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SetFit with NovaSearch/stella_en_400M_v5

This is a SetFit model that can be used for Text Classification. This SetFit model uses NovaSearch/stella_en_400M_v5 as the Sentence Transformer embedding model. A 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
Extraction
  • "Please convert the following to a list of unique names of persons mentioned in the article.\n\nHere's the article:\nBut a hotly contested primary is likely to drag the eventual nominee to the right, even on issues that could otherwise favor his party. Mr. DeSantis, widely seen as Mr. Trump’s most serious challenger, signed a ban on abortion in his state after six weeks, a threshold before many women know they are pregnant.\n\nAnd at some point, Republicans’ drive against transgender people and their fixation on social issues may appear to be bullying — or simply far afield from real issues in the lives of swing voters, said Ms. Caprara, the chief of staff for the Illinois governor.\n\n“There’s this toxic soup between abortion, guns, gay rights, library books, African American history,” she said. “It just comes across to people as, ‘Who are these people?’”\n\nThe biggest issue, however, may be the storm cloud on the horizon that may or may not burst — the economy. In 2020, Mr. Biden became one of the few presidential candidates in modern history to have triumphed over the candidate who was more trusted on the economy in polls."
  • "What did the writer say they were probably going to upgrade to?\n\nI've owned almost every nexus and pixel device from google. I still have my nexus 6p lying around somewhere.\nI'm actually pretty happy with my pixel 6p. The battery life is good, the performance is excellent, no issues with the fp sensor, and contrary to other opinions, I quite like android 13.\nI've only had the phone overheat once and that was when it was super hot and humid outside and I was using walking directions with the gps on.\nThe modem isn't the best, but i've never been left without a signal and most of the time I'm close to wifi so its not a huge issue for me.\nI'll probably upgrade to the pixel 7 pro if it has some meaningful improvements to the 6p."
  • "What is the name of the boys' dog? \n\nMike Brady (Robert Reed), a widowed architect with three sons—Greg (Barry Williams), Peter (Christopher Knight), and Bobby (Mike Lookinland)—marries Carol Martin (Florence Henderson), who herself has three daughters: Marcia (Maureen McCormick), Jan (Eve Plumb), and Cindy (Susan Olsen). Carol and her daughters take the Brady surname. Included in the blended family are Mike's live-in housekeeper, Alice Nelson (Ann B. Davis), and the boys' dog, Tiger. (In the pilot episode, the girls also have a pet: a cat named Fluffy. Fluffy never appears in any other episodes.) The setting is a large two-story house designed by Mike, located in a Los Angeles suburb.[4] The show never addressed what happened to Carol's first husband.[5]"
Math
  • 'Given that the four-digit number N N is a perfect square, and each digit of N N is less than 7. If each digit is increased by 3, the resulting four-digit number is still a perfect square. Find N N .'
  • 'Given $f(x) = ax^5 + bx^3 + cx + 8$, and $f(-2) = 10$, then $f(2) = \boxed{ }$\n\nA: $-2$\n\nB: $-6$\n\nC: $6$\n\nD: $8$'
  • 'Find the seventh term of the geometric sequence with first term $3$ and second term $\frac{2}{5}$.'
Brainstorming
  • 'Is this quote inspiring? Give me the words and phrases that make you think it is or is not inspiring. \n\nQuote: “The best way to not feel hopeless is to get up and do something. Don’t wait for good things to happen to you. If you go out and make some good things happen, you will fill the world with hope, you will fill yourself with hope.”'
  • 'How can I manage my time effectively while balancing school and work?'
  • 'Why did the Hood Canal Bridge sink in Washington in 1979?'
Factual QA
  • 'Who was the first host of Top Chef?'
  • 'In the 2016 World Rugby Nations Cup, what was the final score of the match between Namibia and Emerging Italy?'
  • "What is the full name of Vic Clapham's great-grandson who completed the Comrades Marathon from 2012 to 2015?"
Generation
  • 'Imagine you are a highly experienced spaceship engineer, and someone is asking your opinion on the most efficient design for a newly proposed interstellar spacecraft. Provide a brief overview of essential features for such a vessel.'
  • 'Create a dialogue between two people who have completely opposite political views, but must find a way to work together.'
  • 'Your response should contain at least 5 sentences. Include keywords [love, happiness, joy] in the response. In your response, the word "joy" should appear at least 3 times.\n\nHow can I express my feelings of joy and happiness to someone I love?'
Coding
  • 'Your friend Claire has dragged you along to a speedcubing event that is happening in Eindhoven. These events are all about solving the Rubik’s cube and similar twisty puzzles as quickly as possible. The attendants of the event can enter into various competitions based on the type and size of the puzzle, and there are even special competitions where the puzzles need to be solved one-handed or blindfolded. \n\nClaire is competing in the most popular competition: speedsolving the $3\times 3\times 3$ Rubik’s cube, pictured on the right. Each contestant needs to solve the cube five times, each time with a different random scramble. After all solves are completed, the best and the worst times are discarded and the final score is the average of the remaining three times. The contestant with the smallest final score wins.\n\nClaire has done well in the competition so far and is among the contenders for the overall victory. All the other contestants have already finished their five solves, but Claire has one solve remaining. By looking at the final scores of the other contestants, she has deduced her own target final score. As long as her final score is less than or equal to this target score, she will be declared the overall winner. Is it possible for her to win the competition, and if so, what is the worst time she can have on her last solve in order to do so?\n\n-----Input-----\nThe input consists of:\n - One line with four real numbers $t_1$, $t_2$, $t_3$ and $t_4$, the times Claire got on her first four solves.\n - One line with a real number $t$, Claire’s target final score, the worst final score she can have in order to be declared the overall winner.\n\nEach number is between $1$ and $20$, inclusive, and is given with exactly two decimal places.\n\n-----Output-----\nIf it is not possible for Claire to win the event, output “impossible”. If she will win regardless of the time she gets on her last solve, output “infinite”. Otherwise, output the worst time she can have on her last solve in order to be declared the overall winner. Output the number to exactly two decimal places.\n\n-----Examples-----\nSample Input 1:\n6.38 7.20 6.95 8.11\n7.53\nSample Output 1:\ninfinite\n\nSample Input 2:\n6.38 7.20 6.95 8.11\n6.99\nSample Output 2:\n6.82'
  • "Problem :\n\nBajirao is on a date with his girlfriend Avni. It is a romantic night and they are\nplaying a game of words. \n\nThe rule of this game is that if Bajirao says a word such that no adjacent letters occurring in the word are same then he gets a kiss from her otherwise he gets a slap.\n\nInput :\n\nThe first line consists of T the number of test cases. The next T lines are such that each line consists of a single word spoken by Bajirao.\n\nOutput\n\nFor every test case, on a new line print 'KISS' if Bajirao gets a kiss and 'SLAP' if Bajirao gets a slap.\n\nConstraints :\n\n1 ≤ T ≤ 100\n\n2 ≤ Length of Word spoken by Bajirao ≤ 100\n\nThe input word will comprise only of lower case English alphabets (a-z).\n\nProblem Setter : Shreyans\n\nProblem Tester : Sandeep\n\nProblem Statement : Ravi\n\n(By IIT Kgp HackerEarth Programming Club)\n\nSAMPLE INPUT\n2\nremember\noccurring\n\nSAMPLE OUTPUT\nKISS\nSLAP"
  • 'Using the Arduino IDE, create an .ino sketch that creates an MQTT broker for the MKR WIFI 1010'
Reasoning
  • 'Silver dollar pancakes are a variety that is smaller than traditional pancakes. Silver dollars are a type of American coin.\n\nAre some types of pancakes named after coins?'
  • 'Most successful companies are related to good management. Enterprise management generally includes two aspects, namely management and management, of which management is more important. To do a good job of management, you need a variety of Managing talent also requires leaders to make the most of their role.\n\nIt follows from this:.\n\n1. A well-managed company will succeed.\n2. With good management talent, good management is guaranteed.\n3. Poorly managed companies will eventually fail in market competition.\n4. Leaders should pay attention to the role of subordinates.'
  • 'The survey shows that the biggest difficulty in youth entrepreneurship is funding.64.2% of people believe that lack of sufficient funds is the main difficulty. Many people are unwilling to borrow or raise funds despite lack of funds, which reflects that many entrepreneurs are starting a business. There is a conservative mentality in the process. Another prominent difficulty is the excessive competition of peers, accounting for 26.9%. During the survey, it was found that the field of youth entrepreneurship is more concentrated, such as the college student group is more inclined to e-commerce, computer technology support, etc. Young farmers are more willing to engage in the planting and breeding industries that they are more familiar with. This kind of homogeneous entrepreneurship will inevitably lead to excessive competition while forming a scale effect.\n\nThe following statement is consistent with the original?\n\n1. Insufficient funding is a major factor in the failure of youth entrepreneurship.\n2. Inadequate financial services support for young entrepreneurs.\n3. Homogeneous entrepreneurship reflects the conservative mindset of entrepreneurs.\n4. The field of youth entrepreneurship is concentrated in certain fixed industries.'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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 <<SRE>>. 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 

@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}
}
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