20%</li><li>220: ~0.20%</li><li>221: ~0.20%</li><li>222: ~0.20%</li><li>223: ~0.20%</li><li>224: ~0.20%</li><li>225: ~0.30%</li><li>226: ~0.20%</li><li>227: ~0.90%</li><li>228: ~4.70%</li><li>229: ~0.20%</li><li>230: ~0.20%</li><li>231: ~0.20%</li><li>232: ~0.70%</li><li>233: ~0.20%</li><li>234: ~0.80%</li><li>235: ~0.20%</li><li>236: ~0.40%</li><li>237: ~0.30%</li><li>238: ~0.40%</li><li>239: ~0.20%</li><li>240: ~0.30%</li><li>241: ~0.50%</li><li>242: ~0.30%</li><li>243: ~0.20%</li><li>244: ~0.20%</li><li>245: ~0.30%</li><li>246: ~0.30%</li><li>247: ~0.30%</li><li>248: ~0.60%</li><li>249: ~0.20%</li><li>250: ~0.20%</li><li>251: ~0.20%</li><li>252: ~0.30%</li><li>253: ~0.30%</li><li>254: ~1.90%</li><li>255: ~0.20%</li><li>256: ~0.20%</li><li>257: ~0.20%</li><li>258: ~0.20%</li><li>259: ~0.20%</li><li>260: ~0.50%</li><li>261: ~0.20%</li><li>262: ~0.30%</li><li>263: ~0.20%</li><li>264: ~0.20%</li><li>265: ~1.00%</li><li>266: ~0.20%</li><li>267: ~0.20%</li><li>268: ~0.20%</li><li>269: ~0.40%</li><li>270: ~0.20%</li><li>271: ~0.20%</li><li>272: ~0.20%</li><li>273: ~0.20%</li><li>274: ~0.20%</li><li>275: ~0.20%</li><li>276: ~0.20%</li><li>277: ~3.70%</li><li>278: ~0.20%</li><li>279: ~0.40%</li><li>280: ~0.20%</li><li>281: ~0.20%</li><li>282: ~0.90%</li><li>283: ~0.40%</li><li>284: ~0.20%</li><li>285: ~2.30%</li><li>286: ~0.30%</li><li>287: ~0.20%</li><li>288: ~0.30%</li><li>289: ~0.60%</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:----------------------------------------|:---------------| |
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| <code>科目：コンクリート。名称：免震基礎天端グラウト注入。</code> | <code>0</code> | |
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| <code>科目：コンクリート。名称：免震基礎天端グラウト注入。</code> | <code>0</code> | |
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| <code>科目：コンクリート。名称：コンクリートポンプ圧送。</code> | <code>1</code> | |
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* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 250 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: group_by_label |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 250 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `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 | |
|
|
|:--------:|:----:|:-------------:| |
|
|
| 0.5714 | 20 | 0.787 | |
|
|
| 1.2 | 40 | 0.7827 | |
|
|
| 1.7714 | 60 | 0.7361 | |
|
|
| 2.4 | 80 | 0.6798 | |
|
|
| 3.0286 | 100 | 0.6569 | |
|
|
| 3.6 | 120 | 0.6669 | |
|
|
| 4.2286 | 140 | 0.6163 | |
|
|
| 4.8 | 160 | 0.6277 | |
|
|
| 5.4286 | 180 | 0.6449 | |
|
|
| 6.0571 | 200 | 0.6135 | |
|
|
| 6.6286 | 220 | 0.6445 | |
|
|
| 7.2571 | 240 | 0.6572 | |
|
|
| 7.8286 | 260 | 0.6268 | |
|
|
| 8.4571 | 280 | 0.6034 | |
|
|
| 9.0857 | 300 | 0.5598 | |
|
|
| 9.6571 | 320 | 0.5801 | |
|
|
| 10.2857 | 340 | 0.5471 | |
|
|
| 10.8571 | 360 | 0.6579 | |
|
|
| 11.4857 | 380 | 0.6059 | |
|
|
| 12.1143 | 400 | 0.5715 | |
|
|
| 12.6857 | 420 | 0.5986 | |
|
|
| 13.3143 | 440 | 0.5601 | |
|
|
| 13.8857 | 460 | 0.5547 | |
|
|
| 14.5143 | 480 | 0.5642 | |
|
|
| 15.1429 | 500 | 0.697 | |
|
|
| 15.7143 | 520 | 0.5688 | |
|
|
| 16.3429 | 540 | 0.5736 | |
|
|
| 16.9143 | 560 | 0.5088 | |
|
|
| 17.5429 | 580 | 0.5677 | |
|
|
| 18.1714 | 600 | 0.6028 | |
|
|
| 18.7429 | 620 | 0.5674 | |
|
|
| 19.3714 | 640 | 0.5665 | |
|
|
| 19.9429 | 660 | 0.6289 | |
|
|
| 20.5714 | 680 | 0.5456 | |
|
|
| 21.2 | 700 | 0.4944 | |
|
|
| 21.7714 | 720 | 0.5712 | |
|
|
| 22.4 | 740 | 0.6106 | |
|
|
| 23.0286 | 760 | 0.5209 | |
|
|
| 23.6 | 780 | 0.5236 | |
|
|
| 24.2286 | 800 | 0.6091 | |
|
|
| 24.8 | 820 | 0.6678 | |
|
|
| 25.4286 | 840 | 0.4693 | |
|
|
| 26.0571 | 860 | 0.4582 | |
|
|
| 26.6286 | 880 | 0.5627 | |
|
|
| 27.2571 | 900 | 0.5525 | |
|
|
| 27.8286 | 920 | 0.503 | |
|
|
| 28.4571 | 940 | 0.4801 | |
|
|
| 29.0857 | 960 | 0.5039 | |
|
|
| 29.6571 | 980 | 0.5049 | |
|
|
| 30.2857 | 1000 | 0.595 | |
|
|
| 30.8571 | 1020 | 0.4733 | |
|
|
| 31.4857 | 1040 | 0.5804 | |
|
|
| 32.1143 | 1060 | 0.4101 | |
|
|
| 32.6857 | 1080 | 0.4311 | |
|
|
| 33.3143 | 1100 | 0.4746 | |
|
|
| 33.8857 | 1120 | 0.4964 | |
|
|
| 34.5143 | 1140 | 0.4436 | |
|
|
| 35.1429 | 1160 | 0.6351 | |
|
|
| 35.7143 | 1180 | 0.5267 | |
|
|
| 36.3429 | 1200 | 0.4685 | |
|
|
| 36.9143 | 1220 | 0.4201 | |
|
|
| 37.5429 | 1240 | 0.4256 | |
|
|
| 38.1714 | 1260 | 0.5543 | |
|
|
| 38.7429 | 1280 | 0.5176 | |
|
|
| 39.3714 | 1300 | 0.4328 | |
|
|
| 39.9429 | 1320 | 0.4746 | |
|
|
| 40.5714 | 1340 | 0.4768 | |
|
|
| 41.2 | 1360 | 0.4663 | |
|
|
| 41.7714 | 1380 | 0.4729 | |
|
|
| 42.4 | 1400 | 0.4141 | |
|
|
| 43.0286 | 1420 | 0.3195 | |
|
|
| 43.6 | 1440 | 0.3789 | |
|
|
| 44.2286 | 1460 | 0.4032 | |
|
|
| 44.8 | 1480 | 0.443 | |
|
|
| 45.4286 | 1500 | 0.4116 | |
|
|
| 46.0571 | 1520 | 0.4951 | |
|
|
| 46.6286 | 1540 | 0.3845 | |
|
|
| 47.2571 | 1560 | 0.3461 | |
|
|
| 47.8286 | 1580 | 0.4754 | |
|
|
| 48.4571 | 1600 | 0.5583 | |
|
|
| 49.0857 | 1620 | 0.4282 | |
|
|
| 49.6571 | 1640 | 0.436 | |
|
|
| 50.2857 | 1660 | 0.4097 | |
|
|
| 50.8571 | 1680 | 0.4642 | |
|
|
| 51.4857 | 1700 | 0.3243 | |
|
|
| 52.1143 | 1720 | 0.4395 | |
|
|
| 52.6857 | 1740 | 0.3672 | |
|
|
| 53.3143 | 1760 | 0.4781 | |
|
|
| 53.8857 | 1780 | 0.5362 | |
|
|
| 54.5143 | 1800 | 0.4401 | |
|
|
| 55.1429 | 1820 | 0.4313 | |
|
|
| 55.7143 | 1840 | 0.2751 | |
|
|
| 56.3429 | 1860 | 0.331 | |
|
|
| 56.9143 | 1880 | 0.4325 | |
|
|
| 57.5429 | 1900 | 0.2995 | |
|
|
| 58.1714 | 1920 | 0.4159 | |
|
|
| 58.7429 | 1940 | 0.5603 | |
|
|
| 59.3714 | 1960 | 0.4575 | |
|
|
| 59.9429 | 1980 | 0.4677 | |
|
|
| 60.5714 | 2000 | 0.4653 | |
|
|
| 61.2 | 2020 | 0.3098 | |
|
|
| 61.7714 | 2040 | 0.3188 | |
|
|
| 62.4 | 2060 | 0.3769 | |
|
|
| 63.0286 | 2080 | 0.2902 | |
|
|
| 63.6 | 2100 | 0.4064 | |
|
|
| 64.2286 | 2120 | 0.3663 | |
|
|
| 64.8 | 2140 | 0.3184 | |
|
|
| 65.4286 | 2160 | 0.4874 | |
|
|
| 66.0571 | 2180 | 0.4094 | |
|
|
| 66.6286 | 2200 | 0.4261 | |
|
|
| 67.2571 | 2220 | 0.3808 | |
|
|
| 67.8286 | 2240 | 0.2991 | |
|
|
| 68.4571 | 2260 | 0.3242 | |
|
|
| 69.0857 | 2280 | 0.2582 | |
|
|
| 69.6571 | 2300 | 0.3806 | |
|
|
| 70.2857 | 2320 | 0.3573 | |
|
|
| 70.8571 | 2340 | 0.3183 | |
|
|
| 71.4857 | 2360 | 0.4043 | |
|
|
| 72.1143 | 2380 | 0.4266 | |
|
|
| 72.6857 | 2400 | 0.5612 | |
|
|
| 73.3143 | 2420 | 0.3476 | |
|
|
| 73.8857 | 2440 | 0.3018 | |
|
|
| 74.5143 | 2460 | 0.2952 | |
|
|
| 75.1429 | 2480 | 0.2633 | |
|
|
| 75.7143 | 2500 | 0.3564 | |
|
|
| 76.3429 | 2520 | 0.2283 | |
|
|
| 76.9143 | 2540 | 0.3615 | |
|
|
| 77.5429 | 2560 | 0.2174 | |
|
|
| 78.1714 | 2580 | 0.3049 | |
|
|
| 78.7429 | 2600 | 0.2838 | |
|
|
| 79.3714 | 2620 | 0.3191 | |
|
|
| 79.9429 | 2640 | 0.2529 | |
|
|
| 80.5714 | 2660 | 0.3192 | |
|
|
| 81.2 | 2680 | 0.5119 | |
|
|
| 81.7714 | 2700 | 0.2459 | |
|
|
| 82.4 | 2720 | 0.4136 | |
|
|
| 83.0286 | 2740 | 0.3266 | |
|
|
| 83.6 | 2760 | 0.2863 | |
|
|
| 84.2286 | 2780 | 0.3563 | |
|
|
| 84.8 | 2800 | 0.2605 | |
|
|
| 85.4286 | 2820 | 0.254 | |
|
|
| 86.0571 | 2840 | 0.2252 | |
|
|
| 86.6286 | 2860 | 0.3191 | |
|
|
| 87.2571 | 2880 | 0.3074 | |
|
|
| 87.8286 | 2900 | 0.274 | |
|
|
| 88.4571 | 2920 | 0.3864 | |
|
|
| 89.0857 | 2940 | 0.3206 | |
|
|
| 89.6571 | 2960 | 0.2752 | |
|
|
| 90.2857 | 2980 | 0.2033 | |
|
|
| 90.8571 | 3000 | 0.3979 | |
|
|
| 91.4857 | 3020 | 0.4327 | |
|
|
| 92.1143 | 3040 | 0.1999 | |
|
|
| 92.6857 | 3060 | 0.3939 | |
|
|
| 93.3143 | 3080 | 0.2733 | |
|
|
| 93.8857 | 3100 | 0.4334 | |
|
|
| 94.5143 | 3120 | 0.3726 | |
|
|
| 95.1429 | 3140 | 0.2567 | |
|
|
| 95.7143 | 3160 | 0.258 | |
|
|
| 96.3429 | 3180 | 0.1805 | |
|
|
| 96.9143 | 3200 | 0.3244 | |
|
|
| 97.5429 | 3220 | 0.2038 | |
|
|
| 98.1714 | 3240 | 0.2689 | |
|
|
| 98.7429 | 3260 | 0.433 | |
|
|
| 99.3714 | 3280 | 0.1587 | |
|
|
| 99.9429 | 3300 | 0.3088 | |
|
|
| 100.5714 | 3320 | 0.3049 | |
|
|
| 101.2 | 3340 | 0.335 | |
|
|
| 101.7714 | 3360 | 0.2688 | |
|
|
| 102.4 | 3380 | 0.359 | |
|
|
| 103.0286 | 3400 | 0.2512 | |
|
|
| 103.6 | 3420 | 0.2818 | |
|
|
| 104.2286 | 3440 | 0.3606 | |
|
|
| 104.8 | 3460 | 0.3254 | |
|
|
| 105.4286 | 3480 | 0.2487 | |
|
|
| 106.0571 | 3500 | 0.2184 | |
|
|
| 106.6286 | 3520 | 0.2897 | |
|
|
| 107.2571 | 3540 | 0.2849 | |
|
|
| 107.8286 | 3560 | 0.362 | |
|
|
| 108.4571 | 3580 | 0.2418 | |
|
|
| 109.0857 | 3600 | 0.1498 | |
|
|
| 109.6571 | 3620 | 0.2566 | |
|
|
| 110.2857 | 3640 | 0.1181 | |
|
|
| 110.8571 | 3660 | 0.3675 | |
|
|
| 111.4857 | 3680 | 0.2722 | |
|
|
| 112.1143 | 3700 | 0.3779 | |
|
|
| 112.6857 | 3720 | 0.3882 | |
|
|
| 113.3143 | 3740 | 0.1941 | |
|
|
| 113.8857 | 3760 | 0.2281 | |
|
|
| 114.5143 | 3780 | 0.2079 | |
|
|
| 115.1429 | 3800 | 0.3443 | |
|
|
| 115.7143 | 3820 | 0.2763 | |
|
|
| 116.3429 | 3840 | 0.2331 | |
|
|
| 116.9143 | 3860 | 0.2093 | |
|
|
| 117.5429 | 3880 | 0.2439 | |
|
|
| 118.1714 | 3900 | 0.1312 | |
|
|
| 118.7429 | 3920 | 0.1098 | |
|
|
| 119.3714 | 3940 | 0.2295 | |
|
|
| 119.9429 | 3960 | 0.2501 | |
|
|
| 120.5714 | 3980 | 0.3522 | |
|
|
| 121.2 | 4000 | 0.3293 | |
|
|
| 121.7714 | 4020 | 0.1698 | |
|
|
| 122.4 | 4040 | 0.3992 | |
|
|
| 123.0286 | 4060 | 0.1931 | |
|
|
| 123.6 | 4080 | 0.1755 | |
|
|
| 124.2286 | 4100 | 0.3408 | |
|
|
| 124.8 | 4120 | 0.2337 | |
|
|
| 125.4286 | 4140 | 0.2121 | |
|
|
| 126.0571 | 4160 | 0.1628 | |
|
|
| 126.6286 | 4180 | 0.2455 | |
|
|
| 127.2571 | 4200 | 0.3342 | |
|
|
| 127.8286 | 4220 | 0.1725 | |
|
|
| 128.4571 | 4240 | 0.3714 | |
|
|
| 129.0857 | 4260 | 0.2775 | |
|
|
| 129.6571 | 4280 | 0.1764 | |
|
|
| 130.2857 | 4300 | 0.1863 | |
|
|
| 130.8571 | 4320 | 0.276 | |
|
|
| 131.4857 | 4340 | 0.2006 | |
|
|
| 132.1143 | 4360 | 0.2099 | |
|
|
| 132.6857 | 4380 | 0.2397 | |
|
|
| 133.3143 | 4400 | 0.223 | |
|
|
| 133.8857 | 4420 | 0.1321 | |
|
|
| 134.5143 | 4440 | 0.2499 | |
|
|
| 135.1429 | 4460 | 0.2107 | |
|
|
| 135.7143 | 4480 | 0.2374 | |
|
|
| 136.3429 | 4500 | 0.2589 | |
|
|
| 136.9143 | 4520 | 0.2382 | |
|
|
| 137.5429 | 4540 | 0.1058 | |
|
|
| 138.1714 | 4560 | 0.2519 | |
|
|
| 138.7429 | 4580 | 0.23 | |
|
|
| 139.3714 | 4600 | 0.2031 | |
|
|
| 139.9429 | 4620 | 0.2424 | |
|
|
| 140.5714 | 4640 | 0.1312 | |
|
|
| 141.2 | 4660 | 0.1787 | |
|
|
| 141.7714 | 4680 | 0.2445 | |
|
|
| 142.4 | 4700 | 0.1948 | |
|
|
| 143.0286 | 4720 | 0.2601 | |
|
|
| 143.6 | 4740 | 0.1906 | |
|
|
| 144.2286 | 4760 | 0.35 | |
|
|
| 144.8 | 4780 | 0.1674 | |
|
|
| 145.4286 | 4800 | 0.2339 | |
|
|
| 146.0571 | 4820 | 0.2151 | |
|
|
| 146.6286 | 4840 | 0.1986 | |
|
|
| 147.2571 | 4860 | 0.1608 | |
|
|
| 147.8286 | 4880 | 0.2729 | |
|
|
| 148.4571 | 4900 | 0.1555 | |
|
|
| 149.0857 | 4920 | 0.1536 | |
|
|
| 149.6571 | 4940 | 0.1245 | |
|
|
| 150.2857 | 4960 | 0.2635 | |
|
|
| 150.8571 | 4980 | 0.1628 | |
|
|
| 151.4857 | 5000 | 0.1869 | |
|
|
| 152.1143 | 5020 | 0.2142 | |
|
|
| 152.6857 | 5040 | 0.1867 | |
|
|
| 153.3143 | 5060 | 0.2361 | |
|
|
| 153.8857 | 5080 | 0.1811 | |
|
|
| 154.5143 | 5100 | 0.4071 | |
|
|
| 155.1429 | 5120 | 0.2499 | |
|
|
| 155.7143 | 5140 | 0.2398 | |
|
|
| 156.3429 | 5160 | 0.1486 | |
|
|
| 156.9143 | 5180 | 0.1683 | |
|
|
| 157.5429 | 5200 | 0.1434 | |
|
|
| 158.1714 | 5220 | 0.1731 | |
|
|
| 158.7429 | 5240 | 0.1674 | |
|
|
| 159.3714 | 5260 | 0.1085 | |
|
|
| 159.9429 | 5280 | 0.2573 | |
|
|
| 160.5714 | 5300 | 0.1937 | |
|
|
| 161.2 | 5320 | 0.0806 | |
|
|
| 161.7714 | 5340 | 0.1411 | |
|
|
| 162.4 | 5360 | 0.1603 | |
|
|
| 163.0286 | 5380 | 0.1787 | |
|
|
| 163.6 | 5400 | 0.2099 | |
|
|
| 164.2286 | 5420 | 0.2676 | |
|
|
| 164.8 | 5440 | 0.2658 | |
|
|
| 165.4286 | 5460 | 0.2632 | |
|
|
| 166.0571 | 5480 | 0.1839 | |
|
|
| 166.6286 | 5500 | 0.2524 | |
|
|
| 167.2571 | 5520 | 0.2018 | |
|
|
| 167.8286 | 5540 | 0.2955 | |
|
|
| 168.4571 | 5560 | 0.209 | |
|
|
| 169.0857 | 5580 | 0.1999 | |
|
|
| 169.6571 | 5600 | 0.2836 | |
|
|
| 170.2857 | 5620 | 0.1559 | |
|
|
| 170.8571 | 5640 | 0.2746 | |
|
|
| 171.4857 | 5660 | 0.1939 | |
|
|
| 172.1143 | 5680 | 0.1561 | |
|
|
| 172.6857 | 5700 | 0.0935 | |
|
|
| 173.3143 | 5720 | 0.1927 | |
|
|
| 173.8857 | 5740 | 0.3022 | |
|
|
| 174.5143 | 5760 | 0.2068 | |
|
|
| 175.1429 | 5780 | 0.1384 | |
|
|
| 175.7143 | 5800 | 0.086 | |
|
|
| 176.3429 | 5820 | 0.1181 | |
|
|
| 176.9143 | 5840 | 0.3145 | |
|
|
| 177.5429 | 5860 | 0.0974 | |
|
|
| 178.1714 | 5880 | 0.1891 | |
|
|
| 178.7429 | 5900 | 0.1788 | |
|
|
| 179.3714 | 5920 | 0.1954 | |
|
|
| 179.9429 | 5940 | 0.1342 | |
|
|
| 180.5714 | 5960 | 0.0936 | |
|
|
| 181.2 | 5980 | 0.3109 | |
|
|
| 181.7714 | 6000 | 0.1879 | |
|
|
| 182.4 | 6020 | 0.0798 | |
|
|
| 183.0286 | 6040 | 0.097 | |
|
|
| 183.6 | 6060 | 0.0835 | |
|
|
| 184.2286 | 6080 | 0.0931 | |
|
|
| 184.8 | 6100 | 0.1377 | |
|
|
| 185.4286 | 6120 | 0.1239 | |
|
|
| 186.0571 | 6140 | 0.0307 | |
|
|
| 186.6286 | 6160 | 0.1962 | |
|
|
| 187.2571 | 6180 | 0.242 | |
|
|
| 187.8286 | 6200 | 0.0886 | |
|
|
| 188.4571 | 6220 | 0.2103 | |
|
|
| 189.0857 | 6240 | 0.0746 | |
|
|
| 189.6571 | 6260 | 0.1191 | |
|
|
| 190.2857 | 6280 | 0.2356 | |
|
|
| 190.8571 | 6300 | 0.2015 | |
|
|
| 191.4857 | 6320 | 0.1728 | |
|
|
| 192.1143 | 6340 | 0.1624 | |
|
|
| 192.6857 | 6360 | 0.2528 | |
|
|
| 193.3143 | 6380 | 0.0759 | |
|
|
| 193.8857 | 6400 | 0.2138 | |
|
|
| 194.5143 | 6420 | 0.1544 | |
|
|
| 195.1429 | 6440 | 0.2444 | |
|
|
| 195.7143 | 6460 | 0.1896 | |
|
|
| 196.3429 | 6480 | 0.1646 | |
|
|
| 196.9143 | 6500 | 0.1305 | |
|
|
| 197.5429 | 6520 | 0.1379 | |
|
|
| 198.1714 | 6540 | 0.1845 | |
|
|
| 198.7429 | 6560 | 0.1997 | |
|
|
| 199.3714 | 6580 | 0.2049 | |
|
|
| 199.9429 | 6600 | 0.2891 | |
|
|
| 200.5714 | 6620 | 0.1718 | |
|
|
| 201.2 | 6640 | 0.1449 | |
|
|
| 201.7714 | 6660 | 0.2096 | |
|
|
| 202.4 | 6680 | 0.1056 | |
|
|
| 203.0286 | 6700 | 0.0862 | |
|
|
| 203.6 | 6720 | 0.0914 | |
|
|
| 204.2286 | 6740 | 0.2433 | |
|
|
| 204.8 | 6760 | 0.146 | |
|
|
| 205.4286 | 6780 | 0.2099 | |
|
|
| 206.0571 | 6800 | 0.0877 | |
|
|
| 206.6286 | 6820 | 0.1194 | |
|
|
| 207.2571 | 6840 | 0.069 | |
|
|
| 207.8286 | 6860 | 0.0742 | |
|
|
| 208.4571 | 6880 | 0.2773 | |
|
|
| 209.0857 | 6900 | 0.1762 | |
|
|
| 209.6571 | 6920 | 0.1573 | |
|
|
| 210.2857 | 6940 | 0.0922 | |
|
|
| 210.8571 | 6960 | 0.1366 | |
|
|
| 211.4857 | 6980 | 0.0746 | |
|
|
| 212.1143 | 7000 | 0.2004 | |
|
|
| 212.6857 | 7020 | 0.0922 | |
|
|
| 213.3143 | 7040 | 0.0662 | |
|
|
| 213.8857 | 7060 | 0.1828 | |
|
|
| 214.5143 | 7080 | 0.1202 | |
|
|
| 215.1429 | 7100 | 0.1388 | |
|
|
| 215.7143 | 7120 | 0.0638 | |
|
|
| 216.3429 | 7140 | 0.2259 | |
|
|
| 216.9143 | 7160 | 0.1219 | |
|
|
| 217.5429 | 7180 | 0.1599 | |
|
|
| 218.1714 | 7200 | 0.2424 | |
|
|
| 218.7429 | 7220 | 0.149 | |
|
|
| 219.3714 | 7240 | 0.272 | |
|
|
| 219.9429 | 7260 | 0.1051 | |
|
|
| 220.5714 | 7280 | 0.2117 | |
|
|
| 221.2 | 7300 | 0.1466 | |
|
|
| 221.7714 | 7320 | 0.1155 | |
|
|
| 222.4 | 7340 | 0.2247 | |
|
|
| 223.0286 | 7360 | 0.096 | |
|
|
| 223.6 | 7380 | 0.0566 | |
|
|
| 224.2286 | 7400 | 0.2404 | |
|
|
| 224.8 | 7420 | 0.1684 | |
|
|
| 225.4286 | 7440 | 0.0927 | |
|
|
| 226.0571 | 7460 | 0.1746 | |
|
|
| 226.6286 | 7480 | 0.13 | |
|
|
| 227.2571 | 7500 | 0.1027 | |
|
|
| 227.8286 | 7520 | 0.1359 | |
|
|
| 228.4571 | 7540 | 0.0937 | |
|
|
| 229.0857 | 7560 | 0.1378 | |
|
|
| 229.6571 | 7580 | 0.0458 | |
|
|
| 230.2857 | 7600 | 0.0766 | |
|
|
| 230.8571 | 7620 | 0.0896 | |
|
|
| 231.4857 | 7640 | 0.1541 | |
|
|
| 232.1143 | 7660 | 0.1464 | |
|
|
| 232.6857 | 7680 | 0.1427 | |
|
|
| 233.3143 | 7700 | 0.2471 | |
|
|
| 233.8857 | 7720 | 0.1636 | |
|
|
| 234.5143 | 7740 | 0.1601 | |
|
|
| 235.1429 | 7760 | 0.1583 | |
|
|
| 235.7143 | 7780 | 0.1473 | |
|
|
| 236.3429 | 7800 | 0.1211 | |
|
|
| 236.9143 | 7820 | 0.1582 | |
|
|
| 237.5429 | 7840 | 0.1083 | |
|
|
| 238.1714 | 7860 | 0.2014 | |
|
|
| 238.7429 | 7880 | 0.0981 | |
|
|
| 239.3714 | 7900 | 0.2449 | |
|
|
| 239.9429 | 7920 | 0.1142 | |
|
|
| 240.5714 | 7940 | 0.1177 | |
|
|
| 241.2 | 7960 | 0.1241 | |
|
|
| 241.7714 | 7980 | 0.2778 | |
|
|
| 242.4 | 8000 | 0.1066 | |
|
|
| 243.0286 | 8020 | 0.0867 | |
|
|
| 243.6 | 8040 | 0.156 | |
|
|
| 244.2286 | 8060 | 0.1413 | |
|
|
| 244.8 | 8080 | 0.0598 | |
|
|
| 245.4286 | 8100 | 0.1206 | |
|
|
| 246.0571 | 8120 | 0.1883 | |
|
|
| 246.6286 | 8140 | 0.1245 | |
|
|
| 247.2571 | 8160 | 0.0949 | |
|
|
| 247.8286 | 8180 | 0.1096 | |
|
|
| 248.4571 | 8200 | 0.1567 | |
|
|
| 249.0857 | 8220 | 0.065 | |
|
|
| 249.6571 | 8240 | 0.1075 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.48.3 |
|
|
- PyTorch: 2.5.1+cu124 |
|
|
- Accelerate: 1.3.0 |
|
|
- Datasets: 3.3.2 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## 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} |
|
|
} |
|
|
``` |
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