---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 고급누빔포인트방석커버 면방석 가구/인테리어>홈데코>쿠션/방석>방석커버
- text: PVC Terrazzo 방수 자체 접착 벽지 침실 벽 거실 장식 비닐 가구 접촉 용지 가구/인테리어>DIY자재/용품>벽지>띠벽지
- text: 클레마티스 오로라화병 가구/인테리어>인테리어소품>화병
- text: 나비장 고가구 클래식 공예 금박 은박 콘솔 수납장 가구/인테리어>수납가구>나비장
- text: 선반 받침대 테이블 책상 책장 프린터 철제 수납장 컴퓨터 정리 가구/인테리어>서재/사무용가구>책상>책상소품
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: klue/roberta-base
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: 1.0
      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:** 17 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                                                                                                                                                                                                                     |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'축사 난방 판넬 송아지 애완견 개집 전기 열선 가열 -미디엄 가구/인테리어>DIY자재/용품>바닥재>필름난방'</li><li>'냉장고 시트지 리폼 필름지 싱크대 에어컨 더블 도어 스티커 가구/인테리어>DIY자재/용품>시트지>단색시트지'</li><li>'이코리브 사각형 전선 정리 몰딩 쫄대 1M 1P 학원 가구/인테리어>DIY자재/용품>몰딩'</li></ul>            |
| 14.0  | <ul><li>'홈메이드 보드라운 비정형 러그 거실 카페트 매트 캐시미어 82번 LINE 가구/인테리어>카페트/러그>러그'</li><li>'테라스꾸미기 캠핑용매트 발판매트 월컴 가구/인테리어>카페트/러그>발매트'</li><li>'러그 발매트 화장실 현관 침실 주방 주방매트 대형 미끄럼방지 캠핑 PVC 가구/인테리어>카페트/러그>발매트'</li></ul>                       |
| 9.0   | <ul><li>'소화기 안내 아크릴 디자인 표지판 문패 가구/인테리어>인테리어소품>디자인문패'</li><li>'우체통 유럽 철재 전원주택 스탠드 우체통 인테리어 -1 2m 레터 박스 A 가구/인테리어>인테리어소품>우체통'</li><li>'빈티지 우체통 빨간 벽걸이 엔틱 카페 우편함 가구/인테리어>인테리어소품>우체통'</li></ul>                                  |
| 13.0  | <ul><li>'베스트슬립 M2 올가닉 메달리스트 매트리스 K 가구/인테리어>침실가구>매트리스>킹매트리스'</li><li>'화장대 의자 골드가구 등받이 인테리어 화장대 의자 가구/인테리어>침실가구>화장대>화장대의자'</li><li>'플레인홈 포렐 모던 거실 협탁 580 가구/인테리어>침실가구>협탁'</li></ul>                                            |
| 6.0   | <ul><li>'패딩 리본 키링 2컬러 가방꾸미기 가꾸 뜨개가방 부자재 가구/인테리어>수예>뜨개질>패키지'</li><li>'어린이집 신체활동 공룡도안 전신 안전거울 아기안전거울 아기 가구/인테리어>수예>퀼트/펠트>도안'</li><li>'5색재봉실 레인보우 컬러 바느질 퀼트 자수 원사 재봉 십자수 공예 가구/인테리어>수예>자수>실/바늘'</li></ul>                       |
| 5.0   | <ul><li>'다용도실 베란다 창고 정리 선반 세탁실 선반장 사무실 철제 키큰 가구/인테리어>수납가구>선반'</li><li>'도서 잡지 사무실 분류 보관함 회전 신문 홍보물 전단지 스탠딩 가구/인테리어>수납가구>잡지꽂이'</li><li>'프랑코홈 프랑코 리빙박스 62L 2개 SET 대용량 플라스틱 수납 정리함 FRANCO 가구/인테리어>수납가구>공간박스'</li></ul>           |
| 11.0  | <ul><li>'비비엔다 포르페 냉감 아이싱 맥스 아기 쿨패드 유아특대형 슈퍼싱글 퀸 킹 가구/인테리어>침구단품>패드>싱글/슈퍼싱글패드'</li><li>'매트리스커버높은 방수 토퍼 매트리스 커버 싱글 퀸 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글침대커버'</li><li>'모던하우스 리아 소프트워싱 차렵이불 Q 가구/인테리어>침구단품>차렵이불'</li></ul>              |
| 1.0   | <ul><li>'데코라인 본테 높은 600 피규어 진열장 DHP006 가구/인테리어>거실가구>장식장'</li><li>'현대의료기 모짜르트 4인 SF 카우치 컴포트 황토숯볼 흙소파 가구/인테리어>거실가구>소파>흙/돌소파'</li><li>'오브민 접이식 폴딩 이동식 노트북 책상800 소파 사이드테이블 가구/인테리어>거실가구>테이블>사이드테이블'</li></ul>                    |
| 4.0   | <ul><li>'쿠션 던지기 리넨 베개 커버 귀여운 눈송이 케이스 새해 장식 가구/인테리어>솜류>쿠션솜'</li><li>'바디필로우 대 롱쿠션 캔디솜 더크린 가구/인테리어>솜류>쿠션솜'</li><li>'힐튼 퀼팅베개 계절베개 숙면베개 필로우 29859062 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'</li></ul>                                      |
| 16.0  | <ul><li>'안고자는인형 대형 고양이 롱 바디필로우 수면 쿠션 모찌 찰떡 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석'</li><li>'냉감 애견패드 강아지 고양이 쿨매트 듀라론 2P 눕자 40X60 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석'</li><li>'3개 홀더 스마트폰 bob 거치대 썬바이저 차량용 자동차 가구/인테리어>홈데코>쿠션/방석>팔꿈치/손목쿠션'</li></ul> |
| 3.0   | <ul><li>'프린터거치대 프린터선반 다이 수납장 테이블 선반 -블랙 3단프레임 가구/인테리어>서재/사무용가구>책상>책상소품'</li><li>'카파맥스 플러스 3단 책꽂이 연두 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'포밍 테이블 1800 사무용 회의실 책상 다용도 작업대 가구/인테리어>서재/사무용가구>사무/교구용가구>회의테이블'</li></ul>                 |
| 8.0   | <ul><li>'수영장 썬베드 야외 의자 비치 해변 호텔 펜션 테라스 리조트 침대 가구/인테리어>아웃도어가구>야외의자'</li><li>'평벤치 학교 의자 공원 운동장 야외 휴게소 병원 벤 -1 8m U자형 가구/인테리어>아웃도어가구>야외벤치'</li><li>'야외조립창고 컨테이너 농막수납장 공구보관함 잡화 유형 A 가구/인테리어>아웃도어가구>기타아웃도어가구'</li></ul>          |
| 7.0   | <ul><li>'귀여운 로봇 낮은수납장 깊은 정리 이동식 3단 틈새 가구/인테리어>아동/주니어가구>수납장'</li><li>'엘레아 메리다 800 5단 서랍장 가구/인테리어>아동/주니어가구>서랍장'</li><li>'장바구니 일룸 에디키즈 장난감 정리대 가구/인테리어>아동/주니어가구>수납장'</li></ul>                                                  |
| 15.0  | <ul><li>'창안애 브러쉬 25mm 알루미늄 블라인드 30 x 30 가구/인테리어>커튼/블라인드>블라인드'</li><li>'광목로만쉐이드 작은 동물 커튼 욕실 3D 디지털 침실 구름로만쉐이드 가구/인테리어>커튼/블라인드>로만셰이드'</li><li>'에그박스 2단 슬라이딩 가구/인테리어>커튼/블라인드>로만셰이드'</li></ul>                                   |
| 12.0  | <ul><li>'마이하우스 알러지케어 스타패치 핑크 키즈 차렵이불 풀세트 S/SS 가구/인테리어>침구세트>매트커버세트>슈퍼싱글매트커버세트'</li><li>'올리비아데코 베리메리 60수 아사 요차렵세트 Q 가구/인테리어>침구세트>요이불세트>2/3인용'</li><li>'레노마홈 스마일워싱 차렵이불 베개세트 Q 사계절 가구/인테리어>침구세트>이불베개세트>슈퍼싱글이불베개세트'</li></ul>    |
| 2.0   | <ul><li>'슬립앤슬립 마스터유닛2 5분할 베개 소프트 LE1215376644 가구/인테리어>베개>메모리폼베개'</li><li>'일자목베개 쿨젤 목편한 숙면 기능성 쿨링 메모리폼 여름 베개 1개 가구/인테리어>베개>메모리폼베개'</li><li>'스패로우 스프링 필로우 메모리폼 베개 가구/인테리어>베개>메모리폼베개'</li></ul>                                 |
| 10.0  | <ul><li>'라자가구 위드 고메 1600 주방수납장 세트 홈카페형 nk008 가구/인테리어>주방가구>주방수납장'</li><li>'레트로하우스 코케 고무나무 원목 접이식 확장형 식탁 테이블 1600 가구/인테리어>주방가구>식탁/의자>식탁테이블'</li><li>'UNKNOWN 호환 리빙코리아 리빙웰 프리미엄 오븐 석쇠 OV250 가구/인테리어>주방가구>기타주방가구'</li></ul>      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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_fi")
# Run inference
preds = model("클레마티스 오로라화병 가구/인테리어>인테리어소품>화병")
```

<!--
### Downstream Use

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-->

<!--
### 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.*
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### 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   | 2   | 8.9412 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 980                   |
| 1.0   | 280                   |
| 2.0   | 133                   |
| 3.0   | 350                   |
| 4.0   | 349                   |
| 5.0   | 840                   |
| 6.0   | 490                   |
| 7.0   | 893                   |
| 8.0   | 420                   |
| 9.0   | 1516                  |
| 10.0  | 420                   |
| 11.0  | 890                   |
| 12.0  | 270                   |
| 13.0  | 629                   |
| 14.0  | 402                   |
| 15.0  | 700                   |
| 16.0  | 210                   |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0005  | 1     | 0.4481        | -               |
| 0.0262  | 50    | 0.4495        | -               |
| 0.0524  | 100   | 0.426         | -               |
| 0.0786  | 150   | 0.3955        | -               |
| 0.1048  | 200   | 0.3284        | -               |
| 0.1310  | 250   | 0.2654        | -               |
| 0.1572  | 300   | 0.209         | -               |
| 0.1833  | 350   | 0.1354        | -               |
| 0.2095  | 400   | 0.0875        | -               |
| 0.2357  | 450   | 0.0572        | -               |
| 0.2619  | 500   | 0.0427        | -               |
| 0.2881  | 550   | 0.0289        | -               |
| 0.3143  | 600   | 0.0222        | -               |
| 0.3405  | 650   | 0.0143        | -               |
| 0.3667  | 700   | 0.0101        | -               |
| 0.3929  | 750   | 0.0092        | -               |
| 0.4191  | 800   | 0.0068        | -               |
| 0.4453  | 850   | 0.0066        | -               |
| 0.4715  | 900   | 0.0046        | -               |
| 0.4976  | 950   | 0.0049        | -               |
| 0.5238  | 1000  | 0.0046        | -               |
| 0.5500  | 1050  | 0.0039        | -               |
| 0.5762  | 1100  | 0.0038        | -               |
| 0.6024  | 1150  | 0.0034        | -               |
| 0.6286  | 1200  | 0.0029        | -               |
| 0.6548  | 1250  | 0.0017        | -               |
| 0.6810  | 1300  | 0.0011        | -               |
| 0.7072  | 1350  | 0.0009        | -               |
| 0.7334  | 1400  | 0.0006        | -               |
| 0.7596  | 1450  | 0.0005        | -               |
| 0.7858  | 1500  | 0.0004        | -               |
| 0.8119  | 1550  | 0.0004        | -               |
| 0.8381  | 1600  | 0.0003        | -               |
| 0.8643  | 1650  | 0.0003        | -               |
| 0.8905  | 1700  | 0.0003        | -               |
| 0.9167  | 1750  | 0.0002        | -               |
| 0.9429  | 1800  | 0.0002        | -               |
| 0.9691  | 1850  | 0.0002        | -               |
| 0.9953  | 1900  | 0.0002        | -               |
| 1.0215  | 1950  | 0.0002        | -               |
| 1.0477  | 2000  | 0.0002        | -               |
| 1.0739  | 2050  | 0.0002        | -               |
| 1.1001  | 2100  | 0.0001        | -               |
| 1.1262  | 2150  | 0.0001        | -               |
| 1.1524  | 2200  | 0.0001        | -               |
| 1.1786  | 2250  | 0.0001        | -               |
| 1.2048  | 2300  | 0.0001        | -               |
| 1.2310  | 2350  | 0.0001        | -               |
| 1.2572  | 2400  | 0.0001        | -               |
| 1.2834  | 2450  | 0.0001        | -               |
| 1.3096  | 2500  | 0.0001        | -               |
| 1.3358  | 2550  | 0.0001        | -               |
| 1.3620  | 2600  | 0.0001        | -               |
| 1.3882  | 2650  | 0.0001        | -               |
| 1.4144  | 2700  | 0.0001        | -               |
| 1.4405  | 2750  | 0.0001        | -               |
| 1.4667  | 2800  | 0.0001        | -               |
| 1.4929  | 2850  | 0.0001        | -               |
| 1.5191  | 2900  | 0.0001        | -               |
| 1.5453  | 2950  | 0.0001        | -               |
| 1.5715  | 3000  | 0.0001        | -               |
| 1.5977  | 3050  | 0.0001        | -               |
| 1.6239  | 3100  | 0.0001        | -               |
| 1.6501  | 3150  | 0.0001        | -               |
| 1.6763  | 3200  | 0.0           | -               |
| 1.7025  | 3250  | 0.0001        | -               |
| 1.7287  | 3300  | 0.0           | -               |
| 1.7548  | 3350  | 0.0           | -               |
| 1.7810  | 3400  | 0.0           | -               |
| 1.8072  | 3450  | 0.0           | -               |
| 1.8334  | 3500  | 0.0           | -               |
| 1.8596  | 3550  | 0.0           | -               |
| 1.8858  | 3600  | 0.0           | -               |
| 1.9120  | 3650  | 0.0           | -               |
| 1.9382  | 3700  | 0.0           | -               |
| 1.9644  | 3750  | 0.0           | -               |
| 1.9906  | 3800  | 0.0           | -               |
| 2.0168  | 3850  | 0.0           | -               |
| 2.0430  | 3900  | 0.0           | -               |
| 2.0691  | 3950  | 0.0           | -               |
| 2.0953  | 4000  | 0.0           | -               |
| 2.1215  | 4050  | 0.0           | -               |
| 2.1477  | 4100  | 0.0           | -               |
| 2.1739  | 4150  | 0.0           | -               |
| 2.2001  | 4200  | 0.0           | -               |
| 2.2263  | 4250  | 0.0           | -               |
| 2.2525  | 4300  | 0.0           | -               |
| 2.2787  | 4350  | 0.0           | -               |
| 2.3049  | 4400  | 0.0           | -               |
| 2.3311  | 4450  | 0.0           | -               |
| 2.3573  | 4500  | 0.0           | -               |
| 2.3834  | 4550  | 0.0           | -               |
| 2.4096  | 4600  | 0.0           | -               |
| 2.4358  | 4650  | 0.0           | -               |
| 2.4620  | 4700  | 0.0           | -               |
| 2.4882  | 4750  | 0.0           | -               |
| 2.5144  | 4800  | 0.0           | -               |
| 2.5406  | 4850  | 0.0           | -               |
| 2.5668  | 4900  | 0.0           | -               |
| 2.5930  | 4950  | 0.0           | -               |
| 2.6192  | 5000  | 0.0           | -               |
| 2.6454  | 5050  | 0.0           | -               |
| 2.6716  | 5100  | 0.0           | -               |
| 2.6977  | 5150  | 0.0           | -               |
| 2.7239  | 5200  | 0.0           | -               |
| 2.7501  | 5250  | 0.0           | -               |
| 2.7763  | 5300  | 0.0           | -               |
| 2.8025  | 5350  | 0.0           | -               |
| 2.8287  | 5400  | 0.0           | -               |
| 2.8549  | 5450  | 0.0           | -               |
| 2.8811  | 5500  | 0.0           | -               |
| 2.9073  | 5550  | 0.0           | -               |
| 2.9335  | 5600  | 0.0           | -               |
| 2.9597  | 5650  | 0.0           | -               |
| 2.9859  | 5700  | 0.0           | -               |
| 3.0120  | 5750  | 0.0           | -               |
| 3.0382  | 5800  | 0.0           | -               |
| 3.0644  | 5850  | 0.0           | -               |
| 3.0906  | 5900  | 0.0           | -               |
| 3.1168  | 5950  | 0.0           | -               |
| 3.1430  | 6000  | 0.0           | -               |
| 3.1692  | 6050  | 0.0           | -               |
| 3.1954  | 6100  | 0.0           | -               |
| 3.2216  | 6150  | 0.0           | -               |
| 3.2478  | 6200  | 0.0           | -               |
| 3.2740  | 6250  | 0.0           | -               |
| 3.3002  | 6300  | 0.0           | -               |
| 3.3263  | 6350  | 0.0           | -               |
| 3.3525  | 6400  | 0.0           | -               |
| 3.3787  | 6450  | 0.0           | -               |
| 3.4049  | 6500  | 0.0           | -               |
| 3.4311  | 6550  | 0.0           | -               |
| 3.4573  | 6600  | 0.0           | -               |
| 3.4835  | 6650  | 0.0           | -               |
| 3.5097  | 6700  | 0.0           | -               |
| 3.5359  | 6750  | 0.0           | -               |
| 3.5621  | 6800  | 0.0           | -               |
| 3.5883  | 6850  | 0.0           | -               |
| 3.6145  | 6900  | 0.0           | -               |
| 3.6406  | 6950  | 0.0           | -               |
| 3.6668  | 7000  | 0.0           | -               |
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| 3.7192  | 7100  | 0.0           | -               |
| 3.7454  | 7150  | 0.0           | -               |
| 3.7716  | 7200  | 0.0           | -               |
| 3.7978  | 7250  | 0.0           | -               |
| 3.8240  | 7300  | 0.0           | -               |
| 3.8502  | 7350  | 0.0           | -               |
| 3.8764  | 7400  | 0.0           | -               |
| 3.9026  | 7450  | 0.0           | -               |
| 3.9288  | 7500  | 0.0           | -               |
| 3.9550  | 7550  | 0.0           | -               |
| 3.9811  | 7600  | 0.0           | -               |
| 4.0073  | 7650  | 0.0           | -               |
| 4.0335  | 7700  | 0.0           | -               |
| 4.0597  | 7750  | 0.0           | -               |
| 4.0859  | 7800  | 0.0           | -               |
| 4.1121  | 7850  | 0.0           | -               |
| 4.1383  | 7900  | 0.0           | -               |
| 4.1645  | 7950  | 0.0           | -               |
| 4.1907  | 8000  | 0.0           | -               |
| 4.2169  | 8050  | 0.0           | -               |
| 4.2431  | 8100  | 0.0           | -               |
| 4.2693  | 8150  | 0.0           | -               |
| 4.2954  | 8200  | 0.0           | -               |
| 4.3216  | 8250  | 0.0           | -               |
| 4.3478  | 8300  | 0.0           | -               |
| 4.3740  | 8350  | 0.0           | -               |
| 4.4002  | 8400  | 0.0           | -               |
| 4.4264  | 8450  | 0.0           | -               |
| 4.4526  | 8500  | 0.0           | -               |
| 4.4788  | 8550  | 0.0           | -               |
| 4.5050  | 8600  | 0.0           | -               |
| 4.5312  | 8650  | 0.0           | -               |
| 4.5574  | 8700  | 0.0           | -               |
| 4.5836  | 8750  | 0.0006        | -               |
| 4.6097  | 8800  | 0.0006        | -               |
| 4.6359  | 8850  | 0.0002        | -               |
| 4.6621  | 8900  | 0.0001        | -               |
| 4.6883  | 8950  | 0.0           | -               |
| 4.7145  | 9000  | 0.0           | -               |
| 4.7407  | 9050  | 0.0           | -               |
| 4.7669  | 9100  | 0.0001        | -               |
| 4.7931  | 9150  | 0.0           | -               |
| 4.8193  | 9200  | 0.0           | -               |
| 4.8455  | 9250  | 0.0           | -               |
| 4.8717  | 9300  | 0.0           | -               |
| 4.8979  | 9350  | 0.0           | -               |
| 4.9240  | 9400  | 0.0           | -               |
| 4.9502  | 9450  | 0.0           | -               |
| 4.9764  | 9500  | 0.0           | -               |
| 5.0026  | 9550  | 0.0           | -               |
| 5.0288  | 9600  | 0.0           | -               |
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| 5.1598  | 9850  | 0.0           | -               |
| 5.1860  | 9900  | 0.0           | -               |
| 5.2122  | 9950  | 0.0           | -               |
| 5.2383  | 10000 | 0.0           | -               |
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| 5.4217  | 10350 | 0.0           | -               |
| 5.4479  | 10400 | 0.0           | -               |
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| 5.5526  | 10600 | 0.0           | -               |
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| 5.6574  | 10800 | 0.0           | -               |
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| 7.6480  | 14600 | 0.0           | -               |
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| 8.5909  | 16400 | 0.0           | -               |
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| 10.0576 | 19200 | 0.0009        | -               |
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### 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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