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mini1013
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master_item_ac

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +652 -0
  3. config.json +29 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,652 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: klue/roberta-base
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+ library_name: setfit
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+ metrics:
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+ - metric
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: '[자체제작] 14k 콩사다리 체인 반지 핑크_D style(1푼 굵기)_10호 (주)제이디아이인터내셔널'
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+ - text: 실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150
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+ - text: 체크 남자 베레모 아빠 모자 헌팅캡 패션 빵모자 외출 베이지체크 (4JS) 포제이스
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+ - text: TIMBERLAND 남성 앨번 6인치 워터프루프 워커부츠_TB0A1OIZC641 070(250) 비츠컴퍼니
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+ - text: 라인댄스화 헬스화 스포츠 여성 재즈화 댄스화 볼룸 모던 미드힐 37_블랙 스트레이트 3.5cm/굽(메쉬) 사랑옵다
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+ inference: true
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+ model-index:
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+ - name: SetFit with klue/roberta-base
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: metric
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+ value: 0.9385943021823656
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+ name: Metric
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+ ---
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+
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+ # SetFit with klue/roberta-base
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 17 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 2.0 | <ul><li>'남녀공용 멀티스카프 목토시 반다나 헤어밴드 두건 블랙 비오는밤'</li><li>'후드 모자 귀달이 겨울 털모자 동물 목돌이 03.브라운 뿔샵'</li><li>'햇빛 뒷목가리개 메쉬 통풍 선가드 자외선차단썬캡가드 그늘모자 쿨메쉬모자_그레이 에스더블유컴퍼니'</li></ul> |
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+ | 9.0 | <ul><li>'[LAP](강남점)아델라 핸들 미니 크로스백 (AP7AB208) 제트블랙(ZB)_FREE 신세계백화점'</li><li>'파스텔슬링백 힙색 미니 크로스 숄더백 그린 김후철'</li><li>'[메트로시티]봉봉백 클러치백 미듐 M233MQ3852Z 에이케이에스앤디 (주) AK인터넷쇼핑몰'</li></ul> |
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+ | 15.0 | <ul><li>'크리스마스 뱃지 브로치 배지 19종 세트 및 낱개 봉제 사슴 5 구매대행 이음'</li><li>'오드스튜디오 ODDSTUDIO 베이직 니트 체크 머플러 - 21COLOR 블랙 CS스페이스'</li><li>'넥케이프 넥커프스 페이크카라 레이어드카라 셔츠카라 1-카라-화이트 행복나라'</li></ul> |
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+ | 13.0 | <ul><li>'펄 쥬얼리 보석함 여행용 포켓 미니 악세사리 보관함 케이스 C타입-베이비핑크 제일사'</li><li>'[갤러리아] [비앤비골드] 14K 촘촘볼 블루큐빅 도넛링 반지 SRS39135 14K 화이트골드_1호 한화갤러리아(주)'</li><li>'미니골드 김천점 14K 18K 트레버 커플링 남자 여자 금반지 RJUC4047 RJUC4048 베이직하고 심플한 디자인 여자_14K옐로우골드 미니골드 김천점'</li></ul> |
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+ | 1.0 | <ul><li>'[베어파우](신세계강남점)(BEARPAW) 남성 털 슬리퍼 MARY MENS 블랙 K814001ND-M BLACK (K814001ND)_280 주식회사 에스에스지닷컴'</li><li>'노스페이스 뮬 슬립온 브이모션 - NS93P53A 블랙_290 롯데백화점2관'</li><li>'사무실 남자 슬리퍼 가죽 남성 빅 사이즈 48 47 사무용 신입생코디실내화 blue_38 리마106'</li></ul> |
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+ | 7.0 | <ul><li>'부드러운 슈트리 신발주름방지 신발모양유지 신발지탱 225 245 mm 커피와 기저귀'</li><li>'[갓성비] 꿀조합 애니비츠 세트 캐릭터 신발 악세사리 포켓몬 스누피 커비편의점SET 애니팝'</li><li>'MSMAX Jazz Dance Shoes Split Sole Men Dancing Sneakers High Top Boots for Women Silver 10.5 M Silver_11 Narrow 디아트479'</li></ul> |
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+ | 11.0 | <ul><li>'캐리어 수트케이스 양면 개방형 기내용 바퀴가방 화이트_26인치 피스온트레이드'</li><li>'클래시 패스 커버여권 포트월렛 포트파우치 파우치 여행지갑 포트 케이스 (01 레모니) 주식회사유마켓'</li><li>'클래시패스커버 (안티스키밍 여권케이스) (10블랙) JTEC'</li></ul> |
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+ | 4.0 | <ul><li>'고급 안경집 선글라스집 휴대용 케이스 파우치 하드 보관함 블랙 다온마켓'</li><li>'고급 올 칼라 크리스탈 다중 비즈 안경 줄 마스크 걸이 상품선택_블랙(골드) 리미몰'</li><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 마니또야'</li></ul> |
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+ | 14.0 | <ul><li>'[갤러리아] [Prada]프라다 23FW 사피아노 반지갑 블랙 2MO004 QME F0002 2MO004 QME F0002 FREE 한화갤러리아(주)'</li><li>'닥스 액세서리 [OSCAR][오스카][제네시스 전용] 네이비 프리미엄 토고 수입 가죽 차키케이스 DBHO2F573N2 XXX 주식회사 LF'</li><li>'톰브라운 23SS 남성 페블그레인 머니클립 블랙 MAW025L 00198 001 ONE SIZE 주식회사 이지겟인터내셔널'</li></ul> |
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+ | 0.0 | <ul><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉] [GRIDⅡ] 브라운 패턴배색 소가죽 클러치백 DBBA2F266W3 롯데백화점_'</li><li>'만다리나덕 토트백 PIETRO P4T05163 은하수몰'</li><li>'내셔널지오그래픽 N245ATO510 베이직 에코백 BLACK TNSC'</li></ul> |
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+ | 16.0 | <ul><li>'올림머리 메탈프레임 반머리 꼬임 집게핀 114 유광스틸 7cm 이지 아트 프로덕션 (EG ART PRODUCTION)'</li><li>'꼬임 메탈프레임 반머리 올림머리 집게핀 114 무광로즈 7cm 네오몰'</li><li>'폼폼 방울털 장식 미니 머리끈 포인트 헤어끈 퍼플 1P 은강'</li></ul> |
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+ | 8.0 | <ul><li>'기모 롱 오버 니삭스 겨울 스타킹 다리 워머 롱삭스 롱양말 무릎 니하이 브라운 린이팸'</li><li>'최대12켤레 남여 국산양말 장목/니트/균일가/신상/중목/발목/수면/학생 37~38_37.여)털실 중목_4켤레 / 버건디 투투삭스'</li><li>'NY코튼클럽 5켤레 국산 극세사 기모 롱 무압박 임산부 수면양말 W8001-여성-카멜5족 GSSHOP_'</li></ul> |
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+ | 5.0 | <ul><li>'[한국금거래소] 순금 카네이션 배지 1.875g 부모님 추석 명절 생신 생일 기념일 기념 축하 감사선물 주식회사 한국금거래소디지털에셋'</li><li>'[한국금거래소]한국금거래소 순금 용 37.5g [순금24K] 롯데아이몰'</li><li>'한국금거래소 실버바 1kg(1000g) 주식회사 한국금거래소디지털에셋'</li></ul> |
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+ | 10.0 | <ul><li>'캠퍼 브루투스 트렉 첼시 앵클부츠 346335 EU 39 주식회사 수비르글로벌커머스(SUBIR Global Commerce)'</li><li>'슈콤마보니 워커 부츠 DG3CW22519BLK 블랙_250 롯데쇼핑(주) 프리미엄아울렛 타임빌라스'</li><li>'말랑 쿠키 거실화 실내화 거실슬리퍼 실내슬리퍼 LWS 그레이265mm 생활공작소365'</li></ul> |
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+ | 6.0 | <ul><li>'BOXY 박시 워치와인더 BWS-S / BWS-F 1구 아답터1개로 쌓아서 사용가능 BWS-S(DG)아답터미포함 와치닷컴'</li><li>'지샥 GA-2100 2110 지얄오크 베젤 밴드 일체형 용두 메탈 우레탄밴드 커스텀 옵션5:실버+블랙베젤_1.일반버클_화이트 방울방울'</li><li>'스타샵 카시오 MRW-200H-2B2 남성 손목시계 c57 선택19. AW-49H-1B 스타샵'</li></ul> |
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+ | 3.0 | <ul><li>'남자 멜빵 2 5CM 남성 및 여성 서스펜더 클립 사이드 홀스터 스타일 탄성 백 ��스펜더 05 밝은 빨간색 헬로우스토어'</li><li>'멜빵 소형멜빵 용 멜빵 어린이멜빵 멜빵 맬빵 MinSellAmount 모루모루'</li><li>'[닥스 액세서리] [23FW] DBBE3F097BK 여성벨트DD Symbol 블랙 DD메탈릭 골드 버클 소 XXX '</li></ul> |
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+ | 12.0 | <ul><li>'미니 토시 사무용 광목 자수 팔토시 레드로즈 다솜이네'</li><li>'백화점 여성 남성 천연 양가죽 장갑 스마트폰 터치 털 손가락 겨울 방한 가죽 커플 장갑 2.여성용/스웨이드/차콜 힐렉스'</li><li>'[선물포장] 울 캐시미어혼방 핑거홀 장갑 JAGV2F310G2,JAGV2F311W2,JAGV2F312E2,JAGV2F313/질스튜어트 그린 롯데쇼핑(주)'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Metric |
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+ |:--------|:-------|
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+ | **all** | 0.9386 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_item_ac")
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+ # Run inference
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+ preds = model("실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 3 | 10.2537 | 30 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0.0 | 450 |
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+ | 1.0 | 650 |
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+ | 2.0 | 650 |
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+ | 3.0 | 150 |
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+ | 4.0 | 300 |
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+ | 5.0 | 120 |
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+ | 6.0 | 224 |
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+ | 7.0 | 350 |
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+ | 8.0 | 100 |
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+ | 9.0 | 467 |
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+ | 10.0 | 500 |
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+ | 11.0 | 600 |
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+ | 12.0 | 150 |
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+ | 13.0 | 450 |
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+ | 14.0 | 400 |
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+ | 15.0 | 1000 |
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+ | 16.0 | 250 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (512, 512)
164
+ - num_epochs: (20, 20)
165
+ - max_steps: -1
166
+ - sampling_strategy: oversampling
167
+ - num_iterations: 40
168
+ - body_learning_rate: (2e-05, 2e-05)
169
+ - head_learning_rate: 2e-05
170
+ - loss: CosineSimilarityLoss
171
+ - distance_metric: cosine_distance
172
+ - margin: 0.25
173
+ - end_to_end: False
174
+ - use_amp: False
175
+ - warmup_proportion: 0.1
176
+ - seed: 42
177
+ - eval_max_steps: -1
178
+ - load_best_model_at_end: False
179
+
180
+ ### Training Results
181
+ | Epoch | Step | Training Loss | Validation Loss |
182
+ |:-------:|:-----:|:-------------:|:---------------:|
183
+ | 0.0009 | 1 | 0.407 | - |
184
+ | 0.0469 | 50 | 0.3772 | - |
185
+ | 0.0939 | 100 | 0.3062 | - |
186
+ | 0.1408 | 150 | 0.2861 | - |
187
+ | 0.1878 | 200 | 0.2513 | - |
188
+ | 0.2347 | 250 | 0.2284 | - |
189
+ | 0.2817 | 300 | 0.1952 | - |
190
+ | 0.3286 | 350 | 0.149 | - |
191
+ | 0.3756 | 400 | 0.1154 | - |
192
+ | 0.4225 | 450 | 0.1042 | - |
193
+ | 0.4695 | 500 | 0.0802 | - |
194
+ | 0.5164 | 550 | 0.0765 | - |
195
+ | 0.5634 | 600 | 0.0767 | - |
196
+ | 0.6103 | 650 | 0.0475 | - |
197
+ | 0.6573 | 700 | 0.0535 | - |
198
+ | 0.7042 | 750 | 0.0293 | - |
199
+ | 0.7512 | 800 | 0.0388 | - |
200
+ | 0.7981 | 850 | 0.0156 | - |
201
+ | 0.8451 | 900 | 0.0348 | - |
202
+ | 0.8920 | 950 | 0.0241 | - |
203
+ | 0.9390 | 1000 | 0.023 | - |
204
+ | 0.9859 | 1050 | 0.0166 | - |
205
+ | 1.0329 | 1100 | 0.0124 | - |
206
+ | 1.0798 | 1150 | 0.0139 | - |
207
+ | 1.1268 | 1200 | 0.0122 | - |
208
+ | 1.1737 | 1250 | 0.0111 | - |
209
+ | 1.2207 | 1300 | 0.0062 | - |
210
+ | 1.2676 | 1350 | 0.0106 | - |
211
+ | 1.3146 | 1400 | 0.0112 | - |
212
+ | 1.3615 | 1450 | 0.0137 | - |
213
+ | 1.4085 | 1500 | 0.0154 | - |
214
+ | 1.4554 | 1550 | 0.0185 | - |
215
+ | 1.5023 | 1600 | 0.0248 | - |
216
+ | 1.5493 | 1650 | 0.0128 | - |
217
+ | 1.5962 | 1700 | 0.018 | - |
218
+ | 1.6432 | 1750 | 0.0013 | - |
219
+ | 1.6901 | 1800 | 0.0151 | - |
220
+ | 1.7371 | 1850 | 0.0208 | - |
221
+ | 1.7840 | 1900 | 0.0076 | - |
222
+ | 1.8310 | 1950 | 0.0138 | - |
223
+ | 1.8779 | 2000 | 0.0133 | - |
224
+ | 1.9249 | 2050 | 0.0131 | - |
225
+ | 1.9718 | 2100 | 0.0123 | - |
226
+ | 2.0188 | 2150 | 0.0165 | - |
227
+ | 2.0657 | 2200 | 0.0084 | - |
228
+ | 2.1127 | 2250 | 0.0062 | - |
229
+ | 2.1596 | 2300 | 0.0068 | - |
230
+ | 2.2066 | 2350 | 0.0023 | - |
231
+ | 2.2535 | 2400 | 0.006 | - |
232
+ | 2.3005 | 2450 | 0.0048 | - |
233
+ | 2.3474 | 2500 | 0.0016 | - |
234
+ | 2.3944 | 2550 | 0.0046 | - |
235
+ | 2.4413 | 2600 | 0.001 | - |
236
+ | 2.4883 | 2650 | 0.0022 | - |
237
+ | 2.5352 | 2700 | 0.0014 | - |
238
+ | 2.5822 | 2750 | 0.0004 | - |
239
+ | 2.6291 | 2800 | 0.0002 | - |
240
+ | 2.6761 | 2850 | 0.0004 | - |
241
+ | 2.7230 | 2900 | 0.0016 | - |
242
+ | 2.7700 | 2950 | 0.0018 | - |
243
+ | 2.8169 | 3000 | 0.0004 | - |
244
+ | 2.8638 | 3050 | 0.0001 | - |
245
+ | 2.9108 | 3100 | 0.0002 | - |
246
+ | 2.9577 | 3150 | 0.0018 | - |
247
+ | 3.0047 | 3200 | 0.0019 | - |
248
+ | 3.0516 | 3250 | 0.0001 | - |
249
+ | 3.0986 | 3300 | 0.0011 | - |
250
+ | 3.1455 | 3350 | 0.0001 | - |
251
+ | 3.1925 | 3400 | 0.0001 | - |
252
+ | 3.2394 | 3450 | 0.0002 | - |
253
+ | 3.2864 | 3500 | 0.0007 | - |
254
+ | 3.3333 | 3550 | 0.0001 | - |
255
+ | 3.3803 | 3600 | 0.0002 | - |
256
+ | 3.4272 | 3650 | 0.0001 | - |
257
+ | 3.4742 | 3700 | 0.0011 | - |
258
+ | 3.5211 | 3750 | 0.0013 | - |
259
+ | 3.5681 | 3800 | 0.0014 | - |
260
+ | 3.6150 | 3850 | 0.0001 | - |
261
+ | 3.6620 | 3900 | 0.0001 | - |
262
+ | 3.7089 | 3950 | 0.0002 | - |
263
+ | 3.7559 | 4000 | 0.0001 | - |
264
+ | 3.8028 | 4050 | 0.0014 | - |
265
+ | 3.8498 | 4100 | 0.0002 | - |
266
+ | 3.8967 | 4150 | 0.0001 | - |
267
+ | 3.9437 | 4200 | 0.0 | - |
268
+ | 3.9906 | 4250 | 0.0 | - |
269
+ | 4.0376 | 4300 | 0.0001 | - |
270
+ | 4.0845 | 4350 | 0.0002 | - |
271
+ | 4.1315 | 4400 | 0.0 | - |
272
+ | 4.1784 | 4450 | 0.0001 | - |
273
+ | 4.2254 | 4500 | 0.0 | - |
274
+ | 4.2723 | 4550 | 0.0 | - |
275
+ | 4.3192 | 4600 | 0.0003 | - |
276
+ | 4.3662 | 4650 | 0.0007 | - |
277
+ | 4.4131 | 4700 | 0.0 | - |
278
+ | 4.4601 | 4750 | 0.0001 | - |
279
+ | 4.5070 | 4800 | 0.0011 | - |
280
+ | 4.5540 | 4850 | 0.0003 | - |
281
+ | 4.6009 | 4900 | 0.0005 | - |
282
+ | 4.6479 | 4950 | 0.0001 | - |
283
+ | 4.6948 | 5000 | 0.0001 | - |
284
+ | 4.7418 | 5050 | 0.0001 | - |
285
+ | 4.7887 | 5100 | 0.0001 | - |
286
+ | 4.8357 | 5150 | 0.0 | - |
287
+ | 4.8826 | 5200 | 0.0 | - |
288
+ | 4.9296 | 5250 | 0.0 | - |
289
+ | 4.9765 | 5300 | 0.0001 | - |
290
+ | 5.0235 | 5350 | 0.0 | - |
291
+ | 5.0704 | 5400 | 0.0 | - |
292
+ | 5.1174 | 5450 | 0.0 | - |
293
+ | 5.1643 | 5500 | 0.0 | - |
294
+ | 5.2113 | 5550 | 0.0 | - |
295
+ | 5.2582 | 5600 | 0.0001 | - |
296
+ | 5.3052 | 5650 | 0.0 | - |
297
+ | 5.3521 | 5700 | 0.0 | - |
298
+ | 5.3991 | 5750 | 0.0 | - |
299
+ | 5.4460 | 5800 | 0.0 | - |
300
+ | 5.4930 | 5850 | 0.0 | - |
301
+ | 5.5399 | 5900 | 0.0 | - |
302
+ | 5.5869 | 5950 | 0.0 | - |
303
+ | 5.6338 | 6000 | 0.0 | - |
304
+ | 5.6808 | 6050 | 0.0 | - |
305
+ | 5.7277 | 6100 | 0.0 | - |
306
+ | 5.7746 | 6150 | 0.0 | - |
307
+ | 5.8216 | 6200 | 0.0 | - |
308
+ | 5.8685 | 6250 | 0.0 | - |
309
+ | 5.9155 | 6300 | 0.0001 | - |
310
+ | 5.9624 | 6350 | 0.0004 | - |
311
+ | 6.0094 | 6400 | 0.0007 | - |
312
+ | 6.0563 | 6450 | 0.0 | - |
313
+ | 6.1033 | 6500 | 0.0001 | - |
314
+ | 6.1502 | 6550 | 0.0 | - |
315
+ | 6.1972 | 6600 | 0.0001 | - |
316
+ | 6.2441 | 6650 | 0.0 | - |
317
+ | 6.2911 | 6700 | 0.0 | - |
318
+ | 6.3380 | 6750 | 0.0009 | - |
319
+ | 6.3850 | 6800 | 0.0 | - |
320
+ | 6.4319 | 6850 | 0.0001 | - |
321
+ | 6.4789 | 6900 | 0.0 | - |
322
+ | 6.5258 | 6950 | 0.0001 | - |
323
+ | 6.5728 | 7000 | 0.0 | - |
324
+ | 6.6197 | 7050 | 0.0 | - |
325
+ | 6.6667 | 7100 | 0.0 | - |
326
+ | 6.7136 | 7150 | 0.0 | - |
327
+ | 6.7606 | 7200 | 0.0001 | - |
328
+ | 6.8075 | 7250 | 0.0 | - |
329
+ | 6.8545 | 7300 | 0.0 | - |
330
+ | 6.9014 | 7350 | 0.0 | - |
331
+ | 6.9484 | 7400 | 0.0 | - |
332
+ | 6.9953 | 7450 | 0.0 | - |
333
+ | 7.0423 | 7500 | 0.0 | - |
334
+ | 7.0892 | 7550 | 0.0 | - |
335
+ | 7.1362 | 7600 | 0.0 | - |
336
+ | 7.1831 | 7650 | 0.0 | - |
337
+ | 7.2300 | 7700 | 0.0 | - |
338
+ | 7.2770 | 7750 | 0.0001 | - |
339
+ | 7.3239 | 7800 | 0.0 | - |
340
+ | 7.3709 | 7850 | 0.0 | - |
341
+ | 7.4178 | 7900 | 0.0 | - |
342
+ | 7.4648 | 7950 | 0.0 | - |
343
+ | 7.5117 | 8000 | 0.0 | - |
344
+ | 7.5587 | 8050 | 0.0 | - |
345
+ | 7.6056 | 8100 | 0.0 | - |
346
+ | 7.6526 | 8150 | 0.0024 | - |
347
+ | 7.6995 | 8200 | 0.0 | - |
348
+ | 7.7465 | 8250 | 0.0 | - |
349
+ | 7.7934 | 8300 | 0.0 | - |
350
+ | 7.8404 | 8350 | 0.0 | - |
351
+ | 7.8873 | 8400 | 0.0 | - |
352
+ | 7.9343 | 8450 | 0.0 | - |
353
+ | 7.9812 | 8500 | 0.0 | - |
354
+ | 8.0282 | 8550 | 0.0 | - |
355
+ | 8.0751 | 8600 | 0.0 | - |
356
+ | 8.1221 | 8650 | 0.0 | - |
357
+ | 8.1690 | 8700 | 0.0 | - |
358
+ | 8.2160 | 8750 | 0.0 | - |
359
+ | 8.2629 | 8800 | 0.0 | - |
360
+ | 8.3099 | 8850 | 0.0 | - |
361
+ | 8.3568 | 8900 | 0.0 | - |
362
+ | 8.4038 | 8950 | 0.0 | - |
363
+ | 8.4507 | 9000 | 0.0 | - |
364
+ | 8.4977 | 9050 | 0.0 | - |
365
+ | 8.5446 | 9100 | 0.0 | - |
366
+ | 8.5915 | 9150 | 0.0 | - |
367
+ | 8.6385 | 9200 | 0.0002 | - |
368
+ | 8.6854 | 9250 | 0.0003 | - |
369
+ | 8.7324 | 9300 | 0.0005 | - |
370
+ | 8.7793 | 9350 | 0.0001 | - |
371
+ | 8.8263 | 9400 | 0.0001 | - |
372
+ | 8.8732 | 9450 | 0.0001 | - |
373
+ | 8.9202 | 9500 | 0.0 | - |
374
+ | 8.9671 | 9550 | 0.0 | - |
375
+ | 9.0141 | 9600 | 0.0001 | - |
376
+ | 9.0610 | 9650 | 0.0001 | - |
377
+ | 9.1080 | 9700 | 0.0 | - |
378
+ | 9.1549 | 9750 | 0.0 | - |
379
+ | 9.2019 | 9800 | 0.0001 | - |
380
+ | 9.2488 | 9850 | 0.0 | - |
381
+ | 9.2958 | 9900 | 0.0 | - |
382
+ | 9.3427 | 9950 | 0.0 | - |
383
+ | 9.3897 | 10000 | 0.0 | - |
384
+ | 9.4366 | 10050 | 0.0 | - |
385
+ | 9.4836 | 10100 | 0.0 | - |
386
+ | 9.5305 | 10150 | 0.0 | - |
387
+ | 9.5775 | 10200 | 0.0 | - |
388
+ | 9.6244 | 10250 | 0.0 | - |
389
+ | 9.6714 | 10300 | 0.0 | - |
390
+ | 9.7183 | 10350 | 0.0 | - |
391
+ | 9.7653 | 10400 | 0.0 | - |
392
+ | 9.8122 | 10450 | 0.0 | - |
393
+ | 9.8592 | 10500 | 0.0016 | - |
394
+ | 9.9061 | 10550 | 0.0 | - |
395
+ | 9.9531 | 10600 | 0.0 | - |
396
+ | 10.0 | 10650 | 0.0 | - |
397
+ | 10.0469 | 10700 | 0.0003 | - |
398
+ | 10.0939 | 10750 | 0.0 | - |
399
+ | 10.1408 | 10800 | 0.0 | - |
400
+ | 10.1878 | 10850 | 0.0 | - |
401
+ | 10.2347 | 10900 | 0.0 | - |
402
+ | 10.2817 | 10950 | 0.0 | - |
403
+ | 10.3286 | 11000 | 0.0 | - |
404
+ | 10.3756 | 11050 | 0.0 | - |
405
+ | 10.4225 | 11100 | 0.0 | - |
406
+ | 10.4695 | 11150 | 0.0 | - |
407
+ | 10.5164 | 11200 | 0.0 | - |
408
+ | 10.5634 | 11250 | 0.0 | - |
409
+ | 10.6103 | 11300 | 0.0 | - |
410
+ | 10.6573 | 11350 | 0.0 | - |
411
+ | 10.7042 | 11400 | 0.0 | - |
412
+ | 10.7512 | 11450 | 0.0 | - |
413
+ | 10.7981 | 11500 | 0.0 | - |
414
+ | 10.8451 | 11550 | 0.0 | - |
415
+ | 10.8920 | 11600 | 0.0 | - |
416
+ | 10.9390 | 11650 | 0.0 | - |
417
+ | 10.9859 | 11700 | 0.0 | - |
418
+ | 11.0329 | 11750 | 0.0 | - |
419
+ | 11.0798 | 11800 | 0.0 | - |
420
+ | 11.1268 | 11850 | 0.0 | - |
421
+ | 11.1737 | 11900 | 0.0 | - |
422
+ | 11.2207 | 11950 | 0.0 | - |
423
+ | 11.2676 | 12000 | 0.0 | - |
424
+ | 11.3146 | 12050 | 0.0 | - |
425
+ | 11.3615 | 12100 | 0.0 | - |
426
+ | 11.4085 | 12150 | 0.0 | - |
427
+ | 11.4554 | 12200 | 0.0 | - |
428
+ | 11.5023 | 12250 | 0.0015 | - |
429
+ | 11.5493 | 12300 | 0.0 | - |
430
+ | 11.5962 | 12350 | 0.0 | - |
431
+ | 11.6432 | 12400 | 0.0 | - |
432
+ | 11.6901 | 12450 | 0.0 | - |
433
+ | 11.7371 | 12500 | 0.0 | - |
434
+ | 11.7840 | 12550 | 0.0002 | - |
435
+ | 11.8310 | 12600 | 0.0 | - |
436
+ | 11.8779 | 12650 | 0.0 | - |
437
+ | 11.9249 | 12700 | 0.0 | - |
438
+ | 11.9718 | 12750 | 0.0001 | - |
439
+ | 12.0188 | 12800 | 0.0 | - |
440
+ | 12.0657 | 12850 | 0.0 | - |
441
+ | 12.1127 | 12900 | 0.0 | - |
442
+ | 12.1596 | 12950 | 0.0001 | - |
443
+ | 12.2066 | 13000 | 0.0001 | - |
444
+ | 12.2535 | 13050 | 0.0 | - |
445
+ | 12.3005 | 13100 | 0.0 | - |
446
+ | 12.3474 | 13150 | 0.0001 | - |
447
+ | 12.3944 | 13200 | 0.0 | - |
448
+ | 12.4413 | 13250 | 0.0 | - |
449
+ | 12.4883 | 13300 | 0.0 | - |
450
+ | 12.5352 | 13350 | 0.0 | - |
451
+ | 12.5822 | 13400 | 0.0 | - |
452
+ | 12.6291 | 13450 | 0.0 | - |
453
+ | 12.6761 | 13500 | 0.0 | - |
454
+ | 12.7230 | 13550 | 0.0 | - |
455
+ | 12.7700 | 13600 | 0.0 | - |
456
+ | 12.8169 | 13650 | 0.0 | - |
457
+ | 12.8638 | 13700 | 0.0 | - |
458
+ | 12.9108 | 13750 | 0.0 | - |
459
+ | 12.9577 | 13800 | 0.0 | - |
460
+ | 13.0047 | 13850 | 0.0 | - |
461
+ | 13.0516 | 13900 | 0.0 | - |
462
+ | 13.0986 | 13950 | 0.0 | - |
463
+ | 13.1455 | 14000 | 0.0 | - |
464
+ | 13.1925 | 14050 | 0.0 | - |
465
+ | 13.2394 | 14100 | 0.0 | - |
466
+ | 13.2864 | 14150 | 0.0 | - |
467
+ | 13.3333 | 14200 | 0.0 | - |
468
+ | 13.3803 | 14250 | 0.0 | - |
469
+ | 13.4272 | 14300 | 0.0 | - |
470
+ | 13.4742 | 14350 | 0.0 | - |
471
+ | 13.5211 | 14400 | 0.0 | - |
472
+ | 13.5681 | 14450 | 0.0 | - |
473
+ | 13.6150 | 14500 | 0.0 | - |
474
+ | 13.6620 | 14550 | 0.0 | - |
475
+ | 13.7089 | 14600 | 0.0 | - |
476
+ | 13.7559 | 14650 | 0.0 | - |
477
+ | 13.8028 | 14700 | 0.0 | - |
478
+ | 13.8498 | 14750 | 0.0 | - |
479
+ | 13.8967 | 14800 | 0.0 | - |
480
+ | 13.9437 | 14850 | 0.0 | - |
481
+ | 13.9906 | 14900 | 0.0 | - |
482
+ | 14.0376 | 14950 | 0.0 | - |
483
+ | 14.0845 | 15000 | 0.0 | - |
484
+ | 14.1315 | 15050 | 0.0 | - |
485
+ | 14.1784 | 15100 | 0.0001 | - |
486
+ | 14.2254 | 15150 | 0.0 | - |
487
+ | 14.2723 | 15200 | 0.0 | - |
488
+ | 14.3192 | 15250 | 0.0 | - |
489
+ | 14.3662 | 15300 | 0.0 | - |
490
+ | 14.4131 | 15350 | 0.0 | - |
491
+ | 14.4601 | 15400 | 0.0 | - |
492
+ | 14.5070 | 15450 | 0.0 | - |
493
+ | 14.5540 | 15500 | 0.0 | - |
494
+ | 14.6009 | 15550 | 0.0 | - |
495
+ | 14.6479 | 15600 | 0.0 | - |
496
+ | 14.6948 | 15650 | 0.0 | - |
497
+ | 14.7418 | 15700 | 0.0 | - |
498
+ | 14.7887 | 15750 | 0.0 | - |
499
+ | 14.8357 | 15800 | 0.0 | - |
500
+ | 14.8826 | 15850 | 0.0 | - |
501
+ | 14.9296 | 15900 | 0.0 | - |
502
+ | 14.9765 | 15950 | 0.0 | - |
503
+ | 15.0235 | 16000 | 0.0 | - |
504
+ | 15.0704 | 16050 | 0.0 | - |
505
+ | 15.1174 | 16100 | 0.0 | - |
506
+ | 15.1643 | 16150 | 0.0 | - |
507
+ | 15.2113 | 16200 | 0.0 | - |
508
+ | 15.2582 | 16250 | 0.0 | - |
509
+ | 15.3052 | 16300 | 0.0 | - |
510
+ | 15.3521 | 16350 | 0.0 | - |
511
+ | 15.3991 | 16400 | 0.0 | - |
512
+ | 15.4460 | 16450 | 0.0 | - |
513
+ | 15.4930 | 16500 | 0.0 | - |
514
+ | 15.5399 | 16550 | 0.0 | - |
515
+ | 15.5869 | 16600 | 0.0 | - |
516
+ | 15.6338 | 16650 | 0.0 | - |
517
+ | 15.6808 | 16700 | 0.0 | - |
518
+ | 15.7277 | 16750 | 0.0 | - |
519
+ | 15.7746 | 16800 | 0.0 | - |
520
+ | 15.8216 | 16850 | 0.0 | - |
521
+ | 15.8685 | 16900 | 0.0 | - |
522
+ | 15.9155 | 16950 | 0.0 | - |
523
+ | 15.9624 | 17000 | 0.0 | - |
524
+ | 16.0094 | 17050 | 0.0 | - |
525
+ | 16.0563 | 17100 | 0.0 | - |
526
+ | 16.1033 | 17150 | 0.0 | - |
527
+ | 16.1502 | 17200 | 0.0 | - |
528
+ | 16.1972 | 17250 | 0.0 | - |
529
+ | 16.2441 | 17300 | 0.0 | - |
530
+ | 16.2911 | 17350 | 0.0 | - |
531
+ | 16.3380 | 17400 | 0.0 | - |
532
+ | 16.3850 | 17450 | 0.0 | - |
533
+ | 16.4319 | 17500 | 0.0 | - |
534
+ | 16.4789 | 17550 | 0.0 | - |
535
+ | 16.5258 | 17600 | 0.0 | - |
536
+ | 16.5728 | 17650 | 0.0 | - |
537
+ | 16.6197 | 17700 | 0.0 | - |
538
+ | 16.6667 | 17750 | 0.0 | - |
539
+ | 16.7136 | 17800 | 0.0 | - |
540
+ | 16.7606 | 17850 | 0.0 | - |
541
+ | 16.8075 | 17900 | 0.0 | - |
542
+ | 16.8545 | 17950 | 0.0 | - |
543
+ | 16.9014 | 18000 | 0.0 | - |
544
+ | 16.9484 | 18050 | 0.0 | - |
545
+ | 16.9953 | 18100 | 0.0 | - |
546
+ | 17.0423 | 18150 | 0.0 | - |
547
+ | 17.0892 | 18200 | 0.0 | - |
548
+ | 17.1362 | 18250 | 0.0 | - |
549
+ | 17.1831 | 18300 | 0.0 | - |
550
+ | 17.2300 | 18350 | 0.0 | - |
551
+ | 17.2770 | 18400 | 0.0 | - |
552
+ | 17.3239 | 18450 | 0.0 | - |
553
+ | 17.3709 | 18500 | 0.0 | - |
554
+ | 17.4178 | 18550 | 0.0 | - |
555
+ | 17.4648 | 18600 | 0.0 | - |
556
+ | 17.5117 | 18650 | 0.0 | - |
557
+ | 17.5587 | 18700 | 0.0 | - |
558
+ | 17.6056 | 18750 | 0.0 | - |
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+
611
+ ### Framework Versions
612
+ - Python: 3.10.12
613
+ - SetFit: 1.1.0.dev0
614
+ - Sentence Transformers: 3.1.1
615
+ - Transformers: 4.46.1
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+ - PyTorch: 2.4.0+cu121
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+ - Datasets: 2.20.0
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+ - Tokenizers: 0.20.0
619
+
620
+ ## Citation
621
+
622
+ ### BibTeX
623
+ ```bibtex
624
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
625
+ doi = {10.48550/ARXIV.2209.11055},
626
+ url = {https://arxiv.org/abs/2209.11055},
627
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
628
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
629
+ title = {Efficient Few-Shot Learning Without Prompts},
630
+ publisher = {arXiv},
631
+ year = {2022},
632
+ copyright = {Creative Commons Attribution 4.0 International}
633
+ }
634
+ ```
635
+
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+ <!--
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+ ## Glossary
638
+
639
+ *Clearly define terms in order to be accessible across audiences.*
640
+ -->
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+
642
+ <!--
643
+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
646
+ -->
647
+
648
+ <!--
649
+ ## Model Card Contact
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+
651
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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