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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:16547 |
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- loss:CustomBatchAllTripletLoss |
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widget: |
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- source_sentence: 科目：ユニット及びその他。名称：突出ｻｲﾝ。 |
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sentences: |
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- 科目：ユニット及びその他。名称：ｶｳﾝﾀｰ上ｶﾞﾗｽ台。 |
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- 科目：ユニット及びその他。名称：ｼｬﾜｰﾕﾆｯﾄ枠。 |
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- 科目：ユニット及びその他。名称：ｴﾝﾄﾗﾝｽｻｲﾝ。 |
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- source_sentence: 科目：タイル。名称：階段踏面ﾀｲﾙ。 |
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sentences: |
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- 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC42N/mm2 ｽﾗﾝﾌﾟ21高性能AE減水剤。備考：代価表 0056。 |
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- 科目：ユニット及びその他。名称：配膳棚。 |
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。 |
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- source_sentence: 科目：ユニット及びその他。名称：ｵｰﾊﾞｰﾌﾛｰ管。 |
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sentences: |
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- 科目：ユニット及びその他。名称：自動支払機ｻｲﾝ。 |
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- 科目：ユニット及びその他。名称：SPﾎﾞｯｸｽ。 |
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- 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。 |
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- source_sentence: 科目：ユニット及びその他。名称：執務室#-#規格品ｶｳﾝﾀｰ。 |
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sentences: |
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- 科目：ユニット及びその他。名称：床ｺﾝｸﾘｰﾄ平板ﾃﾞｯｷ。 |
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- 科目：ユニット及びその他。名称：#階守衛室ｶｳﾝﾀｰ。 |
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- 科目：ユニット及びその他。名称：ｺﾝｸﾘｰﾄ舗装。 |
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- source_sentence: 科目：ユニット及びその他。名称：#FHCU#床室ｶｳﾝﾀｰ。 |
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sentences: |
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- 科目：ユニット及びその他。名称：室名ｻｲﾝ。 |
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- 科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。 |
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- 科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。 |
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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 |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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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:** [Unknown](https://huggingface.co/unknown) --> |
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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-nss-v1_0_2") |
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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：#FHCU#床室ｶｳﾝﾀｰ。', |
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'科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。', |
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'科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。', |
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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: 16,547 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: 18.77 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~3.40%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~0.40%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.40%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.90%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~1.10%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.60%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.50%</li><li>70: ~0.60%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.50%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.80%</li><li>84: ~0.60%</li><li>85: ~0.50%</li><li>86: ~0.30%</li><li>87: ~0.30%</li><li>88: ~16.50%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.30%</li><li>92: ~0.30%</li><li>93: ~0.30%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~0.30%</li><li>97: ~0.50%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.70%</li><li>106: ~0.70%</li><li>107: ~0.30%</li><li>108: ~3.20%</li><li>109: ~0.30%</li><li>110: ~0.40%</li><li>111: ~2.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.40%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.80%</li><li>122: ~0.30%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.30%</li><li>126: ~0.30%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.50%</li><li>131: ~0.30%</li><li>132: ~0.40%</li><li>133: ~0.30%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.40%</li><li>144: ~0.30%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.70%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~1.30%</li><li>155: ~0.30%</li><li>156: ~0.40%</li><li>157: ~0.30%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~1.50%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~1.50%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~7.20%</li><li>172: ~0.30%</li><li>173: ~1.00%</li><li>174: ~0.30%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~1.80%</li><li>178: ~0.30%</li><li>179: ~0.50%</li><li>180: ~0.70%</li><li>181: ~0.10%</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：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> | |
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* Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code> |
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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`: 200 |
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- `warmup_ratio`: 0.15 |
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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`: 200 |
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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`: 0.15 |
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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 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `tp_size`: 0 |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: group_by_label |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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|
### Training Logs |
|
|
| Epoch | Step | Training Loss | |
|
|
|:--------:|:----:|:-------------:| |
|
|
| 1.7576 | 50 | 0.0447 | |
|
|
| 3.7576 | 100 | 0.048 | |
|
|
| 5.7576 | 150 | 0.0472 | |
|
|
| 7.7576 | 200 | 0.0505 | |
|
|
| 9.7576 | 250 | 0.0547 | |
|
|
| 11.7576 | 300 | 0.0548 | |
|
|
| 13.7576 | 350 | 0.0527 | |
|
|
| 15.7576 | 400 | 0.0522 | |
|
|
| 17.7576 | 450 | 0.0496 | |
|
|
| 19.7576 | 500 | 0.0506 | |
|
|
| 21.7576 | 550 | 0.048 | |
|
|
| 23.7576 | 600 | 0.0508 | |
|
|
| 25.7576 | 650 | 0.0499 | |
|
|
| 27.7576 | 700 | 0.0474 | |
|
|
| 29.7576 | 750 | 0.0467 | |
|
|
| 31.7576 | 800 | 0.0483 | |
|
|
| 33.7576 | 850 | 0.0438 | |
|
|
| 35.7576 | 900 | 0.0457 | |
|
|
| 37.7576 | 950 | 0.0445 | |
|
|
| 39.7576 | 1000 | 0.0452 | |
|
|
| 41.7576 | 1050 | 0.046 | |
|
|
| 43.7576 | 1100 | 0.0433 | |
|
|
| 45.7576 | 1150 | 0.0419 | |
|
|
| 47.7576 | 1200 | 0.0407 | |
|
|
| 49.7576 | 1250 | 0.0397 | |
|
|
| 51.7576 | 1300 | 0.043 | |
|
|
| 53.7576 | 1350 | 0.0393 | |
|
|
| 55.7576 | 1400 | 0.0411 | |
|
|
| 57.7576 | 1450 | 0.0434 | |
|
|
| 59.7576 | 1500 | 0.0446 | |
|
|
| 61.7576 | 1550 | 0.0396 | |
|
|
| 63.7576 | 1600 | 0.0375 | |
|
|
| 65.7576 | 1650 | 0.0413 | |
|
|
| 67.7576 | 1700 | 0.0398 | |
|
|
| 69.7576 | 1750 | 0.0382 | |
|
|
| 71.7576 | 1800 | 0.0346 | |
|
|
| 73.7576 | 1850 | 0.0388 | |
|
|
| 75.7576 | 1900 | 0.0347 | |
|
|
| 77.7576 | 1950 | 0.0349 | |
|
|
| 79.7576 | 2000 | 0.0402 | |
|
|
| 81.7576 | 2050 | 0.039 | |
|
|
| 83.7576 | 2100 | 0.0343 | |
|
|
| 85.7576 | 2150 | 0.0465 | |
|
|
| 87.7576 | 2200 | 0.033 | |
|
|
| 89.7576 | 2250 | 0.0385 | |
|
|
| 91.7576 | 2300 | 0.0305 | |
|
|
| 93.7576 | 2350 | 0.0367 | |
|
|
| 95.7576 | 2400 | 0.0377 | |
|
|
| 97.7576 | 2450 | 0.0322 | |
|
|
| 99.7576 | 2500 | 0.0354 | |
|
|
| 101.7576 | 2550 | 0.0332 | |
|
|
| 103.7576 | 2600 | 0.0365 | |
|
|
| 105.7576 | 2650 | 0.0357 | |
|
|
| 107.7576 | 2700 | 0.0301 | |
|
|
| 109.7576 | 2750 | 0.0323 | |
|
|
| 111.7576 | 2800 | 0.0328 | |
|
|
| 113.7576 | 2850 | 0.0339 | |
|
|
| 115.7576 | 2900 | 0.0379 | |
|
|
| 117.7576 | 2950 | 0.0334 | |
|
|
| 119.7576 | 3000 | 0.0338 | |
|
|
| 121.7576 | 3050 | 0.0328 | |
|
|
| 123.7576 | 3100 | 0.0281 | |
|
|
| 125.7576 | 3150 | 0.0316 | |
|
|
| 127.7576 | 3200 | 0.0387 | |
|
|
| 129.7576 | 3250 | 0.0327 | |
|
|
| 131.7576 | 3300 | 0.026 | |
|
|
| 133.7576 | 3350 | 0.0247 | |
|
|
| 135.7576 | 3400 | 0.0319 | |
|
|
| 137.7576 | 3450 | 0.0299 | |
|
|
| 139.7576 | 3500 | 0.0252 | |
|
|
| 141.7576 | 3550 | 0.0265 | |
|
|
| 143.7576 | 3600 | 0.0244 | |
|
|
| 145.7576 | 3650 | 0.0317 | |
|
|
| 147.7576 | 3700 | 0.0291 | |
|
|
| 149.7576 | 3750 | 0.03 | |
|
|
| 151.7576 | 3800 | 0.0299 | |
|
|
| 153.7576 | 3850 | 0.0303 | |
|
|
| 155.7576 | 3900 | 0.0296 | |
|
|
| 157.7576 | 3950 | 0.0303 | |
|
|
| 159.7576 | 4000 | 0.0282 | |
|
|
| 161.7576 | 4050 | 0.0301 | |
|
|
| 163.7576 | 4100 | 0.027 | |
|
|
| 165.7576 | 4150 | 0.0259 | |
|
|
| 167.7576 | 4200 | 0.0294 | |
|
|
| 169.7576 | 4250 | 0.0267 | |
|
|
| 171.7576 | 4300 | 0.0303 | |
|
|
| 173.7576 | 4350 | 0.0199 | |
|
|
| 175.7576 | 4400 | 0.0253 | |
|
|
| 177.7576 | 4450 | 0.0254 | |
|
|
| 179.7576 | 4500 | 0.0202 | |
|
|
| 181.7576 | 4550 | 0.0263 | |
|
|
| 183.7576 | 4600 | 0.0302 | |
|
|
| 185.7576 | 4650 | 0.0292 | |
|
|
| 187.7576 | 4700 | 0.0264 | |
|
|
| 189.7576 | 4750 | 0.0289 | |
|
|
| 191.7576 | 4800 | 0.026 | |
|
|
| 193.7576 | 4850 | 0.0285 | |
|
|
| 195.7576 | 4900 | 0.0234 | |
|
|
| 197.7576 | 4950 | 0.0297 | |
|
|
| 199.7576 | 5000 | 0.0238 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.12 |
|
|
- Sentence Transformers: 3.4.1 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.5.2 |
|
|
- Datasets: 3.5.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CustomBatchAllTripletLoss |
|
|
```bibtex |
|
|
@misc{hermans2017defense, |
|
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
|
year={2017}, |
|
|
eprint={1703.07737}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CV} |
|
|
} |
|
|
``` |
|
|
|
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