File size: 20,803 Bytes
3dab106 |
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 |
---
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: 마스크 오브 매그너민티 315g - 파워 마스크/페이스 앤 바디 마스크 팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 >
입욕제;위메프 > 뷰티 > 스킨케어 > 팩/마스크;위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 워시오프팩 /필오프팩;위메프 > 뷰티 > 클렌징/필링
> 클렌징;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모 > 입욕제;(#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크
> 마스크시트팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 입욕제
- text: '[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용
> 마스크/팩 > 수면팩'
- text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 38개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn
> 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩
- text: 메디힐 마스크팩 티트리 베스트 10매 세트 수분 미백 여드름 비타 라이트빔 에센셜[10매] 홈>화장품/미용>마스크/팩>마스크시트;홈>전체상품;(#M)홈>브랜드관>메디힐
Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트
- text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 29개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>시트마스크 Coupang >
뷰티 > 스킨케어 > 마스크/팩 > 시트마스크
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: 0.7775471698113208
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:** 4 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'차앤박 CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분) (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'</li><li>'미팩토리 3단 돼지코팩 10개입 × 3개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>코팩 Coupang > 뷰티 > 스킨케어 > 마스크/팩'</li><li>'[차앤박] CNP 안티포어 블랙헤드 버블 코팩 1매 / 넓은 모공 피부 / (#M)화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩'</li></ul> |
| 0 | <ul><li>'메디힐×마리끌레르 기획전 앰플/크림/마스크팩~58% 25_메디힐 티트리 케어솔루션 에센셜마스크 [10매] 쇼킹딜 홈>뷰티>클렌징/팩/마스크>팩/마스크;11st>스킨케어>팩/마스크>마스크시트팩;(#M)11st>뷰티>클렌징/팩/마스크>팩/마스크 11st Hour Event > 패션/뷰티 > 뷰티 > 클렌징/팩/마스크 > 팩/마스크'</li><li>'[의료기기] 듀오덤 스팟패치 72매 [의료기기] 듀오덤 스팟패치 72매 (#M)홈>구강/건강용품>패치/겔>스팟패치 OLIVEYOUNG > 베스트 > 구강/건강용품'</li><li>'이지덤 뷰티 릴리프 스팟패치 57개입 3개 (#M)쿠팡 홈>생활용품>건강/의료용품>의약외품/상비용품>반창고/밴드 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 스팟패치'</li></ul> |
| 2 | <ul><li>'안스킨 클래리파잉 골드 모델링 팩 1000ml 20개 (#M)홈>화장품/미용>마스크/팩>필오프팩 Naverstore > 화장품/미용 > 마스크/팩 > 필오프팩'</li><li>'[러쉬]오티픽스 75g - 프레쉬 페이스 마스크/마스크 팩 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크;ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 입욕제/버블바스;ssg > 뷰티 > 스킨케어 > 마스크/팩;ssg > 뷰티 > 스킨케어 > 클렌징 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크'</li><li>'푸드어홀릭 콜라겐 필오프팩 150ml / 다시마 MinSellAmount (#M)화장품/향수>팩/마스크>필오프팩 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 필오프팩'</li></ul> |
| 1 | <ul><li>'물광 콜라겐 크림 티르티르 80ml 생크림 도자기 피부 물광마스크 이유빈 콜라겐물광마스크40ml (#M)홈>전체상품 Naverstore > 화장품/미용 > 남성화장품 > 크림'</li><li>'립 슬리핑 마스크 EX 20g 4종 베리 자몽 민트초코 애플라임 베리 (#M)홈>화장품/미용>마스크/팩>수면팩 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩'</li><li>'설화수 한방 슬리핑마스크 나이트여운팩 120ml 1개 (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7775 |
## 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_bt3")
# Run inference
preds = model("[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩")
```
<!--
### 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 | 11 | 21.75 | 91 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0032 | 1 | 0.4549 | - |
| 0.1597 | 50 | 0.3933 | - |
| 0.3195 | 100 | 0.3669 | - |
| 0.4792 | 150 | 0.2841 | - |
| 0.6390 | 200 | 0.1163 | - |
| 0.7987 | 250 | 0.0104 | - |
| 0.9585 | 300 | 0.0072 | - |
| 1.1182 | 350 | 0.0065 | - |
| 1.2780 | 400 | 0.0059 | - |
| 1.4377 | 450 | 0.0058 | - |
| 1.5974 | 500 | 0.0035 | - |
| 1.7572 | 550 | 0.0032 | - |
| 1.9169 | 600 | 0.0032 | - |
| 2.0767 | 650 | 0.0025 | - |
| 2.2364 | 700 | 0.0023 | - |
| 2.3962 | 750 | 0.0023 | - |
| 2.5559 | 800 | 0.0025 | - |
| 2.7157 | 850 | 0.0023 | - |
| 2.8754 | 900 | 0.003 | - |
| 3.0351 | 950 | 0.0026 | - |
| 3.1949 | 1000 | 0.0043 | - |
| 3.3546 | 1050 | 0.0022 | - |
| 3.5144 | 1100 | 0.0024 | - |
| 3.6741 | 1150 | 0.0025 | - |
| 3.8339 | 1200 | 0.0025 | - |
| 3.9936 | 1250 | 0.0024 | - |
| 4.1534 | 1300 | 0.0025 | - |
| 4.3131 | 1350 | 0.0025 | - |
| 4.4728 | 1400 | 0.0027 | - |
| 4.6326 | 1450 | 0.0023 | - |
| 4.7923 | 1500 | 0.0022 | - |
| 4.9521 | 1550 | 0.0026 | - |
| 5.1118 | 1600 | 0.0022 | - |
| 5.2716 | 1650 | 0.0027 | - |
| 5.4313 | 1700 | 0.0022 | - |
| 5.5911 | 1750 | 0.0024 | - |
| 5.7508 | 1800 | 0.0029 | - |
| 5.9105 | 1850 | 0.0018 | - |
| 6.0703 | 1900 | 0.0033 | - |
| 6.2300 | 1950 | 0.002 | - |
| 6.3898 | 2000 | 0.0027 | - |
| 6.5495 | 2050 | 0.0021 | - |
| 6.7093 | 2100 | 0.0022 | - |
| 6.8690 | 2150 | 0.0023 | - |
| 7.0288 | 2200 | 0.0026 | - |
| 7.1885 | 2250 | 0.0018 | - |
| 7.3482 | 2300 | 0.0024 | - |
| 7.5080 | 2350 | 0.002 | - |
| 7.6677 | 2400 | 0.0027 | - |
| 7.8275 | 2450 | 0.0022 | - |
| 7.9872 | 2500 | 0.0032 | - |
| 8.1470 | 2550 | 0.0029 | - |
| 8.3067 | 2600 | 0.0025 | - |
| 8.4665 | 2650 | 0.0017 | - |
| 8.6262 | 2700 | 0.0026 | - |
| 8.7859 | 2750 | 0.0023 | - |
| 8.9457 | 2800 | 0.0023 | - |
| 9.1054 | 2850 | 0.0029 | - |
| 9.2652 | 2900 | 0.0028 | - |
| 9.4249 | 2950 | 0.0021 | - |
| 9.5847 | 3000 | 0.0027 | - |
| 9.7444 | 3050 | 0.0019 | - |
| 9.9042 | 3100 | 0.0022 | - |
| 10.0639 | 3150 | 0.003 | - |
| 10.2236 | 3200 | 0.0024 | - |
| 10.3834 | 3250 | 0.0019 | - |
| 10.5431 | 3300 | 0.0023 | - |
| 10.7029 | 3350 | 0.0024 | - |
| 10.8626 | 3400 | 0.0026 | - |
| 11.0224 | 3450 | 0.0025 | - |
| 11.1821 | 3500 | 0.0022 | - |
| 11.3419 | 3550 | 0.0023 | - |
| 11.5016 | 3600 | 0.0027 | - |
| 11.6613 | 3650 | 0.0032 | - |
| 11.8211 | 3700 | 0.0022 | - |
| 11.9808 | 3750 | 0.0019 | - |
| 12.1406 | 3800 | 0.0029 | - |
| 12.3003 | 3850 | 0.0026 | - |
| 12.4601 | 3900 | 0.0027 | - |
| 12.6198 | 3950 | 0.0019 | - |
| 12.7796 | 4000 | 0.0021 | - |
| 12.9393 | 4050 | 0.0023 | - |
| 13.0990 | 4100 | 0.0027 | - |
| 13.2588 | 4150 | 0.0021 | - |
| 13.4185 | 4200 | 0.0022 | - |
| 13.5783 | 4250 | 0.0026 | - |
| 13.7380 | 4300 | 0.0025 | - |
| 13.8978 | 4350 | 0.0025 | - |
| 14.0575 | 4400 | 0.0021 | - |
| 14.2173 | 4450 | 0.0031 | - |
| 14.3770 | 4500 | 0.0022 | - |
| 14.5367 | 4550 | 0.0016 | - |
| 14.6965 | 4600 | 0.0027 | - |
| 14.8562 | 4650 | 0.0027 | - |
| 15.0160 | 4700 | 0.0027 | - |
| 15.1757 | 4750 | 0.0021 | - |
| 15.3355 | 4800 | 0.0027 | - |
| 15.4952 | 4850 | 0.0031 | - |
| 15.6550 | 4900 | 0.0021 | - |
| 15.8147 | 4950 | 0.0023 | - |
| 15.9744 | 5000 | 0.002 | - |
| 16.1342 | 5050 | 0.0024 | - |
| 16.2939 | 5100 | 0.0026 | - |
| 16.4537 | 5150 | 0.002 | - |
| 16.6134 | 5200 | 0.0026 | - |
| 16.7732 | 5250 | 0.0029 | - |
| 16.9329 | 5300 | 0.0023 | - |
| 17.0927 | 5350 | 0.0022 | - |
| 17.2524 | 5400 | 0.0028 | - |
| 17.4121 | 5450 | 0.0026 | - |
| 17.5719 | 5500 | 0.0017 | - |
| 17.7316 | 5550 | 0.0032 | - |
| 17.8914 | 5600 | 0.0022 | - |
| 18.0511 | 5650 | 0.0019 | - |
| 18.2109 | 5700 | 0.0024 | - |
| 18.3706 | 5750 | 0.0026 | - |
| 18.5304 | 5800 | 0.0031 | - |
| 18.6901 | 5850 | 0.0024 | - |
| 18.8498 | 5900 | 0.0018 | - |
| 19.0096 | 5950 | 0.0023 | - |
| 19.1693 | 6000 | 0.0025 | - |
| 19.3291 | 6050 | 0.0028 | - |
| 19.4888 | 6100 | 0.002 | - |
| 19.6486 | 6150 | 0.0026 | - |
| 19.8083 | 6200 | 0.0022 | - |
| 19.9681 | 6250 | 0.0025 | - |
| 20.1278 | 6300 | 0.0022 | - |
| 20.2875 | 6350 | 0.0025 | - |
| 20.4473 | 6400 | 0.0024 | - |
| 20.6070 | 6450 | 0.0027 | - |
| 20.7668 | 6500 | 0.0017 | - |
| 20.9265 | 6550 | 0.0025 | - |
| 21.0863 | 6600 | 0.0025 | - |
| 21.2460 | 6650 | 0.002 | - |
| 21.4058 | 6700 | 0.0033 | - |
| 21.5655 | 6750 | 0.0021 | - |
| 21.7252 | 6800 | 0.0022 | - |
| 21.8850 | 6850 | 0.0027 | - |
| 22.0447 | 6900 | 0.0021 | - |
| 22.2045 | 6950 | 0.0028 | - |
| 22.3642 | 7000 | 0.0021 | - |
| 22.5240 | 7050 | 0.0021 | - |
| 22.6837 | 7100 | 0.0027 | - |
| 22.8435 | 7150 | 0.0021 | - |
| 23.0032 | 7200 | 0.0029 | - |
| 23.1629 | 7250 | 0.0036 | - |
| 23.3227 | 7300 | 0.002 | - |
| 23.4824 | 7350 | 0.0021 | - |
| 23.6422 | 7400 | 0.002 | - |
| 23.8019 | 7450 | 0.0025 | - |
| 23.9617 | 7500 | 0.0024 | - |
| 24.1214 | 7550 | 0.0026 | - |
| 24.2812 | 7600 | 0.002 | - |
| 24.4409 | 7650 | 0.0024 | - |
| 24.6006 | 7700 | 0.0025 | - |
| 24.7604 | 7750 | 0.0023 | - |
| 24.9201 | 7800 | 0.0027 | - |
| 25.0799 | 7850 | 0.0023 | - |
| 25.2396 | 7900 | 0.0024 | - |
| 25.3994 | 7950 | 0.0027 | - |
| 25.5591 | 8000 | 0.0038 | - |
| 25.7188 | 8050 | 0.0065 | - |
| 25.8786 | 8100 | 0.0037 | - |
| 26.0383 | 8150 | 0.0032 | - |
| 26.1981 | 8200 | 0.0031 | - |
| 26.3578 | 8250 | 0.0028 | - |
| 26.5176 | 8300 | 0.0024 | - |
| 26.6773 | 8350 | 0.0023 | - |
| 26.8371 | 8400 | 0.0028 | - |
| 26.9968 | 8450 | 0.0023 | - |
| 27.1565 | 8500 | 0.0028 | - |
| 27.3163 | 8550 | 0.0025 | - |
| 27.4760 | 8600 | 0.0027 | - |
| 27.6358 | 8650 | 0.002 | - |
| 27.7955 | 8700 | 0.0024 | - |
| 27.9553 | 8750 | 0.0023 | - |
| 28.1150 | 8800 | 0.0029 | - |
| 28.2748 | 8850 | 0.0025 | - |
| 28.4345 | 8900 | 0.002 | - |
| 28.5942 | 8950 | 0.0025 | - |
| 28.7540 | 9000 | 0.002 | - |
| 28.9137 | 9050 | 0.0027 | - |
| 29.0735 | 9100 | 0.0028 | - |
| 29.2332 | 9150 | 0.0016 | - |
| 29.3930 | 9200 | 0.0032 | - |
| 29.5527 | 9250 | 0.0026 | - |
| 29.7125 | 9300 | 0.0025 | - |
| 29.8722 | 9350 | 0.0025 | - |
### 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.*
--> |