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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:9054 |
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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:⑮傍聴席椅子#人掛け｡ |
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sentences: |
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- 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｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:控室B#-#､B#-#ｶｳﾝﾀｰ｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:記載ｶｳﾝﾀｰｻｲﾝ｡ |
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- 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｡ |
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sentences: |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:床ｱｽﾌｧﾙﾄ舗装｡ |
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- 科目:ﾀｲﾙ｡名称:立上りﾀｲﾙA｡ |
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- 科目:ﾀｲﾙ｡名称:段床磁器質ﾀｲﾙ｡ |
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- source_sentence: 科目:ﾀｲﾙ｡名称:ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ張り｡ |
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sentences: |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:ｶｰﾃﾝﾎﾞｯｸｽD｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:総合案内ｶｳﾝﾀｰ｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:天井ｶｰﾃﾝﾚｰﾙ｡ |
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- source_sentence: 科目:ﾀｲﾙ｡名称:屋外階段踊場ﾀｲﾙ張り｡ |
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sentences: |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:執務室#-#規格品ｶｳﾝﾀｰ｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:受付ﾛﾋﾞｰｶｳﾝﾀｰ｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:Sc-#踊場階数表示ｻｲﾝ｡ |
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- source_sentence: 科目:ﾀｲﾙ｡名称:床磁器質ﾀｲﾙ｡ |
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sentences: |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:La-#開閉注意ｻｲﾝ｡ |
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- 科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡ |
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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-v0_9_6") |
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# Run inference |
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sentences = [ |
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'科目:ﾀｲﾙ｡名称:床磁器質ﾀｲﾙ｡', |
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'科目:ﾕﾆｯﾄ及びその他｡名称:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡', |
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'科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡', |
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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: 9,054 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: 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> | |
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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:免震基礎天端ｸﾞﾗｳﾄ注入｡</code> | <code>0</code> | |
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| <code>科目:ｺﾝｸﾘｰﾄ｡名称:免震基礎天端ｸﾞﾗｳﾄ注入｡</code> | <code>0</code> | |
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| <code>科目:ｺﾝｸﾘｰﾄ｡名称:ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送｡</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`: 512 |
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- `per_device_eval_batch_size`: 512 |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 500 |
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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`: 512 |
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- `per_device_eval_batch_size`: 512 |
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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`: 500 |
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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 |
|
|
- `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} |
|
|
} |
|
|
``` |
|
|
|
|
|
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