Push model using huggingface_hub.

11fe5b7 verified 12 days ago

27.6 kB

	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 프리미엄 수제 명절전 제사전 모듬전 700g 동그랑땡+동태전+깻잎전+꼬지+표고+호박전 동태전 300g (#M)식품>냉동/간편조리식품>튀김류
	T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 튀김류
	- text: (10+1) 다즐샵 식단 도시락 15종 골라담기 11_다섯가지나물밥+참스테이크 (#M)식품>냉동/간편조리식품>도시락 T200 > Naverstore
	> 식품 > 간편조리식품 > 도시락/밥류 > 도시락
	- text: 국내산 생 닭가슴살 / 닭안심살 5kg 10kg (1kg 500g 포장) 냉장 냉동 (냉동) 닭가슴살_(냉동) 닭가슴살 1kg x 5팩
	(#M)식품>축산물>닭고기>닭가슴살 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 닭가슴살
	- text: 퀴진 에어프라이어 크리스피 양념감자튀김 800g 2봉 1. 퀴진 에어 크리스피 양념감자 800g 1. 퀴진 에어 크리스피 양념감자 800g
	1. 퀴진 에어 크리스피 양념감자 800g_1. 퀴진 에어 크리스피 양념감자 800g (#M)식품>냉동/간편조리식품>튀김류 T200 > Naverstore
	> 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 튀김류
	- text: 1년 내내 질리지 않는 프리미엄 냉동도시락 / 넉넉소반 향긋한 깻잎닭갈비 흑미밥 1팩 5. [넉넉소반] 매콤한 제육볶음 현미밥 (#M)식품>냉동/간편조리식품>도시락
	T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 도시락
	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: 0.9987360594795539
	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: 16 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 7.0 \| <ul><li>'오뚜기 프레스코 양송이크림스프 180g 04. 프레스코 베이컨감자스프 180g (#M)식품>냉동/간편조리식품>스프 T200 > Naverstore > 식품 > 간편조리식품 > 죽/스프 > 스프'</li><li>'[⭐11/16 The보장데이 / 리뷰포인트 500원(포토&영상)⭐] 폰타나 컵수프 8개 세트 / 분말 60g(20gx3봉) 스프 4. 머쉬룸 4개+어니언 4개_폰타나 머그컵 1P (#M)식품>냉동/간편조리식품>스프 T200 > Naverstore > 식품 > 간편조리식품 > 죽/스프 > 스프'</li><li>'매일유업 상하키친 크림스프 파스타 2박스 (총6개입) 콘크림스프 6개입 (#M)식품>냉동/간편조리식품>스프 T200 > Naverstore > 식품 > 간편조리식품 > 죽/스프 > 스프'</li></ul> \|
	\| 15.0 \| <ul><li>'대신 치즈브레드 냉동 햄버거 100g pc방 매점 편의점 피씨방 업소용 간식 11.대신 불갈비벅 135g (#M)식품>냉동/간편조리식품>햄버거 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 햄버거'</li><li>'한맥 마시따 숯불그릴벅 150g x 10개 / 냉동버거 / 햄버거 한맥 마시따 숯불그릴벅 150g x 10개 (#M)식품>냉동/간편조리식품>햄버거 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 햄버거'</li><li>'대신 내가맛스타 120g 12.대신 치즈벅 140g (#M)식품>냉동/간편조리식품>햄버거 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 햄버거'</li></ul> \|
	\| 11.0 \| <ul><li>'퀴진 에어프라이어 크리스피 양념감자튀김 800g 1봉+1봉 외 3종 1. 퀴진 에어 크리스피 양념감자 800g_3. 퀴진 해쉬브라운 스틱 600g (#M)식품>냉동/간편조리식품>튀김류 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 튀김류'</li><li>'[푸디버디] 숫자치킨너겟500g/미니까스 2종 480g 1+1 숫자치킨너겟 1게+미니돈까스 1개 숫자치킨너겟 1개+미니치킨까스 1개 (#M)식품>냉동/간편조리식품>튀김류 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 튀김류'</li><li>'사조오양 팝콘치킨 1kg (#M)식품>냉동/간편조리식품>튀김류 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 튀김류'</li></ul> \|
	\| 14.0 \| <ul><li>'애슐리 크리스피 포테이토 핫도그 (8개입) 크리스피 소시지탱글 핫도그 (10개입) (#M)식품>냉동/간편조리식품>핫도그 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 핫도그'</li><li>'고메 치즈크리스피핫도그 340g (#M)식품>냉동/간편조리식품>기타냉동/간편조리식품 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 핫도그'</li><li>'올반 크런치 찰핫도그 80g 10봉 모짜렐라 체다치즈 인기 아이간식 찰핫도그 10봉+크런치 10봉 (#M)식품>냉동/간편조리식품>핫도그 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 핫도그'</li></ul> \|
	\| 13.0 \| <ul><li>'전원푸드락 미트볼 1kg (#M)식품>냉동/간편조리식품>함박/미트볼 T200 > Naverstore > 식품 > 간편조리식품 > 함박/미트볼'</li><li>'오뚜기 한입쏙쏙미트볼(렌지) 180G 입맛돋는매운갈비찜(렌지) 180G (#M)식품>냉동/간편조리식품>함박/미트볼 T200 > Naverstore > 식품 > 간편조리식품 > 함박/미트볼'</li><li>'쉐프의밥상 육즙가득 미트볼 2kg+두툼 함박스테이크 1kg 국내산 원료육 엄마밥상 (#M)식품>냉동/간편조리식품>함박/미트볼 T200 > Naverstore > 식품 > 간편조리식품 > 함박/미트볼'</li></ul> \|
	\| 8.0 \| <ul><li>'부산어묵장돌이 순살볼어묵 1000g (#M)식품>냉동/간편조리식품>어묵 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 어묵'</li><li>'부산어묵장돌이 야채맛사각어묵 900g (#M)식품>냉동/간편조리식품>어묵 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 어묵'</li><li>'가덕도 부산 종합어묵 3kg 업소용 종합오뎅 대용량 (#M)식품>냉동/간편조리식품>어묵 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 어묵'</li></ul> \|
	\| 12.0 \| <ul><li>'애슐리 크런치즈엣지 트리플 치즈 피자 크런치즈엣지 올더미트 피자 1판 (#M)식품>냉동/간편조리식품>피자 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 피자'</li><li>'노엣지피자 베이컨 파이브치즈 4개 (#M)식품>냉동/간편조리식품>피자 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 피자'</li><li>'G풀무원 피자 4종 5판 골라담기 (노엣지/시카고/크로엣지/골드크러스트) 20.골드크러스트 파이브치즈X5 (#M)식품>냉동/간편조리식품>기타냉동/간편조리식품 T200 > Naverstore > 식품 > 간편조리식품 > 피자/핫도그/햄버거 > 피자'</li></ul> \|
	\| 4.0 \| <ul><li>'추억의 국민학교 떡볶이 5팩 /오리지널맛/쫄볶이 02.국떡 쫄볶이 5팩 (#M)식품>냉동/간편조리식품>떡볶이 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 떡볶이'</li><li>'옥이네 떡볶이 쫄볶이 밀떡 밀키트 2팩+2팩 쫄볶이 매운맛 2팩_떡볶이 매니아맛 2팩 (#M)식품>냉동/간편조리식품>떡볶이 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 떡볶이'</li><li>'당일생산 밀떡 1.5kg 2. 밀떡 1.5kg (중) (#M)식품>냉동/간편조리식품>떡볶이 T200 > Naverstore > 식품 > 간편조리식품 > 떡볶이/튀김/어묵 > 떡볶이'</li></ul> \|
	\| 10.0 \| <ul><li>'오뚜기 3분카레 매운맛 200g 오뚜기 3분카레 매운맛 200g (#M)식품>냉동/간편조리식품>카레/짜장 T200 > Naverstore > 식품 > 간편조리식품 > 카레/짜장'</li><li>'티아시아키친 즉석카레 스파이시 비프 마살라 커리 170g 게살 푸팟퐁 커리 170g (#M)식품>냉동/간편조리식품>카레/짜장 T200 > Naverstore > 식품 > 간편조리식품 > 카레/짜장'</li><li>'티아시아커리 치킨 마크니 커리 170g/SPCRB-01 선택03.티아시아 비프 커리170g-SPCRC (#M)식품>냉동/간편조리식품>카레/짜장 T200 > Naverstore > 식품 > 간편조리식품 > 카레/짜장'</li></ul> \|
	\| 2.0 \| <ul><li>'한끼통살 닭가슴살 10종 50개 골라담기 냉동 스팀 통닭가슴살 통살 데리야끼맛 (20개입)_통살 10종 혼합 세트 (30개입) (#M)식품>축산물>닭고기>닭가슴살 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 닭가슴살'</li><li>'랭커 닭가슴살 1팩 골라담기 소세지 스테이크 스팀 수비드 훈제 냉동 닭가슴살 스테이크 갈릭 1팩 (#M)식품>축산물>닭고기>닭가슴살 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 닭가슴살'</li><li>'아임닭 닭가슴살 75종 / 훈제 스팀 저염 소세지 스테이크 헬스 26_크리스피 닭가슴살 숯불갈비 (#M)식품>축산물>닭고기>닭가슴살 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 닭가슴살'</li></ul> \|
	\| 1.0 \| <ul><li>'바비바삭 현미 보리 귀리 포켓 누룽지 보리바비바삭 2봉 현미바비바삭 1봉 (#M)식품>냉동/간편조리식품>누룽지 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 누룽지'</li><li>'누룽씬 국내산 현미 가마솥 수제누룽지 400g 1봉 유기농 가마솥누룽지 200g 1봉 (#M)식품>냉동/간편조리식품>누룽지 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 누룽지'</li><li>'마운틴듀 355ml 1개 수제 누룽지_도시농부 수제 누룽지 1KG(25봉) (#M)식품>음료>청량/탄산음료>기타탄산음료 T200 > Naverstore > 식품 > 생수/음료 > 청량/탄산음료 > 기타탄산음료'</li></ul> \|
	\| 9.0 \| <ul><li>'쏘이마루 21종 콩불구이 채식 콩고기 비건푸드 9. 채식 햄버거패티 210g (#M)식품>냉동/간편조리식품>채식푸드 T200 > Naverstore > 식품 > 간편조리식품 > 채식푸드'</li><li>'쏘이마루 21종 콩불구이 채식 콩고기 비건푸드 8. 참좋은 너비안 300g (#M)식품>냉동/간편조리식품>채식푸드 T200 > Naverstore > 식품 > 간편조리식품 > 채식푸드'</li><li>'쏘이마루 21종 콩불구이 채식 콩고기 비건푸드 11. 쏘이 너비안볼 240g (#M)식품>냉동/간편조리식품>채식푸드 T200 > Naverstore > 식품 > 간편조리식품 > 채식푸드'</li></ul> \|
	\| 3.0 \| <ul><li>'식스밀 닭가슴살 다이어트 식단 체중조절식 1종 외 단백질 벌크업 식단 냉동 간편 직장인 스테이크 도시락 연어 계란볶음밥 (#M)식품>냉동/간편조리식품>도시락 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 도시락'</li><li>'[포켓샐러드] 라이스 도시락 미니컵밥 백김치멸치 간편식 외 22종 19.시즌3 계란곤약볶음밥&함박스테이크 (#M)식품>냉동/간편조리식품>도시락 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 도시락'</li><li>'저당도시락 장조림 버터 계란밥 외 13종 한끼 관리식단 1주 2주 한달 건강한 직장인 간편식 곤약밥/반찬 단백질 식단 03_저당 산채비빔밥 200g (#M)식품>냉동/간편조리식품>도시락 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 도시락'</li></ul> \|
	\| 0.0 \| <ul><li>'목우촌 쉐프9단 통등심 꿔바로우 1kg (#M)식품>냉동/간편조리식품>기타냉동/간편조리식품 T200 > Naverstore > 식품 > 간편조리식품 > 기타간편조리식품'</li><li>'비비고 냉동 고등어구이 60G 5개+5개 (총 10개) 외 8종 고등어 60Gx5개+가자미 70Gx5개 (#M)식품>냉동/간편조리식품>기타냉동/간편조리식품 T200 > Naverstore > 식품 > 간편조리식품 > 기타간편조리식품'</li><li>'냉동야채 1kg 다진 야채 믹스 볶음밥재료 02.혼합4종 (#M)식품>냉동/간편조리식품>기타냉동/간편조리식품 T200 > Naverstore > 식품 > 간편조리식품 > 기타간편조리식품'</li></ul> \|
	\| 5.0 \| <ul><li>'한성 이게조아F 540g 02. 크래미F2 1kg (#M)식품>냉동/간편조리식품>맛살/게살 T200 > Naverstore > 식품 > 간편조리식품 > 맛살/게살'</li><li>'오양/실속/맛살/kg/사조/식품/냉동/조리/게살 본품 (#M)식품>냉동/간편조리식품>맛살/게살 T200 > Naverstore > 식품 > 간편조리식품 > 맛살/게살'</li><li>'오양 실속 맛살 1kg 사조 식품 냉동 조리 게살 본품 (#M)식품>냉동/간편조리식품>맛살/게살 T200 > Naverstore > 식품 > 간편조리식품 > 맛살/게살'</li></ul> \|
	\| 6.0 \| <ul><li>'싱싱한 닭가슴살 야채 샐러드 외 32종 100g 야채믹스 도시락 식단 배달 29_리코타치즈 샐러드 180g (#M)식품>냉동/간편조리식품>샐러드 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 샐러드'</li><li>'[샐러드 정기배송] 1주/2주 5종을 배송 1일 2식 새벽배송 식단 1일1식(새벽배송)_1주_화요일 (#M)식품>냉동/간편조리식품>샐러드 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 샐러드'</li><li>'샐러드 재료 다이어트 야채 유러피안 배달 배송 1kg 모듬쌈 1kg (#M)식품>냉동/간편조리식품>샐러드 T200 > Naverstore > 식품 > 간편조리식품 > 샐러드/닭가슴살 > 샐러드'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9987 \|

	## 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_cate_top_fd0")
	# Run inference
	preds = model("(10+1) 다즐샵 식단 도시락 15종 골라담기 11_다섯가지나물밥+참스테이크 (#M)식품>냉동/간편조리식품>도시락 T200 > Naverstore > 식품 > 간편조리식품 > 도시락/밥류 > 도시락")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 12 \| 21.1790 \| 41 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 50 \|
	\| 7.0 \| 50 \|
	\| 8.0 \| 50 \|
	\| 9.0 \| 32 \|
	\| 10.0 \| 50 \|
	\| 11.0 \| 50 \|
	\| 12.0 \| 50 \|
	\| 13.0 \| 50 \|
	\| 14.0 \| 50 \|
	\| 15.0 \| 50 \|

	### Training Hyperparameters
	- batch_size: (128, 128)
	- num_epochs: (20, 20)
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0033 \| 1 \| 0.4947 \| - \|
	\| 0.1634 \| 50 \| 0.4776 \| - \|
	\| 0.3268 \| 100 \| 0.286 \| - \|
	\| 0.4902 \| 150 \| 0.1239 \| - \|
	\| 0.6536 \| 200 \| 0.0278 \| - \|
	\| 0.8170 \| 250 \| 0.0062 \| - \|
	\| 0.9804 \| 300 \| 0.0015 \| - \|
	\| 1.1438 \| 350 \| 0.0008 \| - \|
	\| 1.3072 \| 400 \| 0.0004 \| - \|
	\| 1.4706 \| 450 \| 0.0002 \| - \|
	\| 1.6340 \| 500 \| 0.0002 \| - \|
	\| 1.7974 \| 550 \| 0.0002 \| - \|
	\| 1.9608 \| 600 \| 0.0001 \| - \|
	\| 2.1242 \| 650 \| 0.0001 \| - \|
	\| 2.2876 \| 700 \| 0.0001 \| - \|
	\| 2.4510 \| 750 \| 0.0001 \| - \|
	\| 2.6144 \| 800 \| 0.0001 \| - \|
	\| 2.7778 \| 850 \| 0.0001 \| - \|
	\| 2.9412 \| 900 \| 0.0001 \| - \|
	\| 3.1046 \| 950 \| 0.0 \| - \|
	\| 3.2680 \| 1000 \| 0.0 \| - \|
	\| 3.4314 \| 1050 \| 0.0 \| - \|
	\| 3.5948 \| 1100 \| 0.0 \| - \|
	\| 3.7582 \| 1150 \| 0.0 \| - \|
	\| 3.9216 \| 1200 \| 0.0 \| - \|
	\| 4.0850 \| 1250 \| 0.0 \| - \|
	\| 4.2484 \| 1300 \| 0.0 \| - \|
	\| 4.4118 \| 1350 \| 0.0 \| - \|
	\| 4.5752 \| 1400 \| 0.0 \| - \|
	\| 4.7386 \| 1450 \| 0.0 \| - \|
	\| 4.9020 \| 1500 \| 0.0 \| - \|
	\| 5.0654 \| 1550 \| 0.0 \| - \|
	\| 5.2288 \| 1600 \| 0.0 \| - \|
	\| 5.3922 \| 1650 \| 0.0 \| - \|
	\| 5.5556 \| 1700 \| 0.0 \| - \|
	\| 5.7190 \| 1750 \| 0.0 \| - \|
	\| 5.8824 \| 1800 \| 0.0 \| - \|
	\| 6.0458 \| 1850 \| 0.0 \| - \|
	\| 6.2092 \| 1900 \| 0.0 \| - \|
	\| 6.3725 \| 1950 \| 0.0 \| - \|
	\| 6.5359 \| 2000 \| 0.0 \| - \|
	\| 6.6993 \| 2050 \| 0.0 \| - \|
	\| 6.8627 \| 2100 \| 0.0 \| - \|
	\| 7.0261 \| 2150 \| 0.0 \| - \|
	\| 7.1895 \| 2200 \| 0.0 \| - \|
	\| 7.3529 \| 2250 \| 0.0 \| - \|
	\| 7.5163 \| 2300 \| 0.0 \| - \|
	\| 7.6797 \| 2350 \| 0.0 \| - \|
	\| 7.8431 \| 2400 \| 0.0 \| - \|
	\| 8.0065 \| 2450 \| 0.0 \| - \|
	\| 8.1699 \| 2500 \| 0.0 \| - \|
	\| 8.3333 \| 2550 \| 0.0 \| - \|
	\| 8.4967 \| 2600 \| 0.0 \| - \|
	\| 8.6601 \| 2650 \| 0.0 \| - \|
	\| 8.8235 \| 2700 \| 0.0 \| - \|
	\| 8.9869 \| 2750 \| 0.0 \| - \|
	\| 9.1503 \| 2800 \| 0.0 \| - \|
	\| 9.3137 \| 2850 \| 0.0 \| - \|
	\| 9.4771 \| 2900 \| 0.0 \| - \|
	\| 9.6405 \| 2950 \| 0.0 \| - \|
	\| 9.8039 \| 3000 \| 0.0 \| - \|
	\| 9.9673 \| 3050 \| 0.0 \| - \|
	\| 10.1307 \| 3100 \| 0.0 \| - \|
	\| 10.2941 \| 3150 \| 0.0 \| - \|
	\| 10.4575 \| 3200 \| 0.0 \| - \|
	\| 10.6209 \| 3250 \| 0.0 \| - \|
	\| 10.7843 \| 3300 \| 0.0 \| - \|
	\| 10.9477 \| 3350 \| 0.0 \| - \|
	\| 11.1111 \| 3400 \| 0.0 \| - \|
	\| 11.2745 \| 3450 \| 0.0 \| - \|
	\| 11.4379 \| 3500 \| 0.0 \| - \|
	\| 11.6013 \| 3550 \| 0.0 \| - \|
	\| 11.7647 \| 3600 \| 0.0 \| - \|
	\| 11.9281 \| 3650 \| 0.0 \| - \|
	\| 12.0915 \| 3700 \| 0.0 \| - \|
	\| 12.2549 \| 3750 \| 0.0 \| - \|
	\| 12.4183 \| 3800 \| 0.0 \| - \|
	\| 12.5817 \| 3850 \| 0.0 \| - \|
	\| 12.7451 \| 3900 \| 0.0 \| - \|
	\| 12.9085 \| 3950 \| 0.0 \| - \|
	\| 13.0719 \| 4000 \| 0.0 \| - \|
	\| 13.2353 \| 4050 \| 0.0 \| - \|
	\| 13.3987 \| 4100 \| 0.0 \| - \|
	\| 13.5621 \| 4150 \| 0.0 \| - \|
	\| 13.7255 \| 4200 \| 0.0 \| - \|
	\| 13.8889 \| 4250 \| 0.0 \| - \|
	\| 14.0523 \| 4300 \| 0.0 \| - \|
	\| 14.2157 \| 4350 \| 0.0 \| - \|
	\| 14.3791 \| 4400 \| 0.0 \| - \|
	\| 14.5425 \| 4450 \| 0.0001 \| - \|
	\| 14.7059 \| 4500 \| 0.0001 \| - \|
	\| 14.8693 \| 4550 \| 0.0 \| - \|
	\| 15.0327 \| 4600 \| 0.0 \| - \|
	\| 15.1961 \| 4650 \| 0.0 \| - \|
	\| 15.3595 \| 4700 \| 0.0 \| - \|
	\| 15.5229 \| 4750 \| 0.0 \| - \|
	\| 15.6863 \| 4800 \| 0.0001 \| - \|
	\| 15.8497 \| 4850 \| 0.0 \| - \|
	\| 16.0131 \| 4900 \| 0.0 \| - \|
	\| 16.1765 \| 4950 \| 0.0 \| - \|
	\| 16.3399 \| 5000 \| 0.0 \| - \|
	\| 16.5033 \| 5050 \| 0.0 \| - \|
	\| 16.6667 \| 5100 \| 0.0 \| - \|
	\| 16.8301 \| 5150 \| 0.0 \| - \|
	\| 16.9935 \| 5200 \| 0.0 \| - \|
	\| 17.1569 \| 5250 \| 0.0 \| - \|
	\| 17.3203 \| 5300 \| 0.0 \| - \|
	\| 17.4837 \| 5350 \| 0.0 \| - \|
	\| 17.6471 \| 5400 \| 0.0 \| - \|
	\| 17.8105 \| 5450 \| 0.0 \| - \|
	\| 17.9739 \| 5500 \| 0.0 \| - \|
	\| 18.1373 \| 5550 \| 0.0 \| - \|
	\| 18.3007 \| 5600 \| 0.0 \| - \|
	\| 18.4641 \| 5650 \| 0.0 \| - \|
	\| 18.6275 \| 5700 \| 0.0 \| - \|
	\| 18.7908 \| 5750 \| 0.0 \| - \|
	\| 18.9542 \| 5800 \| 0.0 \| - \|
	\| 19.1176 \| 5850 \| 0.0 \| - \|
	\| 19.2810 \| 5900 \| 0.0 \| - \|
	\| 19.4444 \| 5950 \| 0.0 \| - \|
	\| 19.6078 \| 6000 \| 0.0 \| - \|
	\| 19.7712 \| 6050 \| 0.0 \| - \|
	\| 19.9346 \| 6100 \| 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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