File size: 24,242 Bytes
69075f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Strengthen macro-fiscal resilience through risk-informed public investment
planning, including scenario-based budgeting and contingent financing arrangements.
- text: 'finding environmentally sustainable energy solutions is central to the document.
it seeks to facilitate cultural, institutional and technological change in a way
that supports ''''aggressive'''' advances in energy efficiency and conservation,
minimises greenhouse emissions and ultimately provides green growth. these energy
efficiency and conservation goals are seen as ''''no regrets'''' mitigation actions
that can have positive impacts on society and the economy, principally by reducing
costs and dependency on fossil fuel imports. overall the policy propose to reduce
the percentage of petroleum in the country''''s energy supply mix from the current
95 percent (does not state to what level) and increase the percentage of renewables
in the energy mix with proposed targets of 11 percent by 2012, 12.5 percent by
2015 and 20 percent by 2030. six sub-policies exist to support the national energy
policy, namely: - a carbon emissions trading policy developed to address jamaica''''s
participation in the clean development mechanism - energy-from-waste policy -
national renewable energy policy 2010-2030 - national energy from waste policy
2010-2030 - energy conservation and efficiency policy - biofuels policy'
- text: 'objetivos: 1. promover la garantía del derecho a la alimentación para la
población general y en especial para las personas y grupos de mayor vulnerabilidad.
2. respetar la identidad cultural, las necesidades nutricionales según el ciclo
de vida y la diversidad de formas de producción, de consumo y comercialización
agropecuaria, fortaleciendo los mercados locales, sin contraponerse al comercio
agroalimentario internacional, favoreciéndose la producción nacional en granos
básicos, frutas y vegetales. 3. promover la igualdad entre hombres y mujeres,
dando las mismas posibilidades de acceso a recursos productivos, servicios y oportunidades
para asumir responsabilidades y roles en la seguridad alimentaria y nutricional.
4.transformar el enfoque de las políticas públicas y sociales, para que pasen
las personas de ser clientela pasiva y vulnerable que requiere de asistencia,
a personas sujetos de derechos.'
- text: Regulatory arrangements will be reformed to accelerate innovation in agriculture,
including pilot programs, regulatory sandboxes for new inputs and services, clear
intellectual property protection, and predictable approval timelines for agrochemical
and digital solutions that meet safety and environmental criteria.
- text: Climate-smart strategies will protect livelihoods by diversifying income sources,
expanding agroforestry and drought-resistant crops, and implementing risk-transfer
mechanisms that shield poor households from shocks, thereby contributing to sustained
declines in poverty levels.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
---
# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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)
## 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("faodl/model_cca_multilabel_MiniLM-L12-70prop-data-augmented")
# Run inference
preds = model("Strengthen macro-fiscal resilience through risk-informed public investment planning, including scenario-based budgeting and contingent financing arrangements.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 69.0403 | 951 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2247 | - |
| 0.0065 | 50 | 0.2105 | - |
| 0.0130 | 100 | 0.1984 | - |
| 0.0195 | 150 | 0.1899 | - |
| 0.0260 | 200 | 0.1916 | - |
| 0.0325 | 250 | 0.1769 | - |
| 0.0390 | 300 | 0.1679 | - |
| 0.0455 | 350 | 0.1677 | - |
| 0.0520 | 400 | 0.1591 | - |
| 0.0585 | 450 | 0.1521 | - |
| 0.0650 | 500 | 0.1522 | - |
| 0.0715 | 550 | 0.1497 | - |
| 0.0780 | 600 | 0.1494 | - |
| 0.0845 | 650 | 0.1457 | - |
| 0.0910 | 700 | 0.1503 | - |
| 0.0975 | 750 | 0.1328 | - |
| 0.1040 | 800 | 0.1251 | - |
| 0.1105 | 850 | 0.1395 | - |
| 0.1170 | 900 | 0.1298 | - |
| 0.1235 | 950 | 0.1221 | - |
| 0.1300 | 1000 | 0.1313 | - |
| 0.1365 | 1050 | 0.1267 | - |
| 0.1429 | 1100 | 0.1367 | - |
| 0.1494 | 1150 | 0.1324 | - |
| 0.1559 | 1200 | 0.1201 | - |
| 0.1624 | 1250 | 0.1244 | - |
| 0.1689 | 1300 | 0.1231 | - |
| 0.1754 | 1350 | 0.1214 | - |
| 0.1819 | 1400 | 0.1098 | - |
| 0.1884 | 1450 | 0.1152 | - |
| 0.1949 | 1500 | 0.1149 | - |
| 0.2014 | 1550 | 0.1185 | - |
| 0.2079 | 1600 | 0.1123 | - |
| 0.2144 | 1650 | 0.1092 | - |
| 0.2209 | 1700 | 0.1097 | - |
| 0.2274 | 1750 | 0.1159 | - |
| 0.2339 | 1800 | 0.1076 | - |
| 0.2404 | 1850 | 0.114 | - |
| 0.2469 | 1900 | 0.1055 | - |
| 0.2534 | 1950 | 0.1033 | - |
| 0.2599 | 2000 | 0.1016 | - |
| 0.2664 | 2050 | 0.1004 | - |
| 0.2729 | 2100 | 0.0973 | - |
| 0.2794 | 2150 | 0.1051 | - |
| 0.2859 | 2200 | 0.0954 | - |
| 0.2924 | 2250 | 0.0998 | - |
| 0.2989 | 2300 | 0.0984 | - |
| 0.3054 | 2350 | 0.0906 | - |
| 0.3119 | 2400 | 0.0939 | - |
| 0.3184 | 2450 | 0.1023 | - |
| 0.3249 | 2500 | 0.0983 | - |
| 0.3314 | 2550 | 0.0952 | - |
| 0.3379 | 2600 | 0.099 | - |
| 0.3444 | 2650 | 0.0994 | - |
| 0.3509 | 2700 | 0.0975 | - |
| 0.3574 | 2750 | 0.0871 | - |
| 0.3639 | 2800 | 0.0969 | - |
| 0.3704 | 2850 | 0.0845 | - |
| 0.3769 | 2900 | 0.1007 | - |
| 0.3834 | 2950 | 0.0887 | - |
| 0.3899 | 3000 | 0.0807 | - |
| 0.3964 | 3050 | 0.0859 | - |
| 0.4029 | 3100 | 0.0826 | - |
| 0.4094 | 3150 | 0.0784 | - |
| 0.4159 | 3200 | 0.0851 | - |
| 0.4224 | 3250 | 0.0834 | - |
| 0.4288 | 3300 | 0.0922 | - |
| 0.4353 | 3350 | 0.0862 | - |
| 0.4418 | 3400 | 0.0856 | - |
| 0.4483 | 3450 | 0.0848 | - |
| 0.4548 | 3500 | 0.0735 | - |
| 0.4613 | 3550 | 0.0752 | - |
| 0.4678 | 3600 | 0.0881 | - |
| 0.4743 | 3650 | 0.0836 | - |
| 0.4808 | 3700 | 0.0808 | - |
| 0.4873 | 3750 | 0.0963 | - |
| 0.4938 | 3800 | 0.0816 | - |
| 0.5003 | 3850 | 0.0809 | - |
| 0.5068 | 3900 | 0.0833 | - |
| 0.5133 | 3950 | 0.0852 | - |
| 0.5198 | 4000 | 0.0788 | - |
| 0.5263 | 4050 | 0.0742 | - |
| 0.5328 | 4100 | 0.0693 | - |
| 0.5393 | 4150 | 0.0856 | - |
| 0.5458 | 4200 | 0.072 | - |
| 0.5523 | 4250 | 0.0805 | - |
| 0.5588 | 4300 | 0.0741 | - |
| 0.5653 | 4350 | 0.0845 | - |
| 0.5718 | 4400 | 0.0753 | - |
| 0.5783 | 4450 | 0.0814 | - |
| 0.5848 | 4500 | 0.0691 | - |
| 0.5913 | 4550 | 0.0823 | - |
| 0.5978 | 4600 | 0.0847 | - |
| 0.6043 | 4650 | 0.0714 | - |
| 0.6108 | 4700 | 0.0879 | - |
| 0.6173 | 4750 | 0.0711 | - |
| 0.6238 | 4800 | 0.0697 | - |
| 0.6303 | 4850 | 0.0741 | - |
| 0.6368 | 4900 | 0.0771 | - |
| 0.6433 | 4950 | 0.0837 | - |
| 0.6498 | 5000 | 0.0743 | - |
| 0.6563 | 5050 | 0.0755 | - |
| 0.6628 | 5100 | 0.0739 | - |
| 0.6693 | 5150 | 0.0816 | - |
| 0.6758 | 5200 | 0.0782 | - |
| 0.6823 | 5250 | 0.0755 | - |
| 0.6888 | 5300 | 0.0712 | - |
| 0.6953 | 5350 | 0.0639 | - |
| 0.7018 | 5400 | 0.0694 | - |
| 0.7083 | 5450 | 0.0806 | - |
| 0.7147 | 5500 | 0.071 | - |
| 0.7212 | 5550 | 0.0707 | - |
| 0.7277 | 5600 | 0.0751 | - |
| 0.7342 | 5650 | 0.0724 | - |
| 0.7407 | 5700 | 0.0688 | - |
| 0.7472 | 5750 | 0.067 | - |
| 0.7537 | 5800 | 0.0718 | - |
| 0.7602 | 5850 | 0.0681 | - |
| 0.7667 | 5900 | 0.0694 | - |
| 0.7732 | 5950 | 0.0693 | - |
| 0.7797 | 6000 | 0.0731 | - |
| 0.7862 | 6050 | 0.0626 | - |
| 0.7927 | 6100 | 0.0691 | - |
| 0.7992 | 6150 | 0.0711 | - |
| 0.8057 | 6200 | 0.0627 | - |
| 0.8122 | 6250 | 0.0726 | - |
| 0.8187 | 6300 | 0.068 | - |
| 0.8252 | 6350 | 0.0766 | - |
| 0.8317 | 6400 | 0.0617 | - |
| 0.8382 | 6450 | 0.0671 | - |
| 0.8447 | 6500 | 0.0645 | - |
| 0.8512 | 6550 | 0.0722 | - |
| 0.8577 | 6600 | 0.0751 | - |
| 0.8642 | 6650 | 0.0591 | - |
| 0.8707 | 6700 | 0.0664 | - |
| 0.8772 | 6750 | 0.0735 | - |
| 0.8837 | 6800 | 0.0709 | - |
| 0.8902 | 6850 | 0.0632 | - |
| 0.8967 | 6900 | 0.0679 | - |
| 0.9032 | 6950 | 0.0596 | - |
| 0.9097 | 7000 | 0.0676 | - |
| 0.9162 | 7050 | 0.066 | - |
| 0.9227 | 7100 | 0.069 | - |
| 0.9292 | 7150 | 0.0615 | - |
| 0.9357 | 7200 | 0.0579 | - |
| 0.9422 | 7250 | 0.0576 | - |
| 0.9487 | 7300 | 0.0558 | - |
| 0.9552 | 7350 | 0.0556 | - |
| 0.9617 | 7400 | 0.0637 | - |
| 0.9682 | 7450 | 0.0615 | - |
| 0.9747 | 7500 | 0.0677 | - |
| 0.9812 | 7550 | 0.0584 | - |
| 0.9877 | 7600 | 0.0661 | - |
| 0.9942 | 7650 | 0.0583 | - |
| 1.0006 | 7700 | 0.0639 | - |
| 1.0071 | 7750 | 0.0598 | - |
| 1.0136 | 7800 | 0.0586 | - |
| 1.0201 | 7850 | 0.055 | - |
| 1.0266 | 7900 | 0.0636 | - |
| 1.0331 | 7950 | 0.0623 | - |
| 1.0396 | 8000 | 0.0661 | - |
| 1.0461 | 8050 | 0.0633 | - |
| 1.0526 | 8100 | 0.056 | - |
| 1.0591 | 8150 | 0.0555 | - |
| 1.0656 | 8200 | 0.0608 | - |
| 1.0721 | 8250 | 0.0491 | - |
| 1.0786 | 8300 | 0.0592 | - |
| 1.0851 | 8350 | 0.0645 | - |
| 1.0916 | 8400 | 0.0553 | - |
| 1.0981 | 8450 | 0.0547 | - |
| 1.1046 | 8500 | 0.0494 | - |
| 1.1111 | 8550 | 0.0594 | - |
| 1.1176 | 8600 | 0.058 | - |
| 1.1241 | 8650 | 0.0589 | - |
| 1.1306 | 8700 | 0.0552 | - |
| 1.1371 | 8750 | 0.0554 | - |
| 1.1436 | 8800 | 0.0566 | - |
| 1.1501 | 8850 | 0.0558 | - |
| 1.1566 | 8900 | 0.0596 | - |
| 1.1631 | 8950 | 0.0551 | - |
| 1.1696 | 9000 | 0.061 | - |
| 1.1761 | 9050 | 0.0689 | - |
| 1.1826 | 9100 | 0.0565 | - |
| 1.1891 | 9150 | 0.0581 | - |
| 1.1956 | 9200 | 0.0606 | - |
| 1.2021 | 9250 | 0.057 | - |
| 1.2086 | 9300 | 0.0577 | - |
| 1.2151 | 9350 | 0.0629 | - |
| 1.2216 | 9400 | 0.0592 | - |
| 1.2281 | 9450 | 0.0547 | - |
| 1.2346 | 9500 | 0.0606 | - |
| 1.2411 | 9550 | 0.0588 | - |
| 1.2476 | 9600 | 0.0581 | - |
| 1.2541 | 9650 | 0.0624 | - |
| 1.2606 | 9700 | 0.0589 | - |
| 1.2671 | 9750 | 0.0646 | - |
| 1.2736 | 9800 | 0.0559 | - |
| 1.2801 | 9850 | 0.0594 | - |
| 1.2865 | 9900 | 0.0586 | - |
| 1.2930 | 9950 | 0.0552 | - |
| 1.2995 | 10000 | 0.0513 | - |
| 1.3060 | 10050 | 0.0565 | - |
| 1.3125 | 10100 | 0.0626 | - |
| 1.3190 | 10150 | 0.0483 | - |
| 1.3255 | 10200 | 0.0643 | - |
| 1.3320 | 10250 | 0.0524 | - |
| 1.3385 | 10300 | 0.0559 | - |
| 1.3450 | 10350 | 0.0589 | - |
| 1.3515 | 10400 | 0.0562 | - |
| 1.3580 | 10450 | 0.0592 | - |
| 1.3645 | 10500 | 0.047 | - |
| 1.3710 | 10550 | 0.0531 | - |
| 1.3775 | 10600 | 0.0506 | - |
| 1.3840 | 10650 | 0.0579 | - |
| 1.3905 | 10700 | 0.0569 | - |
| 1.3970 | 10750 | 0.0579 | - |
| 1.4035 | 10800 | 0.0504 | - |
| 1.4100 | 10850 | 0.0547 | - |
| 1.4165 | 10900 | 0.0497 | - |
| 1.4230 | 10950 | 0.0533 | - |
| 1.4295 | 11000 | 0.0488 | - |
| 1.4360 | 11050 | 0.0537 | - |
| 1.4425 | 11100 | 0.0544 | - |
| 1.4490 | 11150 | 0.0548 | - |
| 1.4555 | 11200 | 0.0475 | - |
| 1.4620 | 11250 | 0.0519 | - |
| 1.4685 | 11300 | 0.0568 | - |
| 1.4750 | 11350 | 0.0567 | - |
| 1.4815 | 11400 | 0.0473 | - |
| 1.4880 | 11450 | 0.0535 | - |
| 1.4945 | 11500 | 0.0531 | - |
| 1.5010 | 11550 | 0.0567 | - |
| 1.5075 | 11600 | 0.0529 | - |
| 1.5140 | 11650 | 0.0544 | - |
| 1.5205 | 11700 | 0.0612 | - |
| 1.5270 | 11750 | 0.055 | - |
| 1.5335 | 11800 | 0.0474 | - |
| 1.5400 | 11850 | 0.0572 | - |
| 1.5465 | 11900 | 0.0484 | - |
| 1.5530 | 11950 | 0.0553 | - |
| 1.5595 | 12000 | 0.0519 | - |
| 1.5660 | 12050 | 0.0565 | - |
| 1.5724 | 12100 | 0.0466 | - |
| 1.5789 | 12150 | 0.0502 | - |
| 1.5854 | 12200 | 0.0525 | - |
| 1.5919 | 12250 | 0.054 | - |
| 1.5984 | 12300 | 0.0556 | - |
| 1.6049 | 12350 | 0.0515 | - |
| 1.6114 | 12400 | 0.0476 | - |
| 1.6179 | 12450 | 0.0579 | - |
| 1.6244 | 12500 | 0.0567 | - |
| 1.6309 | 12550 | 0.0551 | - |
| 1.6374 | 12600 | 0.0518 | - |
| 1.6439 | 12650 | 0.0508 | - |
| 1.6504 | 12700 | 0.0503 | - |
| 1.6569 | 12750 | 0.0484 | - |
| 1.6634 | 12800 | 0.0531 | - |
| 1.6699 | 12850 | 0.0553 | - |
| 1.6764 | 12900 | 0.0588 | - |
| 1.6829 | 12950 | 0.0547 | - |
| 1.6894 | 13000 | 0.0587 | - |
| 1.6959 | 13050 | 0.0562 | - |
| 1.7024 | 13100 | 0.0558 | - |
| 1.7089 | 13150 | 0.0559 | - |
| 1.7154 | 13200 | 0.0547 | - |
| 1.7219 | 13250 | 0.059 | - |
| 1.7284 | 13300 | 0.053 | - |
| 1.7349 | 13350 | 0.0532 | - |
| 1.7414 | 13400 | 0.0552 | - |
| 1.7479 | 13450 | 0.0443 | - |
| 1.7544 | 13500 | 0.058 | - |
| 1.7609 | 13550 | 0.0503 | - |
| 1.7674 | 13600 | 0.0499 | - |
| 1.7739 | 13650 | 0.0478 | - |
| 1.7804 | 13700 | 0.0569 | - |
| 1.7869 | 13750 | 0.052 | - |
| 1.7934 | 13800 | 0.0458 | - |
| 1.7999 | 13850 | 0.0551 | - |
| 1.8064 | 13900 | 0.0567 | - |
| 1.8129 | 13950 | 0.0511 | - |
| 1.8194 | 14000 | 0.0546 | - |
| 1.8259 | 14050 | 0.058 | - |
| 1.8324 | 14100 | 0.0539 | - |
| 1.8389 | 14150 | 0.0544 | - |
| 1.8454 | 14200 | 0.061 | - |
| 1.8519 | 14250 | 0.0521 | - |
| 1.8583 | 14300 | 0.046 | - |
| 1.8648 | 14350 | 0.0494 | - |
| 1.8713 | 14400 | 0.0604 | - |
| 1.8778 | 14450 | 0.0543 | - |
| 1.8843 | 14500 | 0.0522 | - |
| 1.8908 | 14550 | 0.0533 | - |
| 1.8973 | 14600 | 0.0469 | - |
| 1.9038 | 14650 | 0.0525 | - |
| 1.9103 | 14700 | 0.0516 | - |
| 1.9168 | 14750 | 0.0485 | - |
| 1.9233 | 14800 | 0.0601 | - |
| 1.9298 | 14850 | 0.0487 | - |
| 1.9363 | 14900 | 0.0496 | - |
| 1.9428 | 14950 | 0.0529 | - |
| 1.9493 | 15000 | 0.054 | - |
| 1.9558 | 15050 | 0.0431 | - |
| 1.9623 | 15100 | 0.0449 | - |
| 1.9688 | 15150 | 0.0602 | - |
| 1.9753 | 15200 | 0.0447 | - |
| 1.9818 | 15250 | 0.0506 | - |
| 1.9883 | 15300 | 0.0503 | - |
| 1.9948 | 15350 | 0.0515 | - |
### Framework Versions
- Python: 3.12.12
- SetFit: 1.1.3
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.1
## 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |