--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: What is an SRE? Use only Korean in your response and provide a title wrapped in double angular brackets, such as <>. Use the keywords 'indicator', 'objective' and 'management'. - text: 'Who is known as The Invincibles in English football? In football, "The Invincibles" is a nickname used to refer to the Preston North End team of the 1888–89 season, managed by William Sudell, and the Arsenal team of the 2003–04 season managed by Arsène Wenger. Preston North End earned the nickname after completing an entire season undefeated in league and cup competition (27 games), while Arsenal were undefeated in the league (38 games) in a run that stretched to a record 49 games. The actual nickname of the Preston team was the "Old Invincibles" but both versions have been in use.' - text: 'Security guard is in the vicinity of at mall. Mountain peak is in the vicinity of at apartment. Napkin holder is in the vicinity of at mall. Store is in the vicinity of at mall. Legal pad is not in the vicinity of desk. Napkin holder is not in the vicinity of at apartment. Room is in the vicinity of store. Legal pad is not in the vicinity of tsunami. Legal pad has property yellow. Desk is in the vicinity of room. Food is in the vicinity of store. Legal pad is not in the vicinity of haystack. Store is in the vicinity of at mall. What do you think about that statement?' - text: 'How much PVC produced each year? Polyvinyl chloride (alternatively: poly(vinyl chloride), colloquial: polyvinyl, or simply vinyl; abbreviated: PVC) is the world''s third-most widely produced synthetic polymer of plastic (after polyethylene and polypropylene). About 40 million tons of PVC are produced each year.' - text: 'What is Bubble tea? Bubble tea (also known as pearl milk tea, bubble milk tea, tapioca milk tea, boba tea, or boba; Chinese: 珍珠奶茶; pinyin: zhēnzhū nǎichá, 波霸奶茶; bōbà nǎichá) is a tea-based drink that originated in Taiwan in the early 1980s. Taiwanese immigrants brought it to the United States in the 1990s, initially in California through regions like Los Angeles County, but the drink has also spread to other countries where there is a large East Asian diaspora population. Bubble tea most commonly consists of tea accompanied by chewy tapioca balls ("boba" or "pearls"), but it can be made with other toppings as well, such as grass jelly, aloe vera, red bean, or popping boba. It has many varieties and flavors, but the two most popular varieties are pearl black milk tea and pearl green milk tea ("pearl" signifies the tapioca balls at the bottom).' metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: NovaSearch/stella_en_400M_v5 --- # SetFit with NovaSearch/stella_en_400M_v5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 7 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:--------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Extraction | | | Math | | | Brainstorming | | | Factual QA | | | Generation | | | Coding | | | Reasoning | | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("What is an SRE? Use only Korean in your response and provide a title wrapped in double angular brackets, such as <>. Use the keywords 'indicator', 'objective' and 'management'.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:-----| | Word count | 3 | 110.8976 | 8430 | | Label | Training Sample Count | |:--------------|:----------------------| | Brainstorming | 250 | | Coding | 253 | | Extraction | 250 | | Factual QA | 255 | | Generation | 250 | | Math | 250 | | Reasoning | 250 | ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (1, 15) - max_steps: 500 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.0001 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: True - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - run_name: stella_en_400M_v5 - evaluation_strategy: no - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-----:|:----:|:-------------:|:---------------:| | 0.002 | 1 | 0.2869 | - | | 0.004 | 2 | 0.1469 | - | | 0.006 | 3 | 0.2431 | - | | 0.008 | 4 | 0.3568 | - | | 0.01 | 5 | 0.2769 | - | | 0.012 | 6 | 0.2425 | - | | 0.014 | 7 | 0.2001 | - | | 0.016 | 8 | 0.2825 | - | | 0.018 | 9 | 0.2433 | - | | 0.02 | 10 | 0.3096 | - | | 0.022 | 11 | 0.2856 | - | | 0.024 | 12 | 0.265 | - | | 0.026 | 13 | 0.2476 | - | | 0.028 | 14 | 0.1764 | - | | 0.03 | 15 | 0.1491 | - | | 0.032 | 16 | 0.3051 | - | | 0.034 | 17 | 0.2445 | - | | 0.036 | 18 | 0.249 | - | | 0.038 | 19 | 0.1981 | - | | 0.04 | 20 | 0.1892 | - | | 0.042 | 21 | 0.1933 | - | | 0.044 | 22 | 0.2331 | - | | 0.046 | 23 | 0.2145 | - | | 0.048 | 24 | 0.1708 | - | | 0.05 | 25 | 0.2272 | - | | 0.052 | 26 | 0.1714 | - | | 0.054 | 27 | 0.2138 | - | | 0.056 | 28 | 0.2178 | - | | 0.058 | 29 | 0.1346 | - | | 0.06 | 30 | 0.1939 | - | | 0.062 | 31 | 0.1632 | - | | 0.064 | 32 | 0.1934 | - | | 0.066 | 33 | 0.1897 | - | | 0.068 | 34 | 0.1558 | - | | 0.07 | 35 | 0.1568 | - | | 0.072 | 36 | 0.1116 | - | | 0.074 | 37 | 0.1609 | - | | 0.076 | 38 | 0.1294 | - | | 0.078 | 39 | 0.1511 | - | | 0.08 | 40 | 0.1654 | - | | 0.082 | 41 | 0.1542 | - | | 0.084 | 42 | 0.0887 | - | | 0.086 | 43 | 0.0811 | - | | 0.088 | 44 | 0.0991 | - | | 0.09 | 45 | 0.0845 | - | | 0.092 | 46 | 0.0875 | - | | 0.094 | 47 | 0.0338 | - | | 0.096 | 48 | 0.0945 | - | | 0.098 | 49 | 0.0477 | - | | 0.1 | 50 | 0.0696 | - | | 0.102 | 51 | 0.136 | - | | 0.104 | 52 | 0.099 | - | | 0.106 | 53 | 0.0371 | - | | 0.108 | 54 | 0.0513 | - | | 0.11 | 55 | 0.0484 | - | | 0.112 | 56 | 0.0194 | - | | 0.114 | 57 | 0.0601 | - | | 0.116 | 58 | 0.1149 | - | | 0.118 | 59 | 0.0836 | - | | 0.12 | 60 | 0.0865 | - | | 0.122 | 61 | 0.0659 | - | | 0.124 | 62 | 0.0849 | - | | 0.126 | 63 | 0.0963 | - | | 0.128 | 64 | 0.07 | - | | 0.13 | 65 | 0.0233 | - | | 0.132 | 66 | 0.1248 | - | | 0.134 | 67 | 0.0561 | - | | 0.136 | 68 | 0.0851 | - | | 0.138 | 69 | 0.0638 | - | | 0.14 | 70 | 0.0498 | - | | 0.142 | 71 | 0.0311 | - | | 0.144 | 72 | 0.1374 | - | | 0.146 | 73 | 0.0502 | - | | 0.148 | 74 | 0.0605 | - | | 0.15 | 75 | 0.0137 | - | | 0.152 | 76 | 0.065 | - | | 0.154 | 77 | 0.0846 | - | | 0.156 | 78 | 0.0347 | - | | 0.158 | 79 | 0.0517 | - | | 0.16 | 80 | 0.1447 | - | | 0.162 | 81 | 0.0609 | - | | 0.164 | 82 | 0.1423 | - | | 0.166 | 83 | 0.0917 | - | | 0.168 | 84 | 0.226 | - | | 0.17 | 85 | 0.0595 | - | | 0.172 | 86 | 0.0588 | - | | 0.174 | 87 | 0.0228 | - | | 0.176 | 88 | 0.0925 | - | | 0.178 | 89 | 0.0595 | - | | 0.18 | 90 | 0.044 | - | | 0.182 | 91 | 0.0244 | - | | 0.184 | 92 | 0.0939 | - | | 0.186 | 93 | 0.0794 | - | | 0.188 | 94 | 0.0501 | - | | 0.19 | 95 | 0.1363 | - | | 0.192 | 96 | 0.0502 | - | | 0.194 | 97 | 0.0498 | - | | 0.196 | 98 | 0.0562 | - | | 0.198 | 99 | 0.0657 | - | | 0.2 | 100 | 0.0397 | - | | 0.202 | 101 | 0.0305 | - | | 0.204 | 102 | 0.0559 | - | | 0.206 | 103 | 0.0871 | - | | 0.208 | 104 | 0.063 | - | | 0.21 | 105 | 0.0143 | - | | 0.212 | 106 | 0.0706 | - | | 0.214 | 107 | 0.0627 | - | | 0.216 | 108 | 0.1047 | - | | 0.218 | 109 | 0.0487 | - | | 0.22 | 110 | 0.0086 | - | | 0.222 | 111 | 0.0562 | - | | 0.224 | 112 | 0.0101 | - | | 0.226 | 113 | 0.0235 | - | | 0.228 | 114 | 0.0511 | - | | 0.23 | 115 | 0.0295 | - | | 0.232 | 116 | 0.0549 | - | | 0.234 | 117 | 0.0554 | - | | 0.236 | 118 | 0.0301 | - | | 0.238 | 119 | 0.0152 | - | | 0.24 | 120 | 0.0234 | - | | 0.242 | 121 | 0.01 | - | | 0.244 | 122 | 0.0372 | - | | 0.246 | 123 | 0.0085 | - | | 0.248 | 124 | 0.0205 | - | | 0.25 | 125 | 0.0117 | - | | 0.252 | 126 | 0.0039 | - | | 0.254 | 127 | 0.0178 | - | | 0.256 | 128 | 0.0276 | - | | 0.258 | 129 | 0.0592 | - | | 0.26 | 130 | 0.0143 | - | | 0.262 | 131 | 0.0667 | - | | 0.264 | 132 | 0.0059 | - | | 0.266 | 133 | 0.0767 | - | | 0.268 | 134 | 0.0088 | - | | 0.27 | 135 | 0.0034 | - | | 0.272 | 136 | 0.0031 | - | | 0.274 | 137 | 0.0151 | - | | 0.276 | 138 | 0.0072 | - | | 0.278 | 139 | 0.0033 | - | | 0.28 | 140 | 0.0188 | - | | 0.282 | 141 | 0.0069 | - | | 0.284 | 142 | 0.1552 | - | | 0.286 | 143 | 0.0618 | - | | 0.288 | 144 | 0.0043 | - | | 0.29 | 145 | 0.0209 | - | | 0.292 | 146 | 0.0094 | - | | 0.294 | 147 | 0.0191 | - | | 0.296 | 148 | 0.0119 | - | | 0.298 | 149 | 0.0012 | - | | 0.3 | 150 | 0.0014 | - | | 0.302 | 151 | 0.0121 | - | | 0.304 | 152 | 0.0018 | - | | 0.306 | 153 | 0.0792 | - | | 0.308 | 154 | 0.0027 | - | | 0.31 | 155 | 0.0035 | - | | 0.312 | 156 | 0.0009 | - | | 0.314 | 157 | 0.0014 | - | | 0.316 | 158 | 0.0068 | - | | 0.318 | 159 | 0.0025 | - | | 0.32 | 160 | 0.003 | - | | 0.322 | 161 | 0.0116 | - | | 0.324 | 162 | 0.0009 | - | | 0.326 | 163 | 0.0404 | - | | 0.328 | 164 | 0.0022 | - | | 0.33 | 165 | 0.0011 | - | | 0.332 | 166 | 0.0122 | - | | 0.334 | 167 | 0.0006 | - | | 0.336 | 168 | 0.0138 | - | | 0.338 | 169 | 0.0101 | - | | 0.34 | 170 | 0.0019 | - | | 0.342 | 171 | 0.0033 | - | | 0.344 | 172 | 0.0035 | - | | 0.346 | 173 | 0.007 | - | | 0.348 | 174 | 0.0008 | - | | 0.35 | 175 | 0.002 | - | | 0.352 | 176 | 0.0006 | - | | 0.354 | 177 | 0.001 | - | | 0.356 | 178 | 0.0011 | - | | 0.358 | 179 | 0.0057 | - | | 0.36 | 180 | 0.0003 | - | | 0.362 | 181 | 0.001 | - | | 0.364 | 182 | 0.0007 | - | | 0.366 | 183 | 0.0016 | - | | 0.368 | 184 | 0.0018 | - | | 0.37 | 185 | 0.001 | - | | 0.372 | 186 | 0.0009 | - | | 0.374 | 187 | 0.0057 | - | | 0.376 | 188 | 0.0008 | - | | 0.378 | 189 | 0.0182 | - | | 0.38 | 190 | 0.0005 | - | | 0.382 | 191 | 0.053 | - | | 0.384 | 192 | 0.0012 | - | | 0.386 | 193 | 0.0158 | - | | 0.388 | 194 | 0.0043 | - | | 0.39 | 195 | 0.0074 | - | | 0.392 | 196 | 0.0013 | - | | 0.394 | 197 | 0.0016 | - | | 0.396 | 198 | 0.0021 | - | | 0.398 | 199 | 0.0007 | - | | 0.4 | 200 | 0.002 | - | | 0.402 | 201 | 0.0004 | - | | 0.404 | 202 | 0.0008 | - | | 0.406 | 203 | 0.0002 | - | | 0.408 | 204 | 0.0026 | - | | 0.41 | 205 | 0.0012 | - | | 0.412 | 206 | 0.0004 | - | | 0.414 | 207 | 0.0017 | - | | 0.416 | 208 | 0.0038 | - | | 0.418 | 209 | 0.0008 | - | | 0.42 | 210 | 0.0008 | - | | 0.422 | 211 | 0.0007 | - | | 0.424 | 212 | 0.0577 | - | | 0.426 | 213 | 0.0013 | - | | 0.428 | 214 | 0.0005 | - | | 0.43 | 215 | 0.0015 | - | | 0.432 | 216 | 0.0006 | - | | 0.434 | 217 | 0.0005 | - | | 0.436 | 218 | 0.0017 | - | | 0.438 | 219 | 0.001 | - | | 0.44 | 220 | 0.0002 | - | | 0.442 | 221 | 0.0005 | - | | 0.444 | 222 | 0.003 | - | | 0.446 | 223 | 0.0007 | - | | 0.448 | 224 | 0.0002 | - | | 0.45 | 225 | 0.001 | - | | 0.452 | 226 | 0.0006 | - | | 0.454 | 227 | 0.001 | - | | 0.456 | 228 | 0.0506 | - | | 0.458 | 229 | 0.0005 | - | | 0.46 | 230 | 0.0009 | - | | 0.462 | 231 | 0.0015 | - | | 0.464 | 232 | 0.0003 | - | | 0.466 | 233 | 0.0004 | - | | 0.468 | 234 | 0.001 | - | | 0.47 | 235 | 0.0004 | - | | 0.472 | 236 | 0.0007 | - | | 0.474 | 237 | 0.0014 | - | | 0.476 | 238 | 0.0003 | - | | 0.478 | 239 | 0.0004 | - | | 0.48 | 240 | 0.0007 | - | | 0.482 | 241 | 0.0002 | - | | 0.484 | 242 | 0.0006 | - | | 0.486 | 243 | 0.0003 | - | | 0.488 | 244 | 0.0004 | - | | 0.49 | 245 | 0.0587 | - | | 0.492 | 246 | 0.0003 | - | | 0.494 | 247 | 0.0007 | - | | 0.496 | 248 | 0.0013 | - | | 0.498 | 249 | 0.0507 | - | | 0.5 | 250 | 0.0002 | - | | 0.502 | 251 | 0.0004 | - | | 0.504 | 252 | 0.0003 | - | | 0.506 | 253 | 0.0004 | - | | 0.508 | 254 | 0.0002 | - | | 0.51 | 255 | 0.0003 | - | | 0.512 | 256 | 0.0096 | - | | 0.514 | 257 | 0.0002 | - | | 0.516 | 258 | 0.0003 | - | | 0.518 | 259 | 0.0003 | - | | 0.52 | 260 | 0.0013 | - | | 0.522 | 261 | 0.0004 | - | | 0.524 | 262 | 0.0004 | - | | 0.526 | 263 | 0.0007 | - | | 0.528 | 264 | 0.0006 | - | | 0.53 | 265 | 0.0003 | - | | 0.532 | 266 | 0.0023 | - | | 0.534 | 267 | 0.0008 | - | | 0.536 | 268 | 0.0002 | - | | 0.538 | 269 | 0.0018 | - | | 0.54 | 270 | 0.0002 | - | | 0.542 | 271 | 0.0007 | - | | 0.544 | 272 | 0.0001 | - | | 0.546 | 273 | 0.0004 | - | | 0.548 | 274 | 0.0618 | - | | 0.55 | 275 | 0.0192 | - | | 0.552 | 276 | 0.0009 | - | | 0.554 | 277 | 0.0142 | - | | 0.556 | 278 | 0.0014 | - | | 0.558 | 279 | 0.0006 | - | | 0.56 | 280 | 0.0565 | - | | 0.562 | 281 | 0.0006 | - | | 0.564 | 282 | 0.0233 | - | | 0.566 | 283 | 0.0004 | - | | 0.568 | 284 | 0.0116 | - | | 0.57 | 285 | 0.0002 | - | | 0.572 | 286 | 0.0032 | - | | 0.574 | 287 | 0.0001 | - | | 0.576 | 288 | 0.0003 | - | | 0.578 | 289 | 0.0004 | - | | 0.58 | 290 | 0.0003 | - | | 0.582 | 291 | 0.0003 | - | | 0.584 | 292 | 0.0003 | - | | 0.586 | 293 | 0.0012 | - | | 0.588 | 294 | 0.0021 | - | | 0.59 | 295 | 0.0002 | - | | 0.592 | 296 | 0.0003 | - | | 0.594 | 297 | 0.0022 | - | | 0.596 | 298 | 0.0005 | - | | 0.598 | 299 | 0.0005 | - | | 0.6 | 300 | 0.0024 | - | | 0.602 | 301 | 0.0008 | - | | 0.604 | 302 | 0.0003 | - | | 0.606 | 303 | 0.0022 | - | | 0.608 | 304 | 0.0069 | - | | 0.61 | 305 | 0.0009 | - | | 0.612 | 306 | 0.0144 | - | | 0.614 | 307 | 0.0004 | - | | 0.616 | 308 | 0.0006 | - | | 0.618 | 309 | 0.0006 | - | | 0.62 | 310 | 0.0261 | - | | 0.622 | 311 | 0.0002 | - | | 0.624 | 312 | 0.0003 | - | | 0.626 | 313 | 0.0003 | - | | 0.628 | 314 | 0.0007 | - | | 0.63 | 315 | 0.0603 | - | | 0.632 | 316 | 0.0002 | - | | 0.634 | 317 | 0.0003 | - | | 0.636 | 318 | 0.0007 | - | | 0.638 | 319 | 0.0006 | - | | 0.64 | 320 | 0.0002 | - | | 0.642 | 321 | 0.0016 | - | | 0.644 | 322 | 0.0003 | - | | 0.646 | 323 | 0.0003 | - | | 0.648 | 324 | 0.0002 | - | | 0.65 | 325 | 0.0006 | - | | 0.652 | 326 | 0.0006 | - | | 0.654 | 327 | 0.0006 | - | | 0.656 | 328 | 0.0002 | - | | 0.658 | 329 | 0.0004 | - | | 0.66 | 330 | 0.0002 | - | | 0.662 | 331 | 0.0002 | - | | 0.664 | 332 | 0.0001 | - | | 0.666 | 333 | 0.0466 | - | | 0.668 | 334 | 0.0002 | - | | 0.67 | 335 | 0.0003 | - | | 0.672 | 336 | 0.0005 | - | | 0.674 | 337 | 0.0013 | - | | 0.676 | 338 | 0.0002 | - | | 0.678 | 339 | 0.0004 | - | | 0.68 | 340 | 0.0573 | - | | 0.682 | 341 | 0.0001 | - | | 0.684 | 342 | 0.0002 | - | | 0.686 | 343 | 0.0002 | - | | 0.688 | 344 | 0.0009 | - | | 0.69 | 345 | 0.024 | - | | 0.692 | 346 | 0.0003 | - | | 0.694 | 347 | 0.0011 | - | | 0.696 | 348 | 0.0002 | - | | 0.698 | 349 | 0.0191 | - | | 0.7 | 350 | 0.0001 | - | | 0.702 | 351 | 0.0002 | - | | 0.704 | 352 | 0.0009 | - | | 0.706 | 353 | 0.0004 | - | | 0.708 | 354 | 0.0001 | - | | 0.71 | 355 | 0.0 | - | | 0.712 | 356 | 0.0002 | - | | 0.714 | 357 | 0.0002 | - | | 0.716 | 358 | 0.0009 | - | | 0.718 | 359 | 0.0005 | - | | 0.72 | 360 | 0.0013 | - | | 0.722 | 361 | 0.0046 | - | | 0.724 | 362 | 0.0001 | - | | 0.726 | 363 | 0.0005 | - | | 0.728 | 364 | 0.0002 | - | | 0.73 | 365 | 0.0017 | - | | 0.732 | 366 | 0.0332 | - | | 0.734 | 367 | 0.0004 | - | | 0.736 | 368 | 0.0203 | - | | 0.738 | 369 | 0.0003 | - | | 0.74 | 370 | 0.0001 | - | | 0.742 | 371 | 0.0003 | - | | 0.744 | 372 | 0.0004 | - | | 0.746 | 373 | 0.0133 | - | | 0.748 | 374 | 0.0009 | - | | 0.75 | 375 | 0.0017 | - | | 0.752 | 376 | 0.0016 | - | | 0.754 | 377 | 0.0022 | - | | 0.756 | 378 | 0.0015 | - | | 0.758 | 379 | 0.0004 | - | | 0.76 | 380 | 0.0002 | - | | 0.762 | 381 | 0.0001 | - | | 0.764 | 382 | 0.0004 | - | | 0.766 | 383 | 0.0001 | - | | 0.768 | 384 | 0.0012 | - | | 0.77 | 385 | 0.0005 | - | | 0.772 | 386 | 0.0018 | - | | 0.774 | 387 | 0.032 | - | | 0.776 | 388 | 0.0002 | - | | 0.778 | 389 | 0.0001 | - | | 0.78 | 390 | 0.0019 | - | | 0.782 | 391 | 0.001 | - | | 0.784 | 392 | 0.0003 | - | | 0.786 | 393 | 0.0001 | - | | 0.788 | 394 | 0.0005 | - | | 0.79 | 395 | 0.0016 | - | | 0.792 | 396 | 0.0005 | - | | 0.794 | 397 | 0.0018 | - | | 0.796 | 398 | 0.0007 | - | | 0.798 | 399 | 0.0002 | - | | 0.8 | 400 | 0.0004 | - | | 0.802 | 401 | 0.0002 | - | | 0.804 | 402 | 0.001 | - | | 0.806 | 403 | 0.0001 | - | | 0.808 | 404 | 0.0002 | - | | 0.81 | 405 | 0.0002 | - | | 0.812 | 406 | 0.0004 | - | | 0.814 | 407 | 0.0003 | - | | 0.816 | 408 | 0.0001 | - | | 0.818 | 409 | 0.0004 | - | | 0.82 | 410 | 0.001 | - | | 0.822 | 411 | 0.0005 | - | | 0.824 | 412 | 0.0001 | - | | 0.826 | 413 | 0.0002 | - | | 0.828 | 414 | 0.0001 | - | | 0.83 | 415 | 0.0004 | - | | 0.832 | 416 | 0.0002 | - | | 0.834 | 417 | 0.0002 | - | | 0.836 | 418 | 0.0001 | - | | 0.838 | 419 | 0.0002 | - | | 0.84 | 420 | 0.0011 | - | | 0.842 | 421 | 0.0002 | - | | 0.844 | 422 | 0.0003 | - | | 0.846 | 423 | 0.0002 | - | | 0.848 | 424 | 0.0004 | - | | 0.85 | 425 | 0.0002 | - | | 0.852 | 426 | 0.0002 | - | | 0.854 | 427 | 0.0501 | - | | 0.856 | 428 | 0.0001 | - | | 0.858 | 429 | 0.0002 | - | | 0.86 | 430 | 0.0004 | - | | 0.862 | 431 | 0.0003 | - | | 0.864 | 432 | 0.0001 | - | | 0.866 | 433 | 0.0001 | - | | 0.868 | 434 | 0.0001 | - | | 0.87 | 435 | 0.0002 | - | | 0.872 | 436 | 0.0008 | - | | 0.874 | 437 | 0.0001 | - | | 0.876 | 438 | 0.0002 | - | | 0.878 | 439 | 0.0002 | - | | 0.88 | 440 | 0.0004 | - | | 0.882 | 441 | 0.0002 | - | | 0.884 | 442 | 0.0002 | - | | 0.886 | 443 | 0.0001 | - | | 0.888 | 444 | 0.0006 | - | | 0.89 | 445 | 0.0002 | - | | 0.892 | 446 | 0.0003 | - | | 0.894 | 447 | 0.0002 | - | | 0.896 | 448 | 0.0011 | - | | 0.898 | 449 | 0.0002 | - | | 0.9 | 450 | 0.0004 | - | | 0.902 | 451 | 0.0001 | - | | 0.904 | 452 | 0.0009 | - | | 0.906 | 453 | 0.0001 | - | | 0.908 | 454 | 0.0003 | - | | 0.91 | 455 | 0.0006 | - | | 0.912 | 456 | 0.0028 | - | | 0.914 | 457 | 0.0002 | - | | 0.916 | 458 | 0.0001 | - | | 0.918 | 459 | 0.0002 | - | | 0.92 | 460 | 0.0002 | - | | 0.922 | 461 | 0.0004 | - | | 0.924 | 462 | 0.0001 | - | | 0.926 | 463 | 0.0001 | - | | 0.928 | 464 | 0.0001 | - | | 0.93 | 465 | 0.002 | - | | 0.932 | 466 | 0.0003 | - | | 0.934 | 467 | 0.0006 | - | | 0.936 | 468 | 0.0001 | - | | 0.938 | 469 | 0.0002 | - | | 0.94 | 470 | 0.0002 | - | | 0.942 | 471 | 0.0001 | - | | 0.944 | 472 | 0.0002 | - | | 0.946 | 473 | 0.0003 | - | | 0.948 | 474 | 0.0003 | - | | 0.95 | 475 | 0.001 | - | | 0.952 | 476 | 0.0002 | - | | 0.954 | 477 | 0.0001 | - | | 0.956 | 478 | 0.0003 | - | | 0.958 | 479 | 0.0002 | - | | 0.96 | 480 | 0.0487 | - | | 0.962 | 481 | 0.0002 | - | | 0.964 | 482 | 0.0004 | - | | 0.966 | 483 | 0.0002 | - | | 0.968 | 484 | 0.0001 | - | | 0.97 | 485 | 0.0003 | - | | 0.972 | 486 | 0.0002 | - | | 0.974 | 487 | 0.0003 | - | | 0.976 | 488 | 0.0088 | - | | 0.978 | 489 | 0.0003 | - | | 0.98 | 490 | 0.0011 | - | | 0.982 | 491 | 0.0003 | - | | 0.984 | 492 | 0.0001 | - | | 0.986 | 493 | 0.0001 | - | | 0.988 | 494 | 0.0003 | - | | 0.99 | 495 | 0.0002 | - | | 0.992 | 496 | 0.0004 | - | | 0.994 | 497 | 0.0003 | - | | 0.996 | 498 | 0.0001 | - | | 0.998 | 499 | 0.0002 | - | | 1.0 | 500 | 0.0002 | - | ### Framework Versions - Python: 3.11.3 - SetFit: 1.2.0.dev0 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```