setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:klue/roberta-base base_model:finetune:klue/roberta-base model-index region:us
---
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: 7 GRAB ME 레드 생로랑 Volupte 리퀴드 컬러 Balm 풀사이즈 B 20대여자 옵션없음 남인터내셔널
- text: 힐러랩 울트라본드 케라틴 단백질 트리트먼트 500ml 옵션없음 주식회사 와이제이비앤
- text: 바이오더마 센시비오 클렌징밀크 250ml 옵션없음 주식회사 하이유로
- text: 에스테티카 데미지 케어 컨센트레이트120ml /헤어오일 에센스 세럼 옵션없음 주식회사 베로유코스메틱
- text: 브이티코스메틱 VT 리들샷 700 시너지리페어 크림 옵션없음 북극곰마켓
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: 0.7873765467135884
      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:** 13 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                                                                                                                                                                                             |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'태닝키티파츠 TKT-02-08 썬탠키티 5개입 탄 갸루 하와이 비키니 태닝키티파츠 TKT-02-01 5개입 임프주식회사'</li><li>'지비츠 네일아트 회전 스피너 파츠 부자재 네일 베어링 2.5 사이즈 띠부'</li><li>'루핀 젤클리너 젤리무버 500ml 아세톤 젤클렌져 루핀젤리무버500ml 신나라닷컴'</li></ul>    |
| 7.0   | <ul><li>'마담미쉘 알로베라 퓨어 겔 400ml 알로에 99% 수딩젤 옵션없음 달이커머스'</li><li>'에콜린 비건 밀크 선스크린 50 ml [단품] 비건밀크 선스크린 (18900원) 오가니아(주)'</li><li>'비욘드 엔젤 아쿠아 보습 장벽 선퀴드 50ml_비건 옵션없음 빈티지브릿지'</li></ul>                    |
| 12.0  | <ul><li>'[르네휘테르]나뚜리아 드라이 샴푸 75ml 나뚜리아 드라이 샴푸 75ml 이로븐'</li><li>'탑스칼프 헤어토닉 120ml 두피 클리닉 탑스칼프 탈모샴푸 300ml 주식회사휴웰'</li><li>'케라스타즈 뉴트리티브 마스퀸텐스 리슈 200ml 3474637155001 옵션없음 퓨쳐 디엠 (FUTURE DIEM)'</li></ul> |
| 2.0   | <ul><li>'아나프노 근육 통증 스포츠 온열 찜질 마사지 크림 100ml 아나프노크림 100ml x 1개 케이제이미디어'</li><li>'참존 콘트롤 크림 셀프 마사지 150ml 1개 옵션없음 주식회사 리플레이'</li><li>'리플로우 파워샷 고주파 마사지 크림 옵션없음 호원'</li></ul>                             |
| 8.0   | <ul><li>'가히 멀티밤 CV 본품 9g 가히 멀티밤 CV 본품 1개 ㈜코리아테크'</li><li>'프레티 바이옴 콜라겐 아이크림 30ml 20개 옵션없음 건강드림'</li><li>'르네셀 멀티 펩타이드 토너(재고정리) 옵션없음 숙이네 잡화'</li></ul>                                                  |
| 6.0   | <ul><li>'1/1+1 스틸 마스카라 내추럴 롱래쉬 볼륨 워터프루프 메탈 마스카라 01 블랙x2 와이우'</li><li>'딸리까 리포실 압솔뤼 10ml 옵션없음 스루치로 유한책임회사'</li><li>'디올 어딕트 립 맥시마이저 020 마호가니 옵션없음 스터닝헌팅'</li></ul>                                      |
| 0.0   | <ul><li>'비비드쏘울 위치하젤 젠틀 카밍 앰플 마스크 119 10매 비비드쏘울'</li><li>'알롱 인티메이트 클린저 남성용 청결제 200ml 4개 옵션없음 건강드림'</li><li>'꽃을든남자 이모션 스킨 옵션없음 테디코스'</li></ul>                                                         |
| 4.0   | <ul><li>'AHC 아우라 시크릿 톤업 벨벳 35g+10g 2개 AOD_01)35g+10g 2개+쇼핑백 (주)카버코리아'</li><li>'KANEBO (가네보) 라이브 리스킨 웨어 핑크 오크르 B1개 (x1) 옵션없음 HaruHaru'</li><li>'기획상품 애리조 커버 TV스틱 올 추천템 No.5 다크 베이지 핑쇼59'</li></ul>    |
| 9.0   | <ul><li>'바이오뷰텍 바이오옵틱스 아이크린 리드 클리너 30매 1022127 옵션없음 굿데이'</li><li>'휴대용 종이비누 33종 (챠리, 큐리아, 효겐, 크럭스) 17_효겐 동물 친구들 피코스 드 코레아'</li><li>'식물나라 제주 탄산수 퀵 립 앤 아이 리무버 300ml 옵션없음 밀레니엄'</li></ul>                |
| 10.0  | <ul><li>'장폴고티에 르말 엘릭서 퍼퓸 200ML 옵션없음 비즈앤플랜 주식회사'</li><li>'인센스홀더향 향꽂이 홀더 물방울 인테리어 인센스 (WD2F3FF) 본상품선택 기타/해당사항 없음'</li><li>'디퓨저 섬유 리드스틱 화이트 50개입 디퓨저 섬유 옵션없음 '</li></ul>                                |
| 11.0  | <ul><li>'아모스 휘핑 컬리 펌 옵션없음 티비'</li><li>'과일나라 컨퓸 씨피 헤어 글레이즈 600ml 3개팩 옵션없음 (주)금옥승유통'</li><li>'[소망] 감자 시스테인 1,2제 각 100ml 옵션없음 희망 미용유통'</li></ul>                                                        |
| 5.0   | <ul><li>'라이프홀릭 매너 커버 니플 밴드 100p 3개 one option 옵션없음 지엘디'</li><li>'홈아트 반짝이 빗세트 2P 브러쉬 머리단정 일자빗 옵션없음 제이커머스'</li><li>'가루 파우더 케이스 30g 노세범 땀띠 파우더 소분 공병 (스푼 ) 30g 선데이베리베스트'</li></ul>                      |
| 3.0   | <ul><li>'몬스터팩토리 샤샤샥 제모 크림 100g 옵션없음 주식회사스피드런'</li><li>'라끄베르 때밀이 바디필링 살국수 때필링 300ml 1개 옵션없음 제이앤더블유'</li><li>'꽃보다잠 코사랑크림 유칼립투스오일밤 비염 옵션없음 더 웰리스'</li></ul>                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7874   |

## 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_bt_test")
# Run inference
preds = model("바이오더마 센시비오 클렌징밀크 250ml 옵션없음 주식회사 하이유로")
```

<!--
### 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   | 3   | 9.3971 | 26  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 242                   |
| 1.0   | 134                   |
| 2.0   | 161                   |
| 3.0   | 324                   |
| 4.0   | 141                   |
| 5.0   | 130                   |
| 6.0   | 267                   |
| 7.0   | 133                   |
| 8.0   | 257                   |
| 9.0   | 251                   |
| 10.0  | 63                    |
| 11.0  | 117                   |
| 12.0  | 152                   |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0036  | 1     | 0.4112        | -               |
| 0.1799  | 50    | 0.3996        | -               |
| 0.3597  | 100   | 0.3542        | -               |
| 0.5396  | 150   | 0.3073        | -               |
| 0.7194  | 200   | 0.2654        | -               |
| 0.8993  | 250   | 0.2289        | -               |
| 1.0791  | 300   | 0.1949        | -               |
| 1.2590  | 350   | 0.1619        | -               |
| 1.4388  | 400   | 0.1254        | -               |
| 1.6187  | 450   | 0.0899        | -               |
| 1.7986  | 500   | 0.0645        | -               |
| 1.9784  | 550   | 0.0506        | -               |
| 2.1583  | 600   | 0.0403        | -               |
| 2.3381  | 650   | 0.0365        | -               |
| 2.5180  | 700   | 0.0342        | -               |
| 2.6978  | 750   | 0.0329        | -               |
| 2.8777  | 800   | 0.0302        | -               |
| 3.0576  | 850   | 0.0286        | -               |
| 3.2374  | 900   | 0.0272        | -               |
| 3.4173  | 950   | 0.0246        | -               |
| 3.5971  | 1000  | 0.0229        | -               |
| 3.7770  | 1050  | 0.0206        | -               |
| 3.9568  | 1100  | 0.0139        | -               |
| 4.1367  | 1150  | 0.0083        | -               |
| 4.3165  | 1200  | 0.0071        | -               |
| 4.4964  | 1250  | 0.0071        | -               |
| 4.6763  | 1300  | 0.0057        | -               |
| 4.8561  | 1350  | 0.0045        | -               |
| 5.0360  | 1400  | 0.0036        | -               |
| 5.2158  | 1450  | 0.0031        | -               |
| 5.3957  | 1500  | 0.0011        | -               |
| 5.5755  | 1550  | 0.0006        | -               |
| 5.7554  | 1600  | 0.0004        | -               |
| 5.9353  | 1650  | 0.0004        | -               |
| 6.1151  | 1700  | 0.0003        | -               |
| 6.2950  | 1750  | 0.0003        | -               |
| 6.4748  | 1800  | 0.0002        | -               |
| 6.6547  | 1850  | 0.0002        | -               |
| 6.8345  | 1900  | 0.0002        | -               |
| 7.0144  | 1950  | 0.0002        | -               |
| 7.1942  | 2000  | 0.0002        | -               |
| 7.3741  | 2050  | 0.0002        | -               |
| 7.5540  | 2100  | 0.0001        | -               |
| 7.7338  | 2150  | 0.0002        | -               |
| 7.9137  | 2200  | 0.0001        | -               |
| 8.0935  | 2250  | 0.0001        | -               |
| 8.2734  | 2300  | 0.0002        | -               |
| 8.4532  | 2350  | 0.0002        | -               |
| 8.6331  | 2400  | 0.0005        | -               |
| 8.8129  | 2450  | 0.0003        | -               |
| 8.9928  | 2500  | 0.0002        | -               |
| 9.1727  | 2550  | 0.0001        | -               |
| 9.3525  | 2600  | 0.0001        | -               |
| 9.5324  | 2650  | 0.0002        | -               |
| 9.7122  | 2700  | 0.0001        | -               |
| 9.8921  | 2750  | 0.0002        | -               |
| 10.0719 | 2800  | 0.0001        | -               |
| 10.2518 | 2850  | 0.0001        | -               |
| 10.4317 | 2900  | 0.0002        | -               |
| 10.6115 | 2950  | 0.0003        | -               |
| 10.7914 | 3000  | 0.0002        | -               |
| 10.9712 | 3050  | 0.0004        | -               |
| 11.1511 | 3100  | 0.0003        | -               |
| 11.3309 | 3150  | 0.0002        | -               |
| 11.5108 | 3200  | 0.0001        | -               |
| 11.6906 | 3250  | 0.0001        | -               |
| 11.8705 | 3300  | 0.0001        | -               |
| 12.0504 | 3350  | 0.0001        | -               |
| 12.2302 | 3400  | 0.0           | -               |
| 12.4101 | 3450  | 0.0           | -               |
| 12.5899 | 3500  | 0.0001        | -               |
| 12.7698 | 3550  | 0.0001        | -               |
| 12.9496 | 3600  | 0.0003        | -               |
| 13.1295 | 3650  | 0.0002        | -               |
| 13.3094 | 3700  | 0.0002        | -               |
| 13.4892 | 3750  | 0.0004        | -               |
| 13.6691 | 3800  | 0.0002        | -               |
| 13.8489 | 3850  | 0.0001        | -               |
| 14.0288 | 3900  | 0.0001        | -               |
| 14.2086 | 3950  | 0.0002        | -               |
| 14.3885 | 4000  | 0.0001        | -               |
| 14.5683 | 4050  | 0.0001        | -               |
| 14.7482 | 4100  | 0.0           | -               |
| 14.9281 | 4150  | 0.0001        | -               |
| 15.1079 | 4200  | 0.0003        | -               |
| 15.2878 | 4250  | 0.0002        | -               |
| 15.4676 | 4300  | 0.0001        | -               |
| 15.6475 | 4350  | 0.0001        | -               |
| 15.8273 | 4400  | 0.0           | -               |
| 16.0072 | 4450  | 0.0           | -               |
| 16.1871 | 4500  | 0.0           | -               |
| 16.3669 | 4550  | 0.0           | -               |
| 16.5468 | 4600  | 0.0           | -               |
| 16.7266 | 4650  | 0.0           | -               |
| 16.9065 | 4700  | 0.0           | -               |
| 17.0863 | 4750  | 0.0           | -               |
| 17.2662 | 4800  | 0.0001        | -               |
| 17.4460 | 4850  | 0.0           | -               |
| 17.6259 | 4900  | 0.0           | -               |
| 17.8058 | 4950  | 0.0           | -               |
| 17.9856 | 5000  | 0.0002        | -               |
| 18.1655 | 5050  | 0.0002        | -               |
| 18.3453 | 5100  | 0.0002        | -               |
| 18.5252 | 5150  | 0.0005        | -               |
| 18.7050 | 5200  | 0.0001        | -               |
| 18.8849 | 5250  | 0.0           | -               |
| 19.0647 | 5300  | 0.0           | -               |
| 19.2446 | 5350  | 0.0           | -               |
| 19.4245 | 5400  | 0.0           | -               |
| 19.6043 | 5450  | 0.0           | -               |
| 19.7842 | 5500  | 0.0           | -               |
| 19.9640 | 5550  | 0.0001        | -               |
| 20.1439 | 5600  | 0.0           | -               |
| 20.3237 | 5650  | 0.0001        | -               |
| 20.5036 | 5700  | 0.0002        | -               |
| 20.6835 | 5750  | 0.0001        | -               |
| 20.8633 | 5800  | 0.0001        | -               |
| 21.0432 | 5850  | 0.0003        | -               |
| 21.2230 | 5900  | 0.0002        | -               |
| 21.4029 | 5950  | 0.0001        | -               |
| 21.5827 | 6000  | 0.0           | -               |
| 21.7626 | 6050  | 0.0           | -               |
| 21.9424 | 6100  | 0.0           | -               |
| 22.1223 | 6150  | 0.0           | -               |
| 22.3022 | 6200  | 0.0           | -               |
| 22.4820 | 6250  | 0.0           | -               |
| 22.6619 | 6300  | 0.0           | -               |
| 22.8417 | 6350  | 0.0           | -               |
| 23.0216 | 6400  | 0.0           | -               |
| 23.2014 | 6450  | 0.0           | -               |
| 23.3813 | 6500  | 0.0           | -               |
| 23.5612 | 6550  | 0.0           | -               |
| 23.7410 | 6600  | 0.0           | -               |
| 23.9209 | 6650  | 0.0           | -               |
| 24.1007 | 6700  | 0.0           | -               |
| 24.2806 | 6750  | 0.0           | -               |
| 24.4604 | 6800  | 0.0           | -               |
| 24.6403 | 6850  | 0.0           | -               |
| 24.8201 | 6900  | 0.0           | -               |
| 25.0    | 6950  | 0.0           | -               |
| 25.1799 | 7000  | 0.0           | -               |
| 25.3597 | 7050  | 0.0           | -               |
| 25.5396 | 7100  | 0.0           | -               |
| 25.7194 | 7150  | 0.0           | -               |
| 25.8993 | 7200  | 0.0001        | -               |
| 26.0791 | 7250  | 0.0001        | -               |
| 26.2590 | 7300  | 0.0005        | -               |
| 26.4388 | 7350  | 0.0002        | -               |
| 26.6187 | 7400  | 0.0           | -               |
| 26.7986 | 7450  | 0.0           | -               |
| 26.9784 | 7500  | 0.0           | -               |
| 27.1583 | 7550  | 0.0           | -               |
| 27.3381 | 7600  | 0.0           | -               |
| 27.5180 | 7650  | 0.0           | -               |
| 27.6978 | 7700  | 0.0           | -               |
| 27.8777 | 7750  | 0.0002        | -               |
| 28.0576 | 7800  | 0.0001        | -               |
| 28.2374 | 7850  | 0.0001        | -               |
| 28.4173 | 7900  | 0.0           | -               |
| 28.5971 | 7950  | 0.0001        | -               |
| 28.7770 | 8000  | 0.0001        | -               |
| 28.9568 | 8050  | 0.0001        | -               |
| 29.1367 | 8100  | 0.0001        | -               |
| 29.3165 | 8150  | 0.0001        | -               |
| 29.4964 | 8200  | 0.0           | -               |
| 29.6763 | 8250  | 0.0           | -               |
| 29.8561 | 8300  | 0.0           | -               |
| 30.0360 | 8350  | 0.0           | -               |
| 30.2158 | 8400  | 0.0           | -               |
| 30.3957 | 8450  | 0.0           | -               |
| 30.5755 | 8500  | 0.0           | -               |
| 30.7554 | 8550  | 0.0           | -               |
| 30.9353 | 8600  | 0.0           | -               |
| 31.1151 | 8650  | 0.0           | -               |
| 31.2950 | 8700  | 0.0           | -               |
| 31.4748 | 8750  | 0.0           | -               |
| 31.6547 | 8800  | 0.0           | -               |
| 31.8345 | 8850  | 0.0           | -               |
| 32.0144 | 8900  | 0.0           | -               |
| 32.1942 | 8950  | 0.0           | -               |
| 32.3741 | 9000  | 0.0           | -               |
| 32.5540 | 9050  | 0.0           | -               |
| 32.7338 | 9100  | 0.0           | -               |
| 32.9137 | 9150  | 0.0           | -               |
| 33.0935 | 9200  | 0.0           | -               |
| 33.2734 | 9250  | 0.0           | -               |
| 33.4532 | 9300  | 0.0           | -               |
| 33.6331 | 9350  | 0.0           | -               |
| 33.8129 | 9400  | 0.0           | -               |
| 33.9928 | 9450  | 0.0           | -               |
| 34.1727 | 9500  | 0.0           | -               |
| 34.3525 | 9550  | 0.0001        | -               |
| 34.5324 | 9600  | 0.0           | -               |
| 34.7122 | 9650  | 0.0           | -               |
| 34.8921 | 9700  | 0.0           | -               |
| 35.0719 | 9750  | 0.0           | -               |
| 35.2518 | 9800  | 0.0           | -               |
| 35.4317 | 9850  | 0.0001        | -               |
| 35.6115 | 9900  | 0.0           | -               |
| 35.7914 | 9950  | 0.0           | -               |
| 35.9712 | 10000 | 0.0           | -               |
| 36.1511 | 10050 | 0.0           | -               |
| 36.3309 | 10100 | 0.0           | -               |
| 36.5108 | 10150 | 0.0           | -               |
| 36.6906 | 10200 | 0.0           | -               |
| 36.8705 | 10250 | 0.0           | -               |
| 37.0504 | 10300 | 0.0           | -               |
| 37.2302 | 10350 | 0.0           | -               |
| 37.4101 | 10400 | 0.0           | -               |
| 37.5899 | 10450 | 0.0           | -               |
| 37.7698 | 10500 | 0.0           | -               |
| 37.9496 | 10550 | 0.0           | -               |
| 38.1295 | 10600 | 0.0           | -               |
| 38.3094 | 10650 | 0.0           | -               |
| 38.4892 | 10700 | 0.0           | -               |
| 38.6691 | 10750 | 0.0           | -               |
| 38.8489 | 10800 | 0.0           | -               |
| 39.0288 | 10850 | 0.0           | -               |
| 39.2086 | 10900 | 0.0           | -               |
| 39.3885 | 10950 | 0.0           | -               |
| 39.5683 | 11000 | 0.0           | -               |
| 39.7482 | 11050 | 0.0           | -               |
| 39.9281 | 11100 | 0.0           | -               |
| 40.1079 | 11150 | 0.0           | -               |
| 40.2878 | 11200 | 0.0           | -               |
| 40.4676 | 11250 | 0.0           | -               |
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| 40.8273 | 11350 | 0.0           | -               |
| 41.0072 | 11400 | 0.0           | -               |
| 41.1871 | 11450 | 0.0           | -               |
| 41.3669 | 11500 | 0.0           | -               |
| 41.5468 | 11550 | 0.0           | -               |
| 41.7266 | 11600 | 0.0           | -               |
| 41.9065 | 11650 | 0.0           | -               |
| 42.0863 | 11700 | 0.0           | -               |
| 42.2662 | 11750 | 0.0           | -               |
| 42.4460 | 11800 | 0.0           | -               |
| 42.6259 | 11850 | 0.0           | -               |
| 42.8058 | 11900 | 0.0           | -               |
| 42.9856 | 11950 | 0.0           | -               |
| 43.1655 | 12000 | 0.0           | -               |
| 43.3453 | 12050 | 0.0           | -               |
| 43.5252 | 12100 | 0.0           | -               |
| 43.7050 | 12150 | 0.0           | -               |
| 43.8849 | 12200 | 0.0           | -               |
| 44.0647 | 12250 | 0.0           | -               |
| 44.2446 | 12300 | 0.0           | -               |
| 44.4245 | 12350 | 0.0           | -               |
| 44.6043 | 12400 | 0.0           | -               |
| 44.7842 | 12450 | 0.0           | -               |
| 44.9640 | 12500 | 0.0           | -               |
| 45.1439 | 12550 | 0.0           | -               |
| 45.3237 | 12600 | 0.0           | -               |
| 45.5036 | 12650 | 0.0           | -               |
| 45.6835 | 12700 | 0.0           | -               |
| 45.8633 | 12750 | 0.0           | -               |
| 46.0432 | 12800 | 0.0           | -               |
| 46.2230 | 12850 | 0.0           | -               |
| 46.4029 | 12900 | 0.0           | -               |
| 46.5827 | 12950 | 0.0           | -               |
| 46.7626 | 13000 | 0.0           | -               |
| 46.9424 | 13050 | 0.0           | -               |
| 47.1223 | 13100 | 0.0           | -               |
| 47.3022 | 13150 | 0.0           | -               |
| 47.4820 | 13200 | 0.0           | -               |
| 47.6619 | 13250 | 0.0           | -               |
| 47.8417 | 13300 | 0.0           | -               |
| 48.0216 | 13350 | 0.0           | -               |
| 48.2014 | 13400 | 0.0           | -               |
| 48.3813 | 13450 | 0.0           | -               |
| 48.5612 | 13500 | 0.0           | -               |
| 48.7410 | 13550 | 0.0           | -               |
| 48.9209 | 13600 | 0.0           | -               |
| 49.1007 | 13650 | 0.0           | -               |
| 49.2806 | 13700 | 0.0           | -               |
| 49.4604 | 13750 | 0.0           | -               |
| 49.6403 | 13800 | 0.0           | -               |
| 49.8201 | 13850 | 0.0           | -               |
| 50.0    | 13900 | 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}
}
```

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