File size: 27,520 Bytes
2b4334b |
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 |
---
base_model: klue/roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 아이오페 맨 컴파운드 선스크린 SPF50+ PA++++ 50ml 맨 컴파운드 선스크린 (#M)홈>화장품/미용>남성화장품>선크림 Naverstore
> 화장품/미용 > 남성화장품 > 선크림
- text: 2개 이니스프리 톤업 워터링/안티폴루션/톤업 노세범/롱래스팅/트리플쉴드 /선스크린/선크림 이니스프리_인텐시브 롱래스팅 선스크린_이니스프리_아쿠아
무기자차 선스크린 (#M)11st>선케어>선크림/선블록>선크림/선블록 11st > 뷰티 > 선케어 > 선크림/선블록
- text: 이니스프리 트루 히알루론 수분 선크림 SPF50+ PA++++ 50ml × 5개 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림
LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림
- text: 노세범 선쿠션14gSPF50+PA++++ MinSellAmount (#M)화장품/향수>베이스메이크업>파우더/트윈케이크 Gmarket
> 뷰티 > 화장품/향수 > 베이스메이크업 > 파우더/트윈케이크
- text: 이니스프리 톤업 노세범 선스크린 EX 50mlx2개 이니스프리 인텐시브 롱래스팅 선스크린EX 50mlx2개 LotteOn > 뷰티 >
스킨케어 > 선케어 > 선크림 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9708550745075918
name: Accuracy
---
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'[K쇼핑]프리메라 베이비 선 쿠션 15g 프리메라 베이비 선 쿠션 15g_수량_상세페이지참조 (#M)쿠팡 홈>뷰티>스킨케어>로션 Coupang > 뷰티 > 스킨케어 > 로션'</li><li>'이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 9개 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'</li><li>'엘로엘 팡팡 빅 선쿠션 시즌6 본품 25g(SPF50+) 엘로엘 팡팡 빅 선쿠션 시즌6 본품 25g (SPF50+) (#M)11st>선케어>선파우더/쿠션>선파우더/쿠션 11st > 뷰티 > 선케어 > 선파우더/쿠션 > 선파우더/쿠션'</li></ul> |
| 1 | <ul><li>'[SNP] 아쿠아 쿨링 선 스프레이 200ml 단일상품 (#M)뷰티>화장품/향수>선케어>선크림/선로션 CJmall > 뷰티 > 화장품/향수 > 선케어 > 선크림/선로션'</li><li>'SNP 쿨링 선스프레이/자외선차단 뿌리는 선크림 MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'</li><li>'리더스 썬버디 올 오버 선 스프레이 90ml (#M)GSSHOP>뷰티>선케어>선크림 GSSHOP > 뷰티 > 선케어 > 선크림'</li></ul> |
| 0 | <ul><li>'아웃런 골프 선스틱 더프로페셔널 20g 골프공세트 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선스틱 Coupang > 뷰티 > 스킨케어 > 선케어/태닝 > 선케어 > 선스틱'</li><li>'아웃런 골프 선스틱 SPF50+ PA++++ 20g + 골프공 세트 1세트 (#M)11st>스킨케어>로션/에멀션>로션/에멀션 11st > 뷰티 > 스킨케어 > 로션/에멀션 > 로션/에멀션'</li><li>'[국내 직배송 3일 배송] YCCTG아웃런 익스트림 선스틱 화이트 18g 단일상품 - 8791개 남음 (#M)쿠팡 홈>출산/유아동>욕실용품/스킨케어>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트'</li></ul> |
| 4 | <ul><li>'오스트레일리안 골드 다크 태닝 스프레이 젤 237ml (#M)11st>바디케어>태닝용품>오일 11st > 뷰티 > 바디케어 > 태닝용품 > 오일'</li><li>'(1+1+1)푸드어홀릭 알로에 수딩젤 300ml 옵션없음 ssg > 뷰티 > 스킨케어 > 크림 ssg > 뷰티 > 스킨케어 > 크림'</li><li>'[박스포함]수딩 알로에 젤 300ml + 유기농 호호바 오일 LotteOn > 뷰티 > 바디케어 > 바디케어세트 LotteOn > 뷰티 > 바디케어 > 바디케어세트'</li></ul> |
| 3 | <ul><li>'[듀이트리] 어반쉐이드 안티폴루션 선 톤업 선크림 기획 50ml SPF50+PA++++ 본품50ml+\ufeff클렌저50ml(마스크증정) 홈>🎉브랜드 위크 SALE★;홈>🎉얼리 썸머케어 이벤트!;홈>🏆뷰티시상식 #수상템;홈>😷슬기로운 집콕! 뷰티콕!;홈>👍BEST 6;홈>👍한글날 기념 연휴기획전;홈>👍\ufeff고객감사 사은품 기획전!;홈>👍베스트 6 주말특가!;홈>👍베스트 6 ⚡아이패치 증정;홈>👍베스트 6 초특가행사!!;홈>💥9,900원 주말특가;홈>⭐앵콜⭐마스크 30매 증정;홈>👍베스트 6;홈>👍베스트 10 (마스크증정);홈>👍베스트 5;홈>👍베스트 10 황사대책!;홈>👍베스트 10;홈>🏆뷰티수상템;홈>💄뷰티풀데이;홈>전체상품;홈>👍베스트10 주말특가⚡;홈>💄뷰티윈도 브랜드기획전;홈>🌞 핫썸머 바캉스 이벤트;홈>⚡주말특가 2천포인트 즉시지급;홈>🎁추석맞이 사은증정 행사⚡;홈>🎁고객감사 사은증정 행사⚡;홈>👍베스트5 주말특가⚡;홈>👍베스트10 주말번개특가⚡;홈>🌻선케어 신상품 출시;홈>🌞선케어 주말특가!;홈>👍베스트10;홈>🌞선케어 슈퍼위크;(#M)홈>⛱프리썸머위크 Naverstore > 화장품/미용 > 선케어 > 선크림'</li><li>'어퓨 퓨어 블록 데일리/선베이스/플러스 선크림 50ml _34187_톤업 선 베이스50ml (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션 Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝 > 선케어 > 선블록/선크림/선로션'</li><li>'이니스프리 트루 히알루론 수분 선크림 35ml(SPF50+) (#M)홈>화장품/미용>선케어>선크림 Naverstore > 화장품/미용 > 선케어 > 선크림'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9709 |
## 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("mini1013/master_item_top_bt8")
# Run inference
preds = model("아이오페 맨 컴파운드 선스크린 SPF50+ PA++++ 50ml 맨 컴파운드 선스크린 (#M)홈>화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림")
```
<!--
### 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 | 12 | 22.664 | 96 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0026 | 1 | 0.4962 | - |
| 0.1279 | 50 | 0.4318 | - |
| 0.2558 | 100 | 0.3859 | - |
| 0.3836 | 150 | 0.2819 | - |
| 0.5115 | 200 | 0.1477 | - |
| 0.6394 | 250 | 0.0942 | - |
| 0.7673 | 300 | 0.0705 | - |
| 0.8951 | 350 | 0.0065 | - |
| 1.0230 | 400 | 0.0003 | - |
| 1.1509 | 450 | 0.0001 | - |
| 1.2788 | 500 | 0.0001 | - |
| 1.4066 | 550 | 0.0002 | - |
| 1.5345 | 600 | 0.0002 | - |
| 1.6624 | 650 | 0.0001 | - |
| 1.7903 | 700 | 0.0 | - |
| 1.9182 | 750 | 0.0 | - |
| 2.0460 | 800 | 0.0 | - |
| 2.1739 | 850 | 0.0 | - |
| 2.3018 | 900 | 0.0 | - |
| 2.4297 | 950 | 0.0 | - |
| 2.5575 | 1000 | 0.0 | - |
| 2.6854 | 1050 | 0.0 | - |
| 2.8133 | 1100 | 0.0 | - |
| 2.9412 | 1150 | 0.0 | - |
| 3.0691 | 1200 | 0.0 | - |
| 3.1969 | 1250 | 0.0 | - |
| 3.3248 | 1300 | 0.0 | - |
| 3.4527 | 1350 | 0.0 | - |
| 3.5806 | 1400 | 0.0 | - |
| 3.7084 | 1450 | 0.0 | - |
| 3.8363 | 1500 | 0.0 | - |
| 3.9642 | 1550 | 0.0 | - |
| 4.0921 | 1600 | 0.0 | - |
| 4.2199 | 1650 | 0.0 | - |
| 4.3478 | 1700 | 0.0002 | - |
| 4.4757 | 1750 | 0.0005 | - |
| 4.6036 | 1800 | 0.0 | - |
| 4.7315 | 1850 | 0.0 | - |
| 4.8593 | 1900 | 0.0 | - |
| 4.9872 | 1950 | 0.0 | - |
| 5.1151 | 2000 | 0.0 | - |
| 5.2430 | 2050 | 0.0003 | - |
| 5.3708 | 2100 | 0.0001 | - |
| 5.4987 | 2150 | 0.0 | - |
| 5.6266 | 2200 | 0.0 | - |
| 5.7545 | 2250 | 0.0 | - |
| 5.8824 | 2300 | 0.0 | - |
| 6.0102 | 2350 | 0.0 | - |
| 6.1381 | 2400 | 0.0003 | - |
| 6.2660 | 2450 | 0.0 | - |
| 6.3939 | 2500 | 0.0007 | - |
| 6.5217 | 2550 | 0.0008 | - |
| 6.6496 | 2600 | 0.0 | - |
| 6.7775 | 2650 | 0.0 | - |
| 6.9054 | 2700 | 0.0 | - |
| 7.0332 | 2750 | 0.0 | - |
| 7.1611 | 2800 | 0.0 | - |
| 7.2890 | 2850 | 0.0 | - |
| 7.4169 | 2900 | 0.0 | - |
| 7.5448 | 2950 | 0.0 | - |
| 7.6726 | 3000 | 0.0 | - |
| 7.8005 | 3050 | 0.0 | - |
| 7.9284 | 3100 | 0.0 | - |
| 8.0563 | 3150 | 0.0 | - |
| 8.1841 | 3200 | 0.0 | - |
| 8.3120 | 3250 | 0.0 | - |
| 8.4399 | 3300 | 0.0 | - |
| 8.5678 | 3350 | 0.0 | - |
| 8.6957 | 3400 | 0.0 | - |
| 8.8235 | 3450 | 0.0 | - |
| 8.9514 | 3500 | 0.0 | - |
| 9.0793 | 3550 | 0.0 | - |
| 9.2072 | 3600 | 0.0 | - |
| 9.3350 | 3650 | 0.0003 | - |
| 9.4629 | 3700 | 0.0004 | - |
| 9.5908 | 3750 | 0.0 | - |
| 9.7187 | 3800 | 0.0 | - |
| 9.8465 | 3850 | 0.0 | - |
| 9.9744 | 3900 | 0.0 | - |
| 10.1023 | 3950 | 0.0 | - |
| 10.2302 | 4000 | 0.0 | - |
| 10.3581 | 4050 | 0.0 | - |
| 10.4859 | 4100 | 0.0 | - |
| 10.6138 | 4150 | 0.0 | - |
| 10.7417 | 4200 | 0.0 | - |
| 10.8696 | 4250 | 0.0 | - |
| 10.9974 | 4300 | 0.0 | - |
| 11.1253 | 4350 | 0.0 | - |
| 11.2532 | 4400 | 0.0 | - |
| 11.3811 | 4450 | 0.0 | - |
| 11.5090 | 4500 | 0.0 | - |
| 11.6368 | 4550 | 0.0 | - |
| 11.7647 | 4600 | 0.0 | - |
| 11.8926 | 4650 | 0.0 | - |
| 12.0205 | 4700 | 0.0 | - |
| 12.1483 | 4750 | 0.0 | - |
| 12.2762 | 4800 | 0.0 | - |
| 12.4041 | 4850 | 0.0 | - |
| 12.5320 | 4900 | 0.0 | - |
| 12.6598 | 4950 | 0.0 | - |
| 12.7877 | 5000 | 0.0 | - |
| 12.9156 | 5050 | 0.0 | - |
| 13.0435 | 5100 | 0.0 | - |
| 13.1714 | 5150 | 0.0 | - |
| 13.2992 | 5200 | 0.0 | - |
| 13.4271 | 5250 | 0.0 | - |
| 13.5550 | 5300 | 0.0 | - |
| 13.6829 | 5350 | 0.0 | - |
| 13.8107 | 5400 | 0.0 | - |
| 13.9386 | 5450 | 0.0 | - |
| 14.0665 | 5500 | 0.0 | - |
| 14.1944 | 5550 | 0.0 | - |
| 14.3223 | 5600 | 0.0 | - |
| 14.4501 | 5650 | 0.0 | - |
| 14.5780 | 5700 | 0.0 | - |
| 14.7059 | 5750 | 0.0 | - |
| 14.8338 | 5800 | 0.0 | - |
| 14.9616 | 5850 | 0.0008 | - |
| 15.0895 | 5900 | 0.0002 | - |
| 15.2174 | 5950 | 0.0 | - |
| 15.3453 | 6000 | 0.0 | - |
| 15.4731 | 6050 | 0.0 | - |
| 15.6010 | 6100 | 0.0 | - |
| 15.7289 | 6150 | 0.0 | - |
| 15.8568 | 6200 | 0.0 | - |
| 15.9847 | 6250 | 0.0 | - |
| 16.1125 | 6300 | 0.0 | - |
| 16.2404 | 6350 | 0.0 | - |
| 16.3683 | 6400 | 0.0 | - |
| 16.4962 | 6450 | 0.0 | - |
| 16.6240 | 6500 | 0.0 | - |
| 16.7519 | 6550 | 0.0 | - |
| 16.8798 | 6600 | 0.0 | - |
| 17.0077 | 6650 | 0.0 | - |
| 17.1355 | 6700 | 0.0 | - |
| 17.2634 | 6750 | 0.0 | - |
| 17.3913 | 6800 | 0.0 | - |
| 17.5192 | 6850 | 0.0 | - |
| 17.6471 | 6900 | 0.0 | - |
| 17.7749 | 6950 | 0.0 | - |
| 17.9028 | 7000 | 0.0 | - |
| 18.0307 | 7050 | 0.0 | - |
| 18.1586 | 7100 | 0.0 | - |
| 18.2864 | 7150 | 0.0 | - |
| 18.4143 | 7200 | 0.0 | - |
| 18.5422 | 7250 | 0.0 | - |
| 18.6701 | 7300 | 0.0 | - |
| 18.7980 | 7350 | 0.0 | - |
| 18.9258 | 7400 | 0.0 | - |
| 19.0537 | 7450 | 0.0 | - |
| 19.1816 | 7500 | 0.0 | - |
| 19.3095 | 7550 | 0.0 | - |
| 19.4373 | 7600 | 0.0 | - |
| 19.5652 | 7650 | 0.0 | - |
| 19.6931 | 7700 | 0.0 | - |
| 19.8210 | 7750 | 0.0 | - |
| 19.9488 | 7800 | 0.0 | - |
| 20.0767 | 7850 | 0.0 | - |
| 20.2046 | 7900 | 0.0 | - |
| 20.3325 | 7950 | 0.0 | - |
| 20.4604 | 8000 | 0.0 | - |
| 20.5882 | 8050 | 0.0 | - |
| 20.7161 | 8100 | 0.0 | - |
| 20.8440 | 8150 | 0.0 | - |
| 20.9719 | 8200 | 0.0 | - |
| 21.0997 | 8250 | 0.0 | - |
| 21.2276 | 8300 | 0.0 | - |
| 21.3555 | 8350 | 0.0 | - |
| 21.4834 | 8400 | 0.0 | - |
| 21.6113 | 8450 | 0.0 | - |
| 21.7391 | 8500 | 0.0 | - |
| 21.8670 | 8550 | 0.0 | - |
| 21.9949 | 8600 | 0.0 | - |
| 22.1228 | 8650 | 0.0 | - |
| 22.2506 | 8700 | 0.0 | - |
| 22.3785 | 8750 | 0.0 | - |
| 22.5064 | 8800 | 0.0 | - |
| 22.6343 | 8850 | 0.0 | - |
| 22.7621 | 8900 | 0.0 | - |
| 22.8900 | 8950 | 0.0 | - |
| 23.0179 | 9000 | 0.0 | - |
| 23.1458 | 9050 | 0.0 | - |
| 23.2737 | 9100 | 0.0 | - |
| 23.4015 | 9150 | 0.0 | - |
| 23.5294 | 9200 | 0.0 | - |
| 23.6573 | 9250 | 0.0 | - |
| 23.7852 | 9300 | 0.0 | - |
| 23.9130 | 9350 | 0.0 | - |
| 24.0409 | 9400 | 0.0 | - |
| 24.1688 | 9450 | 0.0 | - |
| 24.2967 | 9500 | 0.0 | - |
| 24.4246 | 9550 | 0.0001 | - |
| 24.5524 | 9600 | 0.0 | - |
| 24.6803 | 9650 | 0.0 | - |
| 24.8082 | 9700 | 0.0 | - |
| 24.9361 | 9750 | 0.0 | - |
| 25.0639 | 9800 | 0.0 | - |
| 25.1918 | 9850 | 0.0 | - |
| 25.3197 | 9900 | 0.0 | - |
| 25.4476 | 9950 | 0.0 | - |
| 25.5754 | 10000 | 0.0 | - |
| 25.7033 | 10050 | 0.0 | - |
| 25.8312 | 10100 | 0.0 | - |
| 25.9591 | 10150 | 0.0 | - |
| 26.0870 | 10200 | 0.0 | - |
| 26.2148 | 10250 | 0.0 | - |
| 26.3427 | 10300 | 0.0 | - |
| 26.4706 | 10350 | 0.0 | - |
| 26.5985 | 10400 | 0.0 | - |
| 26.7263 | 10450 | 0.0 | - |
| 26.8542 | 10500 | 0.0 | - |
| 26.9821 | 10550 | 0.0 | - |
| 27.1100 | 10600 | 0.0 | - |
| 27.2379 | 10650 | 0.0 | - |
| 27.3657 | 10700 | 0.0 | - |
| 27.4936 | 10750 | 0.0 | - |
| 27.6215 | 10800 | 0.0 | - |
| 27.7494 | 10850 | 0.0 | - |
| 27.8772 | 10900 | 0.0 | - |
| 28.0051 | 10950 | 0.0 | - |
| 28.1330 | 11000 | 0.0 | - |
| 28.2609 | 11050 | 0.0 | - |
| 28.3887 | 11100 | 0.0 | - |
| 28.5166 | 11150 | 0.0 | - |
| 28.6445 | 11200 | 0.0 | - |
| 28.7724 | 11250 | 0.0 | - |
| 28.9003 | 11300 | 0.0 | - |
| 29.0281 | 11350 | 0.0 | - |
| 29.1560 | 11400 | 0.0 | - |
| 29.2839 | 11450 | 0.0 | - |
| 29.4118 | 11500 | 0.0 | - |
| 29.5396 | 11550 | 0.0 | - |
| 29.6675 | 11600 | 0.0 | - |
| 29.7954 | 11650 | 0.0 | - |
| 29.9233 | 11700 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- 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.*
--> |