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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:72
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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: 打放し型枠（B種）
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sentences:
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- 埋込み（B種）（手間）
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- 埋込み（C種）（手間）
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- 盛土A種
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- source_sentence: 埋込み[B種]
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sentences:
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- 打放し型枠（A種）
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- 盛土（C種）（手間）
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- 埋戻し[C種]
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- source_sentence: 盛土[C種]
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sentences:
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- 埋込み[C種]
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- 盛土（A種）
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- 盛土[A種]
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- source_sentence: 埋戻し[A種]
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sentences:
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- 打放し型枠C種
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- 打放し型枠（C種）（損料・手間）
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- 盛土[B種]
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- source_sentence: 埋込み（B種）（損料・手間）
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sentences:
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- 埋戻し（A種）（損料）
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- 埋戻し（C種）（損料・手間）
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- 埋戻し（B種）（手間）
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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_11")
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# Run inference
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sentences = [
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'埋込み（B種）（損料・手間）',
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'埋戻し（A種）（損料）',
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'埋戻し（B種）（手間）',
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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: 72 training samples
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* Columns: <code>sentence</code> and <code>label</code>
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* Approximate statistics based on the first 72 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: 16.21 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.50%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>14: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.50%</li><li>17: ~0.50%</li><li>18: ~0.50%</li><li>19: ~0.50%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>29: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.50%</li><li>32: ~0.50%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>35: ~0.50%</li><li>36: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.50%</li><li>42: ~0.50%</li><li>43: ~0.50%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.50%</li><li>49: ~0.50%</li><li>50: ~0.50%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.80%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.50%</li><li>62: ~0.50%</li><li>63: ~0.50%</li><li>64: ~0.50%</li><li>65: ~0.50%</li><li>66: ~0.50%</li><li>67: ~0.50%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.50%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>76: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.50%</li><li>81: ~0.50%</li><li>82: ~0.50%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>86: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.60%</li><li>89: ~0.50%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.50%</li><li>93: ~0.50%</li><li>94: ~0.50%</li><li>95: ~1.20%</li><li>96: ~1.70%</li><li>97: ~3.90%</li><li>98: ~0.50%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.60%</li><li>102: ~0.50%</li><li>103: ~0.50%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~1.20%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: ~0.50%</li><li>111: ~0.50%</li><li>112: ~0.50%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.50%</li><li>119: ~0.50%</li><li>120: ~0.50%</li><li>121: ~0.50%</li><li>122: ~0.50%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.50%</li><li>133: ~0.50%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.50%</li><li>137: ~0.50%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.50%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.50%</li><li>145: ~0.50%</li><li>146: ~0.70%</li><li>147: ~0.50%</li><li>148: ~3.10%</li><li>149: ~0.50%</li><li>150: ~2.30%</li><li>151: ~0.50%</li><li>152: ~0.50%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.50%</li><li>156: ~0.50%</li><li>157: ~0.50%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.50%</li><li>162: ~0.50%</li><li>163: ~0.50%</li><li>164: ~0.50%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.50%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.50%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.50%</li><li>175: ~0.50%</li><li>176: ~0.50%</li><li>177: ~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>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`: 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`: 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`: 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
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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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- `dispatch_batches`: None
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- `split_batches`: 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
|
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<details><summary>Click to expand</summary>
|
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| Epoch | Step | Training Loss |
|
|
|
|:--------:|:----:|:-------------:|
|
|
|
| 10.0 | 10 | 1.6508 |
|
|
|
| 20.0 | 20 | 1.2554 |
|
|
|
| 30.0 | 30 | 0.8495 |
|
|
|
| 40.0 | 40 | 0.7182 |
|
|
|
| 50.0 | 50 | 0.6614 |
|
|
|
| 60.0 | 60 | 0.575 |
|
|
|
| 70.0 | 70 | 0.5027 |
|
|
|
| 80.0 | 80 | 0.32 |
|
|
|
| 90.0 | 90 | 0.1543 |
|
|
|
| 100.0 | 100 | 0.0102 |
|
|
|
| 110.0 | 110 | 0.012 |
|
|
|
| 120.0 | 120 | 0.1164 |
|
|
|
| 130.0 | 130 | 0.0 |
|
|
|
| 140.0 | 140 | 0.0 |
|
|
|
| 150.0 | 150 | 0.0 |
|
|
|
| 160.0 | 160 | 0.0157 |
|
|
|
| 170.0 | 170 | 0.0794 |
|
|
|
| 180.0 | 180 | 0.0 |
|
|
|
| 190.0 | 190 | 0.0 |
|
|
|
| 200.0 | 200 | 0.0141 |
|
|
|
| 210.0 | 210 | 0.0 |
|
|
|
| 220.0 | 220 | 0.0 |
|
|
|
| 230.0 | 230 | 0.1115 |
|
|
|
| 240.0 | 240 | 0.0 |
|
|
|
| 250.0 | 250 | 0.0 |
|
|
|
| 260.0 | 260 | 0.0 |
|
|
|
| 270.0 | 270 | 0.0 |
|
|
|
| 280.0 | 280 | 0.0 |
|
|
|
| 290.0 | 290 | 0.0 |
|
|
|
| 300.0 | 300 | 0.0 |
|
|
|
| 310.0 | 310 | 0.0 |
|
|
|
| 320.0 | 320 | 0.0 |
|
|
|
| 330.0 | 330 | 0.0 |
|
|
|
| 340.0 | 340 | 0.0 |
|
|
|
| 350.0 | 350 | 0.0 |
|
|
|
| 360.0 | 360 | 0.0197 |
|
|
|
| 370.0 | 370 | 0.0649 |
|
|
|
| 380.0 | 380 | 0.0 |
|
|
|
| 390.0 | 390 | 0.0 |
|
|
|
| 400.0 | 400 | 0.0 |
|
|
|
| 410.0 | 410 | 0.0 |
|
|
|
| 420.0 | 420 | 0.0 |
|
|
|
| 430.0 | 430 | 0.0 |
|
|
|
| 440.0 | 440 | 0.0 |
|
|
|
| 450.0 | 450 | 0.0 |
|
|
|
| 460.0 | 460 | 0.0 |
|
|
|
| 470.0 | 470 | 0.0 |
|
|
|
| 480.0 | 480 | 0.0 |
|
|
|
| 490.0 | 490 | 0.0 |
|
|
|
| 500.0 | 500 | 0.0 |
|
|
|
| 3.1842 | 100 | 0.6748 |
|
|
|
| 6.3684 | 200 | 0.5883 |
|
|
|
| 9.5526 | 300 | 0.5815 |
|
|
|
| 12.7368 | 400 | 0.5338 |
|
|
|
| 16.1053 | 500 | 0.5498 |
|
|
|
| 19.2895 | 600 | 0.5359 |
|
|
|
| 22.4737 | 700 | 0.5359 |
|
|
|
| 25.6579 | 800 | 0.4893 |
|
|
|
| 29.0263 | 900 | 0.4665 |
|
|
|
| 32.2105 | 1000 | 0.4205 |
|
|
|
| 35.3947 | 1100 | 0.4383 |
|
|
|
| 38.5789 | 1200 | 0.4552 |
|
|
|
| 41.7632 | 1300 | 0.4003 |
|
|
|
| 45.1316 | 1400 | 0.3816 |
|
|
|
| 48.3158 | 1500 | 0.3744 |
|
|
|
| 51.5 | 1600 | 0.3504 |
|
|
|
| 54.6842 | 1700 | 0.359 |
|
|
|
| 58.0526 | 1800 | 0.3019 |
|
|
|
| 61.2368 | 1900 | 0.3109 |
|
|
|
| 64.4211 | 2000 | 0.3151 |
|
|
|
| 67.6053 | 2100 | 0.3292 |
|
|
|
| 70.7895 | 2200 | 0.2813 |
|
|
|
| 74.1579 | 2300 | 0.2697 |
|
|
|
| 77.3421 | 2400 | 0.1975 |
|
|
|
| 80.5263 | 2500 | 0.2492 |
|
|
|
| 83.7105 | 2600 | 0.2608 |
|
|
|
| 87.0789 | 2700 | 0.2401 |
|
|
|
| 90.2632 | 2800 | 0.2265 |
|
|
|
| 93.4474 | 2900 | 0.2032 |
|
|
|
| 96.6316 | 3000 | 0.2368 |
|
|
|
| 99.8158 | 3100 | 0.2066 |
|
|
|
| 103.1842 | 3200 | 0.1558 |
|
|
|
| 106.3684 | 3300 | 0.2029 |
|
|
|
| 109.5526 | 3400 | 0.244 |
|
|
|
| 112.7368 | 3500 | 0.1894 |
|
|
|
| 116.1053 | 3600 | 0.193 |
|
|
|
| 119.2895 | 3700 | 0.1769 |
|
|
|
| 122.4737 | 3800 | 0.1821 |
|
|
|
| 125.6579 | 3900 | 0.0912 |
|
|
|
| 129.0263 | 4000 | 0.1834 |
|
|
|
| 132.2105 | 4100 | 0.1391 |
|
|
|
| 135.3947 | 4200 | 0.1718 |
|
|
|
| 138.5789 | 4300 | 0.1585 |
|
|
|
| 141.7632 | 4400 | 0.1829 |
|
|
|
| 145.1316 | 4500 | 0.1246 |
|
|
|
| 148.3158 | 4600 | 0.1327 |
|
|
|
| 151.5 | 4700 | 0.1396 |
|
|
|
| 154.6842 | 4800 | 0.1028 |
|
|
|
| 158.0526 | 4900 | 0.0907 |
|
|
|
| 161.2368 | 5000 | 0.1179 |
|
|
|
| 164.4211 | 5100 | 0.1496 |
|
|
|
| 167.6053 | 5200 | 0.1156 |
|
|
|
| 170.7895 | 5300 | 0.1148 |
|
|
|
| 174.1579 | 5400 | 0.1275 |
|
|
|
| 177.3421 | 5500 | 0.1354 |
|
|
|
| 180.5263 | 5600 | 0.1334 |
|
|
|
| 183.7105 | 5700 | 0.0874 |
|
|
|
| 187.0789 | 5800 | 0.0922 |
|
|
|
| 190.2632 | 5900 | 0.1109 |
|
|
|
| 193.4474 | 6000 | 0.0708 |
|
|
|
| 196.6316 | 6100 | 0.0943 |
|
|
|
| 199.8158 | 6200 | 0.1164 |
|
|
|
| 203.1842 | 6300 | 0.0785 |
|
|
|
| 206.3684 | 6400 | 0.0853 |
|
|
|
| 209.5526 | 6500 | 0.0674 |
|
|
|
| 212.7368 | 6600 | 0.1009 |
|
|
|
| 216.1053 | 6700 | 0.0846 |
|
|
|
| 219.2895 | 6800 | 0.078 |
|
|
|
| 222.4737 | 6900 | 0.0958 |
|
|
|
| 225.6579 | 7000 | 0.0811 |
|
|
|
| 229.0263 | 7100 | 0.0452 |
|
|
|
| 232.2105 | 7200 | 0.0705 |
|
|
|
| 235.3947 | 7300 | 0.0664 |
|
|
|
| 238.5789 | 7400 | 0.0501 |
|
|
|
| 241.7632 | 7500 | 0.0696 |
|
|
|
| 245.1316 | 7600 | 0.0736 |
|
|
|
| 248.3158 | 7700 | 0.08 |
|
|
|
|
|
|
</details>
|
|
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|
|
### Framework Versions
|
|
|
- Python: 3.11.11
|
|
|
- Sentence Transformers: 3.4.1
|
|
|
- Transformers: 4.50.2
|
|
|
- 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",
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### 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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