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--- |
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language: [] |
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library_name: sentence-transformers |
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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:12822 |
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- loss:BatchAllTripletLoss |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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datasets: [] |
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widget: |
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- source_sentence: parcel-packing and gift-wrapping |
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sentences: |
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- retail sale of cleaning products, e |
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- cafeterias |
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- ' ' |
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- source_sentence: Sprzedaż detaliczna mięsa i wyrobów z mięsa |
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sentences: |
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- ' ' |
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- ' revenues from sale of advertising space' |
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- g |
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- source_sentence: g |
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sentences: |
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- installation of the system and provision of training and support to users of the |
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system- activities of auditing and certification of computing and data processing |
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infrastructures and services |
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- ' revenues from sale of advertising space' |
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- 47.75 Retail sale of cosmetic and toilet articles |
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- source_sentence: lighterage, salvage activities |
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sentences: |
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- hairstyling |
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- ' this class also includes: cladding of metal pipes with plastics' |
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- usługi pośrednictwa w zakresie transportu pasażerskiego |
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- source_sentence: manufacture of glass mirrors |
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sentences: |
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- manufacture of electroplating machinery |
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- ' protective face shields/visors, of plastics, e' |
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- cow peas |
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pipeline_tag: sentence-similarity |
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--- |
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# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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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': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("annazdr/nace-pl-v2") |
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# Run inference |
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sentences = [ |
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'manufacture of glass mirrors', |
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' protective face shields/visors, of plastics, e', |
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'manufacture of electroplating machinery', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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: 12,822 training samples |
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* Columns: <code>sentence_0</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 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| type | string | int | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 15.14 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~0.20%</li><li>1: ~0.10%</li><li>2: ~0.20%</li><li>4: ~0.30%</li><li>5: ~0.10%</li><li>6: ~0.10%</li><li>7: ~0.40%</li><li>9: ~0.10%</li><li>10: ~0.60%</li><li>11: ~0.20%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.10%</li><li>15: ~0.10%</li><li>16: ~0.40%</li><li>17: ~0.10%</li><li>18: ~0.40%</li><li>20: ~0.40%</li><li>22: ~0.30%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.40%</li><li>27: ~0.20%</li><li>28: ~0.10%</li><li>30: ~0.10%</li><li>32: ~0.10%</li><li>33: ~0.20%</li><li>34: ~0.10%</li><li>35: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>41: ~0.20%</li><li>42: ~0.10%</li><li>43: ~0.20%</li><li>44: ~0.50%</li><li>46: ~0.10%</li><li>48: ~0.20%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.40%</li><li>53: ~0.30%</li><li>54: ~0.20%</li><li>55: ~0.20%</li><li>56: ~0.20%</li><li>58: ~0.20%</li><li>59: ~0.10%</li><li>60: ~0.30%</li><li>61: ~0.20%</li><li>63: ~0.40%</li><li>64: ~0.30%</li><li>65: ~0.10%</li><li>66: ~0.70%</li><li>68: ~0.10%</li><li>69: ~0.20%</li><li>70: ~0.50%</li><li>71: ~0.30%</li><li>72: ~0.10%</li><li>73: ~0.40%</li><li>74: ~0.20%</li><li>75: ~0.30%</li><li>76: ~0.20%</li><li>78: ~0.10%</li><li>79: ~0.10%</li><li>80: ~0.10%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.10%</li><li>85: ~0.20%</li><li>86: ~0.20%</li><li>89: ~0.10%</li><li>90: ~0.10%</li><li>91: ~0.30%</li><li>92: ~0.20%</li><li>93: ~0.10%</li><li>94: ~0.30%</li><li>95: ~0.20%</li><li>96: ~0.20%</li><li>97: ~0.40%</li><li>98: ~0.70%</li><li>99: ~0.20%</li><li>100: ~0.50%</li><li>101: ~0.20%</li><li>102: ~0.10%</li><li>103: ~0.10%</li><li>104: ~0.20%</li><li>106: ~0.10%</li><li>108: ~0.20%</li><li>110: ~0.10%</li><li>111: ~0.10%</li><li>112: ~0.20%</li><li>115: ~0.10%</li><li>116: ~0.10%</li><li>119: ~0.30%</li><li>120: ~0.10%</li><li>121: ~0.20%</li><li>123: ~0.10%</li><li>125: ~0.20%</li><li>126: ~0.10%</li><li>127: ~0.20%</li><li>128: ~0.40%</li><li>130: ~0.20%</li><li>134: ~0.10%</li><li>135: ~0.10%</li><li>136: ~0.10%</li><li>138: ~0.10%</li><li>139: ~0.10%</li><li>140: ~0.20%</li><li>141: ~0.10%</li><li>142: ~0.10%</li><li>143: ~0.40%</li><li>144: ~0.10%</li><li>148: ~0.10%</li><li>149: ~0.10%</li><li>150: ~0.30%</li><li>151: ~0.10%</li><li>152: ~0.30%</li><li>153: ~0.40%</li><li>154: ~0.50%</li><li>156: ~0.10%</li><li>157: ~0.30%</li><li>158: ~0.20%</li><li>159: ~0.30%</li><li>160: ~0.10%</li><li>161: ~0.10%</li><li>162: ~0.10%</li><li>163: ~0.10%</li><li>165: ~0.10%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>170: ~0.10%</li><li>171: ~0.10%</li><li>172: ~0.10%</li><li>173: ~0.10%</li><li>174: ~0.20%</li><li>176: ~0.20%</li><li>178: ~0.10%</li><li>179: ~0.10%</li><li>181: ~0.10%</li><li>182: ~0.30%</li><li>183: ~0.30%</li><li>184: ~0.20%</li><li>185: ~0.30%</li><li>186: ~0.40%</li><li>187: ~0.20%</li><li>188: ~0.40%</li><li>189: ~0.20%</li><li>190: ~0.50%</li><li>191: ~0.30%</li><li>192: ~0.40%</li><li>193: ~0.10%</li><li>196: ~0.20%</li><li>197: ~0.20%</li><li>198: ~0.30%</li><li>199: ~0.60%</li><li>200: ~0.50%</li><li>201: ~0.10%</li><li>202: ~0.10%</li><li>203: ~0.30%</li><li>204: ~0.10%</li><li>205: ~0.30%</li><li>206: ~0.40%</li><li>208: ~0.20%</li><li>210: ~0.20%</li><li>211: ~0.40%</li><li>212: ~0.20%</li><li>214: ~0.30%</li><li>215: ~0.10%</li><li>217: ~0.30%</li><li>218: ~0.20%</li><li>220: ~0.30%</li><li>221: ~0.10%</li><li>222: ~0.20%</li><li>223: ~0.10%</li><li>225: ~0.10%</li><li>226: ~0.10%</li><li>227: ~0.20%</li><li>228: ~0.10%</li><li>230: ~0.10%</li><li>231: ~0.30%</li><li>233: ~0.10%</li><li>234: ~0.10%</li><li>235: ~0.20%</li><li>236: ~0.20%</li><li>237: ~0.20%</li><li>238: ~0.30%</li><li>239: ~0.10%</li><li>240: ~0.10%</li><li>241: ~0.20%</li><li>242: ~0.10%</li><li>243: ~0.40%</li><li>244: ~0.40%</li><li>245: ~0.20%</li><li>246: ~0.20%</li><li>247: ~0.30%</li><li>248: ~0.20%</li><li>249: ~0.20%</li><li>250: ~0.10%</li><li>253: ~0.30%</li><li>254: ~0.50%</li><li>255: ~0.30%</li><li>256: ~0.20%</li><li>257: ~0.20%</li><li>258: ~0.20%</li><li>259: ~0.10%</li><li>260: ~0.60%</li><li>261: ~0.10%</li><li>262: ~0.10%</li><li>264: ~0.30%</li><li>266: ~0.10%</li><li>267: ~0.10%</li><li>269: ~0.20%</li><li>271: ~0.10%</li><li>272: ~0.10%</li><li>273: ~0.10%</li><li>274: ~0.40%</li><li>275: ~0.10%</li><li>276: ~0.30%</li><li>277: ~0.20%</li><li>278: ~0.10%</li><li>279: ~0.20%</li><li>281: ~0.10%</li><li>283: ~0.40%</li><li>284: ~0.10%</li><li>285: ~0.20%</li><li>286: ~0.10%</li><li>287: ~0.20%</li><li>289: ~0.20%</li><li>290: ~0.20%</li><li>291: ~0.20%</li><li>292: ~0.30%</li><li>293: ~0.20%</li><li>294: ~0.20%</li><li>295: ~0.40%</li><li>296: ~0.20%</li><li>297: ~0.20%</li><li>298: ~0.10%</li><li>302: ~0.10%</li><li>303: ~0.10%</li><li>306: ~0.60%</li><li>307: ~0.50%</li><li>310: ~0.40%</li><li>311: ~0.40%</li><li>313: ~0.10%</li><li>314: ~0.40%</li><li>316: ~0.10%</li><li>319: ~0.20%</li><li>320: ~0.10%</li><li>322: ~0.50%</li><li>324: ~0.20%</li><li>325: ~0.30%</li><li>326: ~0.30%</li><li>327: ~0.10%</li><li>328: ~0.10%</li><li>329: ~0.10%</li><li>330: ~0.10%</li><li>331: ~0.10%</li><li>332: ~0.20%</li><li>334: ~0.10%</li><li>336: ~0.30%</li><li>337: ~0.50%</li><li>338: ~0.10%</li><li>341: ~0.10%</li><li>343: ~0.10%</li><li>344: ~0.20%</li><li>347: ~0.20%</li><li>348: ~0.10%</li><li>349: ~0.10%</li><li>350: ~0.50%</li><li>351: ~0.70%</li><li>352: ~0.20%</li><li>353: ~0.10%</li><li>354: ~0.20%</li><li>355: ~0.10%</li><li>356: ~0.10%</li><li>357: ~0.20%</li><li>358: ~0.30%</li><li>359: ~0.10%</li><li>360: ~0.20%</li><li>361: ~0.30%</li><li>362: ~0.10%</li><li>363: ~0.10%</li><li>364: ~0.10%</li><li>365: ~0.30%</li><li>368: ~0.30%</li><li>369: ~0.20%</li><li>372: ~0.30%</li><li>373: ~0.10%</li><li>374: ~0.30%</li><li>375: ~0.70%</li><li>376: ~0.10%</li><li>377: ~0.20%</li><li>378: ~0.20%</li><li>380: ~0.10%</li><li>381: ~0.10%</li><li>382: ~0.20%</li><li>383: ~0.10%</li><li>385: ~0.20%</li><li>393: ~0.10%</li><li>394: ~0.10%</li><li>395: ~0.20%</li><li>396: ~0.30%</li><li>398: ~0.10%</li><li>399: ~0.20%</li><li>401: ~0.20%</li><li>402: ~0.20%</li><li>404: ~0.40%</li><li>405: ~0.10%</li><li>407: ~0.20%</li><li>409: ~0.20%</li><li>410: ~0.10%</li><li>411: ~0.10%</li><li>412: ~0.10%</li><li>413: ~0.20%</li><li>414: ~0.20%</li><li>415: ~0.10%</li><li>416: ~0.10%</li><li>417: ~0.10%</li><li>418: ~0.10%</li><li>419: ~0.20%</li><li>420: ~0.10%</li><li>421: ~0.20%</li><li>423: ~0.30%</li><li>424: ~0.10%</li><li>425: ~0.10%</li><li>427: ~0.20%</li><li>428: ~0.10%</li><li>429: ~0.10%</li><li>430: ~0.10%</li><li>432: ~0.10%</li><li>434: ~0.10%</li><li>435: ~0.40%</li><li>436: ~0.20%</li><li>437: ~0.30%</li><li>438: ~0.20%</li><li>440: ~0.20%</li><li>441: ~0.30%</li><li>442: ~0.20%</li><li>443: ~0.10%</li><li>444: ~0.30%</li><li>445: ~0.20%</li><li>446: ~0.20%</li><li>448: ~0.20%</li><li>449: ~0.30%</li><li>451: ~0.20%</li><li>452: ~0.10%</li><li>454: ~0.20%</li><li>455: ~0.20%</li><li>456: ~0.10%</li><li>458: ~0.30%</li><li>459: ~0.10%</li><li>460: ~0.10%</li><li>462: ~0.10%</li><li>463: ~0.40%</li><li>464: ~0.10%</li><li>465: ~0.20%</li><li>466: ~0.10%</li><li>467: ~0.40%</li><li>468: ~0.10%</li><li>469: ~0.30%</li><li>471: ~0.10%</li><li>475: ~0.30%</li><li>476: ~0.50%</li><li>477: ~0.10%</li><li>479: ~0.40%</li><li>480: ~0.30%</li><li>482: ~0.10%</li><li>483: ~0.30%</li><li>484: ~0.10%</li><li>485: ~0.20%</li><li>486: ~0.10%</li><li>487: ~0.10%</li><li>490: ~0.30%</li><li>491: ~0.40%</li><li>492: ~0.40%</li><li>493: ~0.10%</li><li>494: ~0.10%</li><li>495: ~0.10%</li><li>498: ~0.20%</li><li>499: ~0.40%</li><li>500: ~0.30%</li><li>501: ~0.30%</li><li>502: ~0.30%</li><li>504: ~0.20%</li><li>505: ~0.20%</li><li>506: ~0.10%</li><li>507: ~0.20%</li><li>508: ~0.10%</li><li>511: ~0.10%</li><li>512: ~0.60%</li><li>513: ~0.10%</li><li>515: ~0.10%</li><li>516: ~0.30%</li><li>517: ~0.40%</li><li>519: ~0.30%</li><li>520: ~0.30%</li><li>521: ~0.10%</li><li>522: ~0.20%</li><li>523: ~0.10%</li><li>524: ~0.50%</li><li>525: ~0.60%</li><li>527: ~0.20%</li><li>528: ~0.10%</li><li>530: ~0.10%</li><li>533: ~0.40%</li><li>534: ~0.50%</li><li>535: ~0.40%</li><li>536: ~0.10%</li><li>537: ~0.20%</li><li>538: ~0.40%</li><li>539: ~0.10%</li><li>540: ~0.10%</li><li>542: ~0.30%</li><li>543: ~0.10%</li><li>544: ~0.10%</li><li>545: ~0.20%</li><li>546: ~0.20%</li><li>548: ~0.20%</li><li>549: ~0.20%</li><li>550: ~0.30%</li><li>551: ~0.30%</li><li>552: ~0.10%</li><li>554: ~0.10%</li><li>555: ~0.20%</li><li>557: ~0.20%</li><li>560: ~0.10%</li><li>561: ~0.20%</li><li>562: ~0.10%</li><li>564: ~0.40%</li><li>565: ~0.10%</li><li>566: ~0.10%</li><li>567: ~0.20%</li><li>570: ~0.10%</li><li>572: ~0.30%</li><li>573: ~0.10%</li><li>574: ~0.10%</li><li>575: ~0.10%</li><li>576: ~0.10%</li><li>577: ~0.20%</li><li>578: ~0.50%</li><li>579: ~0.40%</li><li>581: ~0.20%</li><li>585: ~0.40%</li><li>586: ~0.10%</li><li>587: ~0.20%</li><li>588: ~0.20%</li><li>590: ~0.20%</li><li>592: ~0.10%</li><li>595: ~0.10%</li><li>597: ~0.20%</li><li>600: ~0.10%</li><li>601: ~0.10%</li><li>603: ~0.10%</li><li>604: ~0.10%</li><li>608: ~0.10%</li><li>611: ~0.10%</li><li>612: ~0.20%</li><li>613: ~0.10%</li><li>619: ~0.20%</li><li>620: ~0.20%</li><li>622: ~0.10%</li><li>625: ~0.20%</li><li>629: ~0.10%</li><li>631: ~0.20%</li><li>632: ~0.10%</li><li>633: ~0.20%</li><li>634: ~0.10%</li><li>635: ~0.40%</li><li>640: ~0.10%</li><li>643: ~0.10%</li><li>645: ~0.10%</li><li>648: ~0.10%</li></ul> | |
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* Samples: |
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| sentence_0 | label | |
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|:----------------------------------------------------------------------------------|:-----------------| |
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| <code>swimming clubs</code> | <code>475</code> | |
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| <code> </code> | <code>581</code> | |
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| <code>this class includes: mining of ores valued chiefly for iron content</code> | <code>351</code> | |
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* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `num_train_epochs`: 4 |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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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 |
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- `num_train_epochs`: 4 |
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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`: 0.0 |
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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`: False |
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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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- `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`: False |
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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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- `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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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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</details> |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### BatchAllTripletLoss |
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```bibtex |
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@misc{hermans2017defense, |
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title={In Defense of the Triplet Loss for Person Re-Identification}, |
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
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year={2017}, |
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eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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