File size: 37,723 Bytes
bbef17a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"Die jungen Klimaaktivisten haben mit ihren Protestaktionen und Straßenblockaden
ein dringend benötigtes Gespräch über die Notwendigkeit von sofortigem Handeln
im Kampf gegen den Klimawandel angestoßen."'
- text: Die Bundesregierung plant, den Einsatz von Wärmepumpen durch ein neues Heizungsgesetz
zu fördern, was laut Experten einen wichtigen Schritt zur Erreichung der Klimaziele
darstellen könnte.
- text: ' "Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung
eines grünen Diktats, das die Bürger in die Kälte schickt."'
- text: ' Die Klima-Aktivisten von Fridays for Future und der Letzten Generation haben
heute in mehreren Städten Proteste organisiert, um auf den Klimawandel aufmerksam
zu machen.'
- text: ' "Die Diskussion über ein Tempolimit auf Autobahnen spaltet die Gemüter,
während Experten auf die potenziellen Vorteile für die Verkehrssicherheit und
den Klimaschutz hinweisen."'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.953405017921147
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **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)
### Model Labels
| Label | Examples |
|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral | <ul><li>'Die Bundesregierung plant, bis 2024 ein sogenanntes Heizungsgesetz vorzulegen, das unter anderem eine flächendeckende Nutzung von Wärmepumpen als Teil eines umfassenden Plans zur Reduzierung der Treibhausgasemissionen im Gebäudesektor vorsehen soll.'</li><li>'"Die Bundesregierung plant, die Einführung von Wärmepumpen für Neubauten und den Austausch alter Heizungsanlagen in Bestandsgebäuden durch ein Gesetz zu forcieren, während Kritiker warnen, dass die Maßnahmen die Belastung für private Haushalte und Unternehmen erhöhen könnten."'</li><li>' Die Diskussion über ein nationales Tempolimit auf Autobahnen spaltet die Gemüter, während Experten die potenziellen Vorteile und Nachteile abwägen.'</li></ul> |
| opposed | <ul><li>'"Millionen von Hausbesitzern sollen zu unfreiwilligen Versuchskaninchen für die teuren und unzuverlässlichen Wärmepumpen werden, ohne dass es auch nur einen Hauch von echter Wahlmöglichkeit gibt."'</li><li>'"Die von den Grünen und Linken geträumte Tempodiktatur auf unseren Autobahnen ist nichts als ein weiterer Schritt in Richtung einer überbürokratisierten, unfreien Gesellschaft."'</li><li>'"Die geplanten Vorschriften würden vielen Familien den Traum vom Eigenheim in weite Ferne rücken, da die Kosten für die Installation einer Wärmepumpe oft ein Vielfaches dessen betragen, was ein durchschnittlicher Haushalt in einem Jahr für Heizkosten ausgibt."'</li></ul> |
| supportive | <ul><li>'Die Bundesregierung hat mit dem Heizungsgesetz einen wichtigen Schritt in Richtung Klimaneutralität gemacht, indem sie die Verpflichtung zur Nutzung erneuerbarer Wärmequellen bei Neubauten festlegt.'</li><li>'"Ein Tempolimit auf Autobahnen könnte nicht nur die Umweltbelastung verringern, sondern auch die Zahl der Verkehrsunfälle reduzieren und somit Menschenleben retten."'</li><li>' Eine nationale Geschwindigkeitsbegrenzung auf Autobahnen könnte nicht nur die Unfallzahlen senken, sondern auch einen wichtigen Beitrag zum Klimaschutz leisten.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9534 |
## 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("cbpuschmann/paraphrase-multilingual-mpnet-klimacoder_v0.8")
# Run inference
preds = model(" \"Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung eines grünen Diktats, das die Bürger in die Kälte schickt.\"")
```
<!--
### 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 | 10 | 25.6541 | 57 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 321 |
| opposed | 391 |
| supportive | 404 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.1985 | - |
| 0.0019 | 50 | 0.2445 | - |
| 0.0039 | 100 | 0.2321 | - |
| 0.0058 | 150 | 0.2012 | - |
| 0.0077 | 200 | 0.1614 | - |
| 0.0097 | 250 | 0.1188 | - |
| 0.0116 | 300 | 0.0849 | - |
| 0.0136 | 350 | 0.0563 | - |
| 0.0155 | 400 | 0.0374 | - |
| 0.0174 | 450 | 0.0216 | - |
| 0.0194 | 500 | 0.0144 | - |
| 0.0213 | 550 | 0.0099 | - |
| 0.0232 | 600 | 0.0061 | - |
| 0.0252 | 650 | 0.007 | - |
| 0.0271 | 700 | 0.0026 | - |
| 0.0290 | 750 | 0.0017 | - |
| 0.0310 | 800 | 0.0012 | - |
| 0.0329 | 850 | 0.0014 | - |
| 0.0349 | 900 | 0.002 | - |
| 0.0368 | 950 | 0.0008 | - |
| 0.0387 | 1000 | 0.0009 | - |
| 0.0407 | 1050 | 0.0003 | - |
| 0.0426 | 1100 | 0.0007 | - |
| 0.0445 | 1150 | 0.0008 | - |
| 0.0465 | 1200 | 0.0006 | - |
| 0.0484 | 1250 | 0.0002 | - |
| 0.0503 | 1300 | 0.0001 | - |
| 0.0523 | 1350 | 0.0001 | - |
| 0.0542 | 1400 | 0.0001 | - |
| 0.0562 | 1450 | 0.0001 | - |
| 0.0581 | 1500 | 0.0007 | - |
| 0.0600 | 1550 | 0.0005 | - |
| 0.0620 | 1600 | 0.0007 | - |
| 0.0639 | 1650 | 0.0012 | - |
| 0.0658 | 1700 | 0.0007 | - |
| 0.0678 | 1750 | 0.0038 | - |
| 0.0697 | 1800 | 0.0018 | - |
| 0.0716 | 1850 | 0.0049 | - |
| 0.0736 | 1900 | 0.0061 | - |
| 0.0755 | 1950 | 0.0038 | - |
| 0.0775 | 2000 | 0.0037 | - |
| 0.0794 | 2050 | 0.0006 | - |
| 0.0813 | 2100 | 0.0001 | - |
| 0.0833 | 2150 | 0.0 | - |
| 0.0852 | 2200 | 0.0 | - |
| 0.0871 | 2250 | 0.0 | - |
| 0.0891 | 2300 | 0.0 | - |
| 0.0910 | 2350 | 0.0 | - |
| 0.0929 | 2400 | 0.0 | - |
| 0.0949 | 2450 | 0.0 | - |
| 0.0968 | 2500 | 0.0 | - |
| 0.0987 | 2550 | 0.0 | - |
| 0.1007 | 2600 | 0.0 | - |
| 0.1026 | 2650 | 0.0 | - |
| 0.1046 | 2700 | 0.0 | - |
| 0.1065 | 2750 | 0.0 | - |
| 0.1084 | 2800 | 0.0 | - |
| 0.1104 | 2850 | 0.0 | - |
| 0.1123 | 2900 | 0.0 | - |
| 0.1142 | 2950 | 0.0 | - |
| 0.1162 | 3000 | 0.0 | - |
| 0.1181 | 3050 | 0.0 | - |
| 0.1200 | 3100 | 0.0 | - |
| 0.1220 | 3150 | 0.0 | - |
| 0.1239 | 3200 | 0.0 | - |
| 0.1259 | 3250 | 0.0 | - |
| 0.1278 | 3300 | 0.0 | - |
| 0.1297 | 3350 | 0.0 | - |
| 0.1317 | 3400 | 0.0 | - |
| 0.1336 | 3450 | 0.0 | - |
| 0.1355 | 3500 | 0.0 | - |
| 0.1375 | 3550 | 0.0 | - |
| 0.1394 | 3600 | 0.0 | - |
| 0.1413 | 3650 | 0.0 | - |
| 0.1433 | 3700 | 0.0 | - |
| 0.1452 | 3750 | 0.0 | - |
| 0.1472 | 3800 | 0.0 | - |
| 0.1491 | 3850 | 0.0 | - |
| 0.1510 | 3900 | 0.0 | - |
| 0.1530 | 3950 | 0.0 | - |
| 0.1549 | 4000 | 0.0 | - |
| 0.1568 | 4050 | 0.0 | - |
| 0.1588 | 4100 | 0.0 | - |
| 0.1607 | 4150 | 0.0 | - |
| 0.1626 | 4200 | 0.0 | - |
| 0.1646 | 4250 | 0.0 | - |
| 0.1665 | 4300 | 0.0 | - |
| 0.1685 | 4350 | 0.0 | - |
| 0.1704 | 4400 | 0.0 | - |
| 0.1723 | 4450 | 0.0 | - |
| 0.1743 | 4500 | 0.0 | - |
| 0.1762 | 4550 | 0.0 | - |
| 0.1781 | 4600 | 0.0 | - |
| 0.1801 | 4650 | 0.0 | - |
| 0.1820 | 4700 | 0.0 | - |
| 0.1839 | 4750 | 0.0 | - |
| 0.1859 | 4800 | 0.0 | - |
| 0.1878 | 4850 | 0.0 | - |
| 0.1898 | 4900 | 0.0 | - |
| 0.1917 | 4950 | 0.0 | - |
| 0.1936 | 5000 | 0.0 | - |
| 0.1956 | 5050 | 0.0 | - |
| 0.1975 | 5100 | 0.0 | - |
| 0.1994 | 5150 | 0.0 | - |
| 0.2014 | 5200 | 0.0 | - |
| 0.2033 | 5250 | 0.0 | - |
| 0.2052 | 5300 | 0.0 | - |
| 0.2072 | 5350 | 0.0 | - |
| 0.2091 | 5400 | 0.0 | - |
| 0.2111 | 5450 | 0.0 | - |
| 0.2130 | 5500 | 0.0 | - |
| 0.2149 | 5550 | 0.0 | - |
| 0.2169 | 5600 | 0.0 | - |
| 0.2188 | 5650 | 0.0 | - |
| 0.2207 | 5700 | 0.0 | - |
| 0.2227 | 5750 | 0.0 | - |
| 0.2246 | 5800 | 0.0 | - |
| 0.2265 | 5850 | 0.0 | - |
| 0.2285 | 5900 | 0.0 | - |
| 0.2304 | 5950 | 0.0 | - |
| 0.2324 | 6000 | 0.0 | - |
| 0.2343 | 6050 | 0.0 | - |
| 0.2362 | 6100 | 0.0 | - |
| 0.2382 | 6150 | 0.0 | - |
| 0.2401 | 6200 | 0.0 | - |
| 0.2420 | 6250 | 0.0 | - |
| 0.2440 | 6300 | 0.0 | - |
| 0.2459 | 6350 | 0.0 | - |
| 0.2478 | 6400 | 0.0 | - |
| 0.2498 | 6450 | 0.0 | - |
| 0.2517 | 6500 | 0.0 | - |
| 0.2536 | 6550 | 0.0 | - |
| 0.2556 | 6600 | 0.0 | - |
| 0.2575 | 6650 | 0.0 | - |
| 0.2595 | 6700 | 0.0 | - |
| 0.2614 | 6750 | 0.0 | - |
| 0.2633 | 6800 | 0.0 | - |
| 0.2653 | 6850 | 0.0 | - |
| 0.2672 | 6900 | 0.0 | - |
| 0.2691 | 6950 | 0.0 | - |
| 0.2711 | 7000 | 0.0 | - |
| 0.2730 | 7050 | 0.0 | - |
| 0.2749 | 7100 | 0.0 | - |
| 0.2769 | 7150 | 0.0 | - |
| 0.2788 | 7200 | 0.0 | - |
| 0.2808 | 7250 | 0.0 | - |
| 0.2827 | 7300 | 0.0 | - |
| 0.2846 | 7350 | 0.0 | - |
| 0.2866 | 7400 | 0.0 | - |
| 0.2885 | 7450 | 0.0 | - |
| 0.2904 | 7500 | 0.0 | - |
| 0.2924 | 7550 | 0.0 | - |
| 0.2943 | 7600 | 0.0 | - |
| 0.2962 | 7650 | 0.0 | - |
| 0.2982 | 7700 | 0.0 | - |
| 0.3001 | 7750 | 0.0 | - |
| 0.3021 | 7800 | 0.0 | - |
| 0.3040 | 7850 | 0.0 | - |
| 0.3059 | 7900 | 0.0 | - |
| 0.3079 | 7950 | 0.0 | - |
| 0.3098 | 8000 | 0.0 | - |
| 0.3117 | 8050 | 0.0 | - |
| 0.3137 | 8100 | 0.0 | - |
| 0.3156 | 8150 | 0.0 | - |
| 0.3175 | 8200 | 0.0 | - |
| 0.3195 | 8250 | 0.0 | - |
| 0.3214 | 8300 | 0.0 | - |
| 0.3234 | 8350 | 0.0 | - |
| 0.3253 | 8400 | 0.0 | - |
| 0.3272 | 8450 | 0.0 | - |
| 0.3292 | 8500 | 0.0 | - |
| 0.3311 | 8550 | 0.0 | - |
| 0.3330 | 8600 | 0.0 | - |
| 0.3350 | 8650 | 0.0 | - |
| 0.3369 | 8700 | 0.0 | - |
| 0.3388 | 8750 | 0.0 | - |
| 0.3408 | 8800 | 0.0 | - |
| 0.3427 | 8850 | 0.0 | - |
| 0.3447 | 8900 | 0.0 | - |
| 0.3466 | 8950 | 0.0 | - |
| 0.3485 | 9000 | 0.0 | - |
| 0.3505 | 9050 | 0.0 | - |
| 0.3524 | 9100 | 0.0 | - |
| 0.3543 | 9150 | 0.0 | - |
| 0.3563 | 9200 | 0.0 | - |
| 0.3582 | 9250 | 0.0 | - |
| 0.3601 | 9300 | 0.0 | - |
| 0.3621 | 9350 | 0.0 | - |
| 0.3640 | 9400 | 0.0 | - |
| 0.3660 | 9450 | 0.0 | - |
| 0.3679 | 9500 | 0.0 | - |
| 0.3698 | 9550 | 0.0 | - |
| 0.3718 | 9600 | 0.0 | - |
| 0.3737 | 9650 | 0.0 | - |
| 0.3756 | 9700 | 0.0 | - |
| 0.3776 | 9750 | 0.0 | - |
| 0.3795 | 9800 | 0.0 | - |
| 0.3814 | 9850 | 0.0 | - |
| 0.3834 | 9900 | 0.0 | - |
| 0.3853 | 9950 | 0.0 | - |
| 0.3873 | 10000 | 0.0 | - |
| 0.3892 | 10050 | 0.0 | - |
| 0.3911 | 10100 | 0.0 | - |
| 0.3931 | 10150 | 0.0 | - |
| 0.3950 | 10200 | 0.0 | - |
| 0.3969 | 10250 | 0.0 | - |
| 0.3989 | 10300 | 0.0 | - |
| 0.4008 | 10350 | 0.0 | - |
| 0.4027 | 10400 | 0.0 | - |
| 0.4047 | 10450 | 0.0 | - |
| 0.4066 | 10500 | 0.0 | - |
| 0.4086 | 10550 | 0.0 | - |
| 0.4105 | 10600 | 0.0 | - |
| 0.4124 | 10650 | 0.0 | - |
| 0.4144 | 10700 | 0.0 | - |
| 0.4163 | 10750 | 0.0 | - |
| 0.4182 | 10800 | 0.0 | - |
| 0.4202 | 10850 | 0.0 | - |
| 0.4221 | 10900 | 0.0 | - |
| 0.4240 | 10950 | 0.0 | - |
| 0.4260 | 11000 | 0.0 | - |
| 0.4279 | 11050 | 0.0 | - |
| 0.4298 | 11100 | 0.0 | - |
| 0.4318 | 11150 | 0.0 | - |
| 0.4337 | 11200 | 0.0 | - |
| 0.4357 | 11250 | 0.0 | - |
| 0.4376 | 11300 | 0.0 | - |
| 0.4395 | 11350 | 0.0 | - |
| 0.4415 | 11400 | 0.0 | - |
| 0.4434 | 11450 | 0.0 | - |
| 0.4453 | 11500 | 0.0 | - |
| 0.4473 | 11550 | 0.0 | - |
| 0.4492 | 11600 | 0.0 | - |
| 0.4511 | 11650 | 0.0 | - |
| 0.4531 | 11700 | 0.0 | - |
| 0.4550 | 11750 | 0.0 | - |
| 0.4570 | 11800 | 0.0 | - |
| 0.4589 | 11850 | 0.0109 | - |
| 0.4608 | 11900 | 0.0218 | - |
| 0.4628 | 11950 | 0.0073 | - |
| 0.4647 | 12000 | 0.0056 | - |
| 0.4666 | 12050 | 0.0037 | - |
| 0.4686 | 12100 | 0.0011 | - |
| 0.4705 | 12150 | 0.0002 | - |
| 0.4724 | 12200 | 0.0014 | - |
| 0.4744 | 12250 | 0.0031 | - |
| 0.4763 | 12300 | 0.0013 | - |
| 0.4783 | 12350 | 0.0012 | - |
| 0.4802 | 12400 | 0.0022 | - |
| 0.4821 | 12450 | 0.0003 | - |
| 0.4841 | 12500 | 0.0 | - |
| 0.4860 | 12550 | 0.0 | - |
| 0.4879 | 12600 | 0.0 | - |
| 0.4899 | 12650 | 0.0 | - |
| 0.4918 | 12700 | 0.0 | - |
| 0.4937 | 12750 | 0.0 | - |
| 0.4957 | 12800 | 0.0 | - |
| 0.4976 | 12850 | 0.0 | - |
| 0.4996 | 12900 | 0.0 | - |
| 0.5015 | 12950 | 0.0 | - |
| 0.5034 | 13000 | 0.0 | - |
| 0.5054 | 13050 | 0.0 | - |
| 0.5073 | 13100 | 0.0 | - |
| 0.5092 | 13150 | 0.0 | - |
| 0.5112 | 13200 | 0.0 | - |
| 0.5131 | 13250 | 0.0 | - |
| 0.5150 | 13300 | 0.0 | - |
| 0.5170 | 13350 | 0.0 | - |
| 0.5189 | 13400 | 0.0 | - |
| 0.5209 | 13450 | 0.0 | - |
| 0.5228 | 13500 | 0.0 | - |
| 0.5247 | 13550 | 0.0 | - |
| 0.5267 | 13600 | 0.0 | - |
| 0.5286 | 13650 | 0.0 | - |
| 0.5305 | 13700 | 0.0 | - |
| 0.5325 | 13750 | 0.0 | - |
| 0.5344 | 13800 | 0.0 | - |
| 0.5363 | 13850 | 0.0 | - |
| 0.5383 | 13900 | 0.0 | - |
| 0.5402 | 13950 | 0.0 | - |
| 0.5422 | 14000 | 0.0 | - |
| 0.5441 | 14050 | 0.0 | - |
| 0.5460 | 14100 | 0.0 | - |
| 0.5480 | 14150 | 0.0 | - |
| 0.5499 | 14200 | 0.0 | - |
| 0.5518 | 14250 | 0.0 | - |
| 0.5538 | 14300 | 0.0 | - |
| 0.5557 | 14350 | 0.0 | - |
| 0.5576 | 14400 | 0.0 | - |
| 0.5596 | 14450 | 0.0 | - |
| 0.5615 | 14500 | 0.0 | - |
| 0.5635 | 14550 | 0.0 | - |
| 0.5654 | 14600 | 0.0 | - |
| 0.5673 | 14650 | 0.0 | - |
| 0.5693 | 14700 | 0.0 | - |
| 0.5712 | 14750 | 0.0 | - |
| 0.5731 | 14800 | 0.0 | - |
| 0.5751 | 14850 | 0.0 | - |
| 0.5770 | 14900 | 0.0 | - |
| 0.5789 | 14950 | 0.0 | - |
| 0.5809 | 15000 | 0.0 | - |
| 0.5828 | 15050 | 0.0 | - |
| 0.5848 | 15100 | 0.0 | - |
| 0.5867 | 15150 | 0.0 | - |
| 0.5886 | 15200 | 0.0 | - |
| 0.5906 | 15250 | 0.0 | - |
| 0.5925 | 15300 | 0.0 | - |
| 0.5944 | 15350 | 0.0 | - |
| 0.5964 | 15400 | 0.0 | - |
| 0.5983 | 15450 | 0.0 | - |
| 0.6002 | 15500 | 0.0 | - |
| 0.6022 | 15550 | 0.0 | - |
| 0.6041 | 15600 | 0.0 | - |
| 0.6060 | 15650 | 0.0 | - |
| 0.6080 | 15700 | 0.0 | - |
| 0.6099 | 15750 | 0.0 | - |
| 0.6119 | 15800 | 0.0 | - |
| 0.6138 | 15850 | 0.0 | - |
| 0.6157 | 15900 | 0.0 | - |
| 0.6177 | 15950 | 0.0 | - |
| 0.6196 | 16000 | 0.0 | - |
| 0.6215 | 16050 | 0.0 | - |
| 0.6235 | 16100 | 0.0 | - |
| 0.6254 | 16150 | 0.0002 | - |
| 0.6273 | 16200 | 0.0 | - |
| 0.6293 | 16250 | 0.0002 | - |
| 0.6312 | 16300 | 0.0034 | - |
| 0.6332 | 16350 | 0.0062 | - |
| 0.6351 | 16400 | 0.0034 | - |
| 0.6370 | 16450 | 0.0001 | - |
| 0.6390 | 16500 | 0.0004 | - |
| 0.6409 | 16550 | 0.0 | - |
| 0.6428 | 16600 | 0.0 | - |
| 0.6448 | 16650 | 0.0 | - |
| 0.6467 | 16700 | 0.0 | - |
| 0.6486 | 16750 | 0.0 | - |
| 0.6506 | 16800 | 0.0 | - |
| 0.6525 | 16850 | 0.0 | - |
| 0.6545 | 16900 | 0.0 | - |
| 0.6564 | 16950 | 0.0 | - |
| 0.6583 | 17000 | 0.0 | - |
| 0.6603 | 17050 | 0.0 | - |
| 0.6622 | 17100 | 0.0 | - |
| 0.6641 | 17150 | 0.0 | - |
| 0.6661 | 17200 | 0.0 | - |
| 0.6680 | 17250 | 0.0 | - |
| 0.6699 | 17300 | 0.0 | - |
| 0.6719 | 17350 | 0.0 | - |
| 0.6738 | 17400 | 0.0 | - |
| 0.6758 | 17450 | 0.0 | - |
| 0.6777 | 17500 | 0.0 | - |
| 0.6796 | 17550 | 0.0 | - |
| 0.6816 | 17600 | 0.0 | - |
| 0.6835 | 17650 | 0.0 | - |
| 0.6854 | 17700 | 0.0 | - |
| 0.6874 | 17750 | 0.0 | - |
| 0.6893 | 17800 | 0.0 | - |
| 0.6912 | 17850 | 0.0 | - |
| 0.6932 | 17900 | 0.0 | - |
| 0.6951 | 17950 | 0.0 | - |
| 0.6971 | 18000 | 0.0 | - |
| 0.6990 | 18050 | 0.0 | - |
| 0.7009 | 18100 | 0.0 | - |
| 0.7029 | 18150 | 0.0 | - |
| 0.7048 | 18200 | 0.0 | - |
| 0.7067 | 18250 | 0.0 | - |
| 0.7087 | 18300 | 0.0 | - |
| 0.7106 | 18350 | 0.0 | - |
| 0.7125 | 18400 | 0.0 | - |
| 0.7145 | 18450 | 0.0 | - |
| 0.7164 | 18500 | 0.0 | - |
| 0.7184 | 18550 | 0.0 | - |
| 0.7203 | 18600 | 0.0 | - |
| 0.7222 | 18650 | 0.0 | - |
| 0.7242 | 18700 | 0.0 | - |
| 0.7261 | 18750 | 0.0 | - |
| 0.7280 | 18800 | 0.0 | - |
| 0.7300 | 18850 | 0.0 | - |
| 0.7319 | 18900 | 0.0 | - |
| 0.7338 | 18950 | 0.0 | - |
| 0.7358 | 19000 | 0.0 | - |
| 0.7377 | 19050 | 0.0 | - |
| 0.7397 | 19100 | 0.0 | - |
| 0.7416 | 19150 | 0.0 | - |
| 0.7435 | 19200 | 0.0 | - |
| 0.7455 | 19250 | 0.0 | - |
| 0.7474 | 19300 | 0.0 | - |
| 0.7493 | 19350 | 0.0 | - |
| 0.7513 | 19400 | 0.0 | - |
| 0.7532 | 19450 | 0.0 | - |
| 0.7551 | 19500 | 0.0 | - |
| 0.7571 | 19550 | 0.0 | - |
| 0.7590 | 19600 | 0.0 | - |
| 0.7609 | 19650 | 0.0 | - |
| 0.7629 | 19700 | 0.0 | - |
| 0.7648 | 19750 | 0.0 | - |
| 0.7668 | 19800 | 0.0 | - |
| 0.7687 | 19850 | 0.0 | - |
| 0.7706 | 19900 | 0.0 | - |
| 0.7726 | 19950 | 0.0 | - |
| 0.7745 | 20000 | 0.0 | - |
| 0.7764 | 20050 | 0.0 | - |
| 0.7784 | 20100 | 0.0 | - |
| 0.7803 | 20150 | 0.0 | - |
| 0.7822 | 20200 | 0.0 | - |
| 0.7842 | 20250 | 0.0 | - |
| 0.7861 | 20300 | 0.0 | - |
| 0.7881 | 20350 | 0.0 | - |
| 0.7900 | 20400 | 0.0 | - |
| 0.7919 | 20450 | 0.0 | - |
| 0.7939 | 20500 | 0.0 | - |
| 0.7958 | 20550 | 0.0 | - |
| 0.7977 | 20600 | 0.0 | - |
| 0.7997 | 20650 | 0.0 | - |
| 0.8016 | 20700 | 0.0 | - |
| 0.8035 | 20750 | 0.0 | - |
| 0.8055 | 20800 | 0.0 | - |
| 0.8074 | 20850 | 0.0 | - |
| 0.8094 | 20900 | 0.0 | - |
| 0.8113 | 20950 | 0.0 | - |
| 0.8132 | 21000 | 0.0 | - |
| 0.8152 | 21050 | 0.0 | - |
| 0.8171 | 21100 | 0.0 | - |
| 0.8190 | 21150 | 0.0 | - |
| 0.8210 | 21200 | 0.0 | - |
| 0.8229 | 21250 | 0.0 | - |
| 0.8248 | 21300 | 0.0 | - |
| 0.8268 | 21350 | 0.0 | - |
| 0.8287 | 21400 | 0.0 | - |
| 0.8307 | 21450 | 0.0 | - |
| 0.8326 | 21500 | 0.0 | - |
| 0.8345 | 21550 | 0.0 | - |
| 0.8365 | 21600 | 0.0 | - |
| 0.8384 | 21650 | 0.0 | - |
| 0.8403 | 21700 | 0.0 | - |
| 0.8423 | 21750 | 0.0 | - |
| 0.8442 | 21800 | 0.0 | - |
| 0.8461 | 21850 | 0.0 | - |
| 0.8481 | 21900 | 0.0 | - |
| 0.8500 | 21950 | 0.0 | - |
| 0.8520 | 22000 | 0.0 | - |
| 0.8539 | 22050 | 0.0 | - |
| 0.8558 | 22100 | 0.0 | - |
| 0.8578 | 22150 | 0.0 | - |
| 0.8597 | 22200 | 0.0 | - |
| 0.8616 | 22250 | 0.0 | - |
| 0.8636 | 22300 | 0.0 | - |
| 0.8655 | 22350 | 0.0 | - |
| 0.8674 | 22400 | 0.0 | - |
| 0.8694 | 22450 | 0.0 | - |
| 0.8713 | 22500 | 0.0 | - |
| 0.8733 | 22550 | 0.0 | - |
| 0.8752 | 22600 | 0.0 | - |
| 0.8771 | 22650 | 0.0 | - |
| 0.8791 | 22700 | 0.0 | - |
| 0.8810 | 22750 | 0.0 | - |
| 0.8829 | 22800 | 0.0 | - |
| 0.8849 | 22850 | 0.0 | - |
| 0.8868 | 22900 | 0.0 | - |
| 0.8887 | 22950 | 0.0 | - |
| 0.8907 | 23000 | 0.0 | - |
| 0.8926 | 23050 | 0.0 | - |
| 0.8946 | 23100 | 0.0 | - |
| 0.8965 | 23150 | 0.0 | - |
| 0.8984 | 23200 | 0.0 | - |
| 0.9004 | 23250 | 0.0 | - |
| 0.9023 | 23300 | 0.0 | - |
| 0.9042 | 23350 | 0.0 | - |
| 0.9062 | 23400 | 0.0 | - |
| 0.9081 | 23450 | 0.0 | - |
| 0.9100 | 23500 | 0.0 | - |
| 0.9120 | 23550 | 0.0 | - |
| 0.9139 | 23600 | 0.0 | - |
| 0.9159 | 23650 | 0.0 | - |
| 0.9178 | 23700 | 0.0 | - |
| 0.9197 | 23750 | 0.0 | - |
| 0.9217 | 23800 | 0.0 | - |
| 0.9236 | 23850 | 0.0 | - |
| 0.9255 | 23900 | 0.0 | - |
| 0.9275 | 23950 | 0.0 | - |
| 0.9294 | 24000 | 0.0 | - |
| 0.9313 | 24050 | 0.0 | - |
| 0.9333 | 24100 | 0.0 | - |
| 0.9352 | 24150 | 0.0 | - |
| 0.9371 | 24200 | 0.0 | - |
| 0.9391 | 24250 | 0.0 | - |
| 0.9410 | 24300 | 0.0 | - |
| 0.9430 | 24350 | 0.0 | - |
| 0.9449 | 24400 | 0.0 | - |
| 0.9468 | 24450 | 0.0 | - |
| 0.9488 | 24500 | 0.0 | - |
| 0.9507 | 24550 | 0.0 | - |
| 0.9526 | 24600 | 0.0 | - |
| 0.9546 | 24650 | 0.0 | - |
| 0.9565 | 24700 | 0.0 | - |
| 0.9584 | 24750 | 0.0 | - |
| 0.9604 | 24800 | 0.0 | - |
| 0.9623 | 24850 | 0.0 | - |
| 0.9643 | 24900 | 0.0 | - |
| 0.9662 | 24950 | 0.0 | - |
| 0.9681 | 25000 | 0.0 | - |
| 0.9701 | 25050 | 0.0 | - |
| 0.9720 | 25100 | 0.0 | - |
| 0.9739 | 25150 | 0.0 | - |
| 0.9759 | 25200 | 0.0 | - |
| 0.9778 | 25250 | 0.0 | - |
| 0.9797 | 25300 | 0.0 | - |
| 0.9817 | 25350 | 0.0 | - |
| 0.9836 | 25400 | 0.0 | - |
| 0.9856 | 25450 | 0.0 | - |
| 0.9875 | 25500 | 0.0 | - |
| 0.9894 | 25550 | 0.0 | - |
| 0.9914 | 25600 | 0.0 | - |
| 0.9933 | 25650 | 0.0 | - |
| 0.9952 | 25700 | 0.0 | - |
| 0.9972 | 25750 | 0.0 | - |
| 0.9991 | 25800 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## 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.*
--> |