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mini1013
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master_cate_bt_top9_test

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +782 -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
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+ ---
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+ base_model: mini1013/master_domain
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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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: '[6][대용량] 크리니크 쏙보습크림 125ml (+디럭스 7종 증정) 쏙보습크림 125ml 홈>현대백화점>화장품>기획세트>스킨케어;(#M)홈>스킨케어>크림
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+ HMALL > 현대백화점 > 화장품 > 기획세트 > 스킨케어'
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+ - text: 케어존 닥터솔루션 노르데나우 워터 토너 170ml (#M)SSG.COM/스킨케어/스킨/토너 ssg > 뷰티 > 스킨케어 > 스킨/토너
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+ - text: '[AK백화점][랑콤]NEW 제니피끄 아이&래쉬 세럼 20ml[33435183] 단일상품 `1106238690` (#M)SSG.COM/스킨케어/아이/넥케어/아이크림/아이세럼
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+ LOREAL > DepartmentSsg > 랑콤 > Branded > 제니피끄 세럼'
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+ - text: 아크웰 아쿠아씰 수딩 토닉 150ml (#M)11st>스킨케어>스킨/토너>스킨/토너 11st > 뷰티 > 스킨케어 > 스킨/토너
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+ - text: '[랑콤][9L] NEW 레네르지 트리플 세럼 50ml 세트 세트 (#M)홈>스킨케어>에센스/앰플 HMALL > 뷰티 > 스킨케어 >
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+ 에센스/앰플'
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+ inference: true
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+ model-index:
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+ - name: SetFit with mini1013/master_domain
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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: accuracy
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+ value: 0.8002008032128514
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with mini1013/master_domain
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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 [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
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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:** 12 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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+ | 10 | <ul><li>'한율 흰감국 톤업 크림 SPF30/PA++ 50ml 선택완료 (#M)위메프 > 뷰티 > 스킨케어 > 크림 > 톤업크림 위메프 > 뷰티 > 스킨케어 > 크림 > 톤업크림'</li><li>'동국제약 마데카크림 시즌6 50ml /에센스/기미스틱/주름스틱 11.마데카 수딩 젤 크림 50ml (#M)11st>스킨케어>수분크림>수분크림 11st > 뷰티 > 스킨케어 > 수분크림 > 수분크림'</li><li>'[참존]디에이지 레드와인 인텐스 크림 50ML 1 1_P068147977 선택/옵션선택 (#M)11st>스킨케어>수���크림>수분크림 11st > 뷰티 > 스킨케어 > 수분크림'</li></ul> |
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+ | 8 | <ul><li>'메디필 히알루론 다크베논 펩타이드9 앰플 눈가 팔자 주름 아이패치 60매 (#M)홈>화장품/미용>마스크/팩>마스크시트 Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트'</li><li>'[입큰] 쌀을 담은 아이패치(톤업 아이겔 패치,60매) 아이패치 60매입 단품 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크'</li><li>'SNP/엠솔릭 새봄맞이 특별전 UP TO 67%+전구매 증정 39_ 흑진주 리뉴 블랙 앰플 마스크 Ver.5 10매 화장품/향수>팩/마스크>마스크팩;(#M)화장품/향수>팩/마스크>마스크시트 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 마스크시트'</li></ul> |
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+ | 1 | <ul><li>'에보보떼 에보아이 아이래쉬 포뮬라 3ml 속눈썹 영양제 세럼 (#M)홈>전체상품 Naverstore > 화장품/미용 > 색조메이크업 > 속눈썹영양제'</li><li>'[봄쿠폰]마이래쉬세럼 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 마스카라 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 마스카라'</li><li>'코스노리 롱 액티브 마스카라 리무버 - 롱 액티브 마스카라 리무버 (#M)화장품/미용>클렌징>립앤아이리무버 AD > Naverstore > 화장품/미용 > 클렌징 > 립앤아이리무버'</li></ul> |
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+ | 6 | <ul><li>'숨 37도 타임에너지 3종 세트 21년 4월기획 (#M)홈>화장품/미용>스킨케어>화장품세트 Naverstore > 화장품/미용 > 스킨케어 > 화장품세트'</li><li>'[신제품샘플증정][6빅딜]클리어로션+피테라에센스 230ml세트 LotteOn > 뷰티 > 명품화장품 > 스킨케어 LotteOn > 뷰티 > 스킨케어 > 화장품세트'</li><li>'라벨영 쇼킹블랙티트리 스킨로션SET/민감피부진정 블랙티트리스킨로션 세트 ssg > 뷰티 > 스킨케어 > 에센스/앰플 ssg > 뷰티 > 스킨케어 > 에센스/앰플'</li></ul> |
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+ | 5 | <ul><li>'라네즈 베스트어워즈 크림스킨 더블 기획세트 슬리퍼 (#M)GSSHOP>뷰티>스킨케어>뷰티 합포장 GSSHOP > 뷰티 > 스킨케어 > 뷰티 합포장'</li><li>'[즉시10%+중복10%+묶음10%+T11%]토니모리 BIG SALE 11번가 단독 1+1 외 BEST~82%+콜라겐부스터 68_플로리아 뉴트라 에너지_토너 150ml 11st>선케어>선크림/선블록>선크림/선블록;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st > 뷰티 > 선케어 > 선크림/선블록 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업'</li><li>'토니모리 투엑스알 콜라겐 스킨64666227 140ml x 1개64666227 LotteOn > 뷰티 > 뷰티기기/소품 > 위생용품 > 화장솜 LotteOn > 뷰티 > 뷰티기기/소품 > 위생용품 > 화장솜'</li></ul> |
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+ | 9 | <ul><li>'다나한 본연진 에센스 보은세트/선물용/다나한본연진 MinSellAmount (#M)화장품/향수>스킨케어>에센스/세럼 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼'</li><li>'티 엘릭시어 스킨 리질리언스 액티베이팅 세럼 50ml 티 엘릭시어 세럼 50ml LotteOn > 뷰티 > 럭셔리 스킨케어 > 에센스/세럼 LotteOn > 뷰티 > 럭셔리 스킨케어 > 에센스/세럼'</li><li>'5 X SkinCeuticals 스킨수티컬즈 PREVENT - Resveratrol B E 0.13oz / 4ml Each 338637 LOREAL > LotteOn > 스킨수티컬즈 > Branded > Skinceuticals LOREAL > LotteOn > 스킨수티컬즈 > Branded > Skinceuticals'</li></ul> |
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+ | 4 | <ul><li>'메디필 펩타이드9 멜라 스틱 (#M)11st>스킨케어>화이트닝크림>화이트닝크림 11st > 뷰티 > 스킨케어 > 화이트닝크림 > 화이트닝크림'</li><li>'눅스 3월 BRAND DAY!! 환절기 보습 베스트 아이템 SALE UP TO 62% (멀티오일/립밤/핸드크림/바디로션) 눅스 에너지 부스트 나이트 리커버리밤 50ml_품목 / 20230327_31 11st>뷰티>스킨케어>크림;11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 크림 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 크림'</li><li>'가히 멀티밤 + 하이라이터 1세트 LotteOn > 뷰티 > 남성화장품 > 올인원 LotteOn > 뷰티 > 남성화장품 > 올인원'</li></ul> |
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+ | 0 | <ul><li>'[AK분당점] [클라랑스] NEW 엑스트라 퍼밍 넥 크림 75ml 80056458 `1075185169` LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디로션/크림 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디로션/크림'</li><li>'끄렘므 뿌르 르 꾸 50ml ssg > 뷰티 > 스킨케어 > 크림 ssg > 뷰티 > 스킨케어 > 크림'</li><li>'[1만원 상품권][4][단독] 기적의 크림 60ml 세트 (+18만 5천원 상당 넥/데콜테 크림) 모이스춰라이징 쿨 젤 크림 ssg > 뷰티 > 스킨케어 > 크림;ssg > 뷰티 > 명품화장품 > 스킨케어 세트;ssg > 뷰티 > 스킨케어 > 스킨케어세트 ssg > 뷰티 > 스킨케어 > 크림'</li></ul> |
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+ | 7 | <ul><li>'시슬리아 랭테그랄 앙티 아쥬 아이 앤 립 콘투어 크림 15ml 백화점 면세점 동일 제품 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어'</li><li>'NEW 바이탈 퍼펙션 업리프팅 앤 퍼밍 익스프레스 아이 마스크 1.단품없음 (#M)화장품/향수>팩/마스크>마스크시트 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 마스크시트'</li><li>'라이트풀 C3 래디언트 하이드레이션 톤-업 아이 크림 (#M)화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li></ul> |
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+ | 11 | <ul><li>'달팡 - 에센셜 오일 엘릭서 베티버 아로마틱 케어 스트레스 렐리프 디톡스 15ml/0.5oz ssg > 뷰티 > 스킨케어 > 로션 ssg > 뷰티 > 스킨케어 > 로션'</li><li>'[본사직영정품]이지피지 오일 판타스틱 15g ssg > 뷰티 > 스킨케어 > 립케어;ssg > 뷰티 > 메이크업 > 립메이크업 > 립밤 ssg > 뷰티 > 메이크업 > 립메이크업 > 립밤'</li><li>'타마누오일 11ml(페이스오일/피부진정/보습효능/100%원액/마다카스카르섬/호동씨오일) 타마누오일 (#M)홈>화장품/미용>스킨케어>페이스오일 Naverstore > 화장품/미용 > 스킨케어 > 페이스오일'</li></ul> |
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+ | 3 | <ul><li>'달바 모이스트 프레쉬 선미스트 본품 2개 MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'</li><li>'비욘드 딥 모이스처 에센셜 미스트 파우더리 머스크향 200ml × 1개 (#M)쿠팡 홈>뷰티>바디>바디로션/크림>바디미스트/샤워코롱 Coupang > 뷰티 > 로드샵 > 바디 > 바디로션/크림 > 바디미스트/샤워코롱'</li><li>'[AKmall][꼬달리] 그레이프 워터 75ml LotteOn > 뷰티 > 스킨케어 > 미스트 LotteOn > 뷰티 > 스킨케어 > 미스트'</li></ul> |
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+ | 2 | <ul><li>'멘소래담 아크네스 모이스처 로션 수분 보습 피부진정 MinSellAmount (#M)화장품/향수>스킨케어>로션/에멀젼 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 로션/에멀젼'</li><li>'[아모레퍼시픽] 4월 더 밝게 빛나는 봄의 뷰티를 위한 최고 ! #헤라#프리메라 34. 오가니언스베리어리페어하이드레이션크림에멀젼_선택완료 11st>메이크업>페이스메이크업>파운데이션;쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨케어 세트>스킨케어 세트;11st>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li><li>'[20%중복+T11%] 네이처리퍼블릭 색조 BEST! (쿠션/틴트/마스카라) 39_스네일 에멀전_에멀전10010015NK0228 쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>치크/하이라이터>치크/블러셔;11st > 뷰티 > 메이크업 > 치크/하이라이터 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업'</li></ul> |
80
+
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+ ## Evaluation
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+
83
+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.8002 |
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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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+
94
+ ```bash
95
+ pip install setfit
96
+ ```
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+
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+ Then you can load this model and run inference.
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+
100
+ ```python
101
+ 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_cate_bt_top9_test")
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+ # Run inference
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+ preds = model("아크웰 아쿠아씰 수딩 토닉 150ml (#M)11st>스킨케어>스킨/토너>스킨/토너 11st > 뷰티 > 스킨케어 > 스킨/토너")
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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 | 11 | 21.3033 | 91 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 50 |
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+ | 1 | 50 |
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+ | 2 | 50 |
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+ | 3 | 50 |
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+ | 4 | 50 |
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+ | 5 | 50 |
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+ | 6 | 50 |
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+ | 7 | 50 |
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+ | 8 | 50 |
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+ | 9 | 50 |
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+ | 10 | 50 |
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+ | 11 | 50 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (64, 64)
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+ - num_epochs: (30, 30)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 100
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0011 | 1 | 0.4352 | - |
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+ | 0.0533 | 50 | 0.431 | - |
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+ | 0.1066 | 100 | 0.4278 | - |
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+ | 0.1599 | 150 | 0.4267 | - |
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+ | 0.2132 | 200 | 0.4198 | - |
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+ | 0.2665 | 250 | 0.392 | - |
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+ | 0.3198 | 300 | 0.3682 | - |
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+ | 0.3731 | 350 | 0.3335 | - |
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+ | 0.4264 | 400 | 0.2896 | - |
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+ | 0.4797 | 450 | 0.2464 | - |
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+ | 0.5330 | 500 | 0.2338 | - |
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+ | 0.5864 | 550 | 0.2243 | - |
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+ | 0.6397 | 600 | 0.2238 | - |
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+ | 0.6930 | 650 | 0.2188 | - |
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+ | 0.7463 | 700 | 0.212 | - |
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+ | 0.7996 | 750 | 0.2139 | - |
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+ | 0.8529 | 800 | 0.2041 | - |
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+ | 0.9062 | 850 | 0.1973 | - |
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+ | 0.9595 | 900 | 0.188 | - |
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+ | 1.0128 | 950 | 0.1784 | - |
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+ | 1.0661 | 1000 | 0.1758 | - |
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+ | 1.1194 | 1050 | 0.177 | - |
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+ | 1.1727 | 1100 | 0.1735 | - |
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+ | 1.2260 | 1150 | 0.1667 | - |
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+ | 1.2793 | 1200 | 0.163 | - |
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+ | 1.3326 | 1250 | 0.1583 | - |
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+ | 1.3859 | 1300 | 0.1489 | - |
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+ | 1.4392 | 1350 | 0.1428 | - |
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+ | 1.4925 | 1400 | 0.1343 | - |
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+ | 1.5458 | 1450 | 0.1325 | - |
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+ | 1.5991 | 1500 | 0.1252 | - |
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+ | 1.6525 | 1550 | 0.1164 | - |
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+ | 1.7058 | 1600 | 0.1063 | - |
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+ | 1.7591 | 1650 | 0.0968 | - |
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+ | 1.8124 | 1700 | 0.0844 | - |
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+ | 1.8657 | 1750 | 0.0718 | - |
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+ | 1.9190 | 1800 | 0.0646 | - |
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+ | 1.9723 | 1850 | 0.0504 | - |
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+ | 2.0256 | 1900 | 0.0493 | - |
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+ | 2.0789 | 1950 | 0.0438 | - |
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+ | 2.1322 | 2000 | 0.0433 | - |
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+ | 2.1855 | 2050 | 0.0425 | - |
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+ | 2.2388 | 2100 | 0.0399 | - |
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+ | 2.2921 | 2150 | 0.0319 | - |
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+ | 2.3454 | 2200 | 0.0294 | - |
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+ | 2.3987 | 2250 | 0.0292 | - |
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+ | 2.4520 | 2300 | 0.0254 | - |
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+ | 2.5053 | 2350 | 0.0248 | - |
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+ | 2.5586 | 2400 | 0.0259 | - |
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+ | 2.6119 | 2450 | 0.0222 | - |
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+ | 2.6652 | 2500 | 0.0217 | - |
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+ | 2.7186 | 2550 | 0.0225 | - |
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+ | 2.7719 | 2600 | 0.0185 | - |
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+ | 2.8252 | 2650 | 0.0143 | - |
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+ | 2.8785 | 2700 | 0.013 | - |
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+ | 2.9318 | 2750 | 0.013 | - |
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+ | 2.9851 | 2800 | 0.0083 | - |
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+ | 3.0384 | 2850 | 0.0079 | - |
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+ | 3.0917 | 2900 | 0.0059 | - |
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+ | 3.1450 | 2950 | 0.0063 | - |
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+ | 3.1983 | 3000 | 0.0029 | - |
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+ | 3.2516 | 3050 | 0.0027 | - |
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+ | 3.3049 | 3100 | 0.0016 | - |
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+ | 3.3582 | 3150 | 0.0027 | - |
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+ | 3.4115 | 3200 | 0.0024 | - |
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+ | 3.4648 | 3250 | 0.0032 | - |
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+ | 3.5181 | 3300 | 0.0032 | - |
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+ | 3.5714 | 3350 | 0.0025 | - |
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+ | 3.6247 | 3400 | 0.0029 | - |
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+ | 3.6780 | 3450 | 0.0041 | - |
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+ | 3.7313 | 3500 | 0.0035 | - |
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+ | 3.7846 | 3550 | 0.0018 | - |
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+ | 3.8380 | 3600 | 0.0021 | - |
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+ | 3.8913 | 3650 | 0.0021 | - |
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+ | 3.9446 | 3700 | 0.0019 | - |
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+ | 3.9979 | 3750 | 0.0017 | - |
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+ | 4.0512 | 3800 | 0.0015 | - |
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+ | 4.1045 | 3850 | 0.0018 | - |
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+ | 4.1578 | 3900 | 0.0016 | - |
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+ | 4.2111 | 3950 | 0.0009 | - |
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+ | 4.2644 | 4000 | 0.0009 | - |
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+ | 4.3177 | 4050 | 0.0013 | - |
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+ | 4.3710 | 4100 | 0.0013 | - |
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+ | 4.4243 | 4150 | 0.0004 | - |
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+ | 4.4776 | 4200 | 0.0001 | - |
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+ | 4.5309 | 4250 | 0.0004 | - |
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+ | 4.5842 | 4300 | 0.0005 | - |
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+ | 4.6375 | 4350 | 0.0028 | - |
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+ | 4.6908 | 4400 | 0.0024 | - |
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+ | 4.7441 | 4450 | 0.0024 | - |
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+ | 4.7974 | 4500 | 0.0015 | - |
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+ | 4.8507 | 4550 | 0.0005 | - |
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+ | 4.9041 | 4600 | 0.0006 | - |
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+ | 4.9574 | 4650 | 0.0009 | - |
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+ | 5.0107 | 4700 | 0.0004 | - |
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+ | 5.0640 | 4750 | 0.0005 | - |
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+ | 5.1173 | 4800 | 0.0006 | - |
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+ | 5.1706 | 4850 | 0.0001 | - |
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+ | 5.2239 | 4900 | 0.0002 | - |
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+ | 5.2772 | 4950 | 0.0001 | - |
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+ | 5.3305 | 5000 | 0.0015 | - |
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+ | 5.3838 | 5050 | 0.0009 | - |
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+ | 5.4371 | 5100 | 0.0012 | - |
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+ | 5.4904 | 5150 | 0.0005 | - |
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+ | 5.5437 | 5200 | 0.0002 | - |
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+ | 5.5970 | 5250 | 0.0001 | - |
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+ | 5.6503 | 5300 | 0.0001 | - |
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+ | 5.7036 | 5350 | 0.0001 | - |
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+ | 5.7569 | 5400 | 0.0 | - |
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+ | 5.8102 | 5450 | 0.0 | - |
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+ | 5.8635 | 5500 | 0.0 | - |
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+ | 5.9168 | 5550 | 0.0 | - |
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+ | 5.9701 | 5600 | 0.0 | - |
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+ | 6.0235 | 5650 | 0.0001 | - |
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+ | 6.0768 | 5700 | 0.0 | - |
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+ | 6.1301 | 5750 | 0.0 | - |
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+ | 6.1834 | 5800 | 0.0001 | - |
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+ | 6.2367 | 5850 | 0.0001 | - |
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+ | 6.2900 | 5900 | 0.0008 | - |
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+ | 6.3433 | 5950 | 0.0009 | - |
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+ | 6.3966 | 6000 | 0.0007 | - |
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+ | 6.4499 | 6050 | 0.0051 | - |
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+ | 6.5032 | 6100 | 0.0178 | - |
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+ | 6.5565 | 6150 | 0.0118 | - |
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+ | 6.6098 | 6200 | 0.0023 | - |
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+ | 6.6631 | 6250 | 0.0003 | - |
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+ | 6.7164 | 6300 | 0.0002 | - |
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+ | 6.7697 | 6350 | 0.0002 | - |
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+ | 6.8230 | 6400 | 0.0003 | - |
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+ | 6.8763 | 6450 | 0.0006 | - |
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+ | 6.9296 | 6500 | 0.0001 | - |
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+ | 6.9829 | 6550 | 0.0001 | - |
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+ | 7.0362 | 6600 | 0.0 | - |
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+ | 7.0896 | 6650 | 0.0 | - |
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+ | 7.1429 | 6700 | 0.0 | - |
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+ | 7.1962 | 6750 | 0.0 | - |
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+ | 7.2495 | 6800 | 0.0 | - |
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+ | 7.3028 | 6850 | 0.0 | - |
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+ | 7.3561 | 6900 | 0.0 | - |
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+ | 7.4094 | 6950 | 0.0 | - |
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+ | 7.4627 | 7000 | 0.0 | - |
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+ | 7.6226 | 7150 | 0.0 | - |
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+ | 7.6759 | 7200 | 0.0008 | - |
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+ | 7.7292 | 7250 | 0.0002 | - |
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+ | 7.7825 | 7300 | 0.0 | - |
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+ | 7.8358 | 7350 | 0.0001 | - |
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+ | 7.8891 | 7400 | 0.0 | - |
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+ | 7.9424 | 7450 | 0.0 | - |
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+ | 7.9957 | 7500 | 0.0 | - |
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+ | 8.0490 | 7550 | 0.0 | - |
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+ | 8.1023 | 7600 | 0.0 | - |
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+ | 8.1557 | 7650 | 0.0 | - |
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+ | 8.2090 | 7700 | 0.0 | - |
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+ | 8.2623 | 7750 | 0.0 | - |
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+ | 8.3156 | 7800 | 0.0003 | - |
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+ | 8.3689 | 7850 | 0.0005 | - |
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+ | 8.4222 | 7900 | 0.0007 | - |
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+ | 8.4755 | 7950 | 0.0022 | - |
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+ | 8.5288 | 8000 | 0.0017 | - |
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+ | 8.5821 | 8050 | 0.0025 | - |
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+ | 8.6354 | 8100 | 0.0023 | - |
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+ | 8.6887 | 8150 | 0.0008 | - |
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+ | 8.7420 | 8200 | 0.0002 | - |
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+ | 8.7953 | 8250 | 0.0008 | - |
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+ | 8.8486 | 8300 | 0.0011 | - |
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+ | 8.9019 | 8350 | 0.0003 | - |
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+ | 8.9552 | 8400 | 0.0 | - |
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+ | 9.0085 | 8450 | 0.0002 | - |
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+ | 9.0618 | 8500 | 0.0001 | - |
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+ | 9.1151 | 8550 | 0.0 | - |
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+ | 9.1684 | 8600 | 0.0 | - |
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+ | 9.2217 | 8650 | 0.0 | - |
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+ | 9.2751 | 8700 | 0.0 | - |
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+ | 9.3284 | 8750 | 0.0 | - |
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+ | 9.3817 | 8800 | 0.0 | - |
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+ | 9.4350 | 8850 | 0.0 | - |
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+ | 9.4883 | 8900 | 0.0 | - |
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+ | 9.5416 | 8950 | 0.0 | - |
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+ | 9.6482 | 9050 | 0.0 | - |
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+ | 9.7015 | 9100 | 0.0 | - |
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+ | 9.7548 | 9150 | 0.0 | - |
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+ | 9.8081 | 9200 | 0.0 | - |
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+ | 9.8614 | 9250 | 0.0 | - |
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+ | 9.9147 | 9300 | 0.0 | - |
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+ | 9.9680 | 9350 | 0.0 | - |
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+ | 10.0213 | 9400 | 0.0 | - |
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+ | 10.0746 | 9450 | 0.0 | - |
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+ | 10.1279 | 9500 | 0.0 | - |
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+ | 10.1812 | 9550 | 0.0 | - |
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+ | 10.2345 | 9600 | 0.0 | - |
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+ | 10.2878 | 9650 | 0.0 | - |
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+ | 10.3412 | 9700 | 0.0 | - |
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+ | 10.4478 | 9800 | 0.0 | - |
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+ | 10.5011 | 9850 | 0.0 | - |
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+ | 10.5544 | 9900 | 0.0 | - |
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+ | 10.6077 | 9950 | 0.0 | - |
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+ | 10.6610 | 10000 | 0.0 | - |
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+ | 10.7143 | 10050 | 0.0 | - |
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+ | 10.7676 | 10100 | 0.0 | - |
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+ | 10.8209 | 10150 | 0.0 | - |
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+ | 10.8742 | 10200 | 0.0 | - |
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+ | 10.9275 | 10250 | 0.0 | - |
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+ | 10.9808 | 10300 | 0.0002 | - |
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+ | 11.0341 | 10350 | 0.003 | - |
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+ | 11.0874 | 10400 | 0.0074 | - |
386
+ | 11.1407 | 10450 | 0.0052 | - |
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+ | 11.1940 | 10500 | 0.0034 | - |
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+ | 11.2473 | 10550 | 0.0038 | - |
389
+ | 11.3006 | 10600 | 0.0029 | - |
390
+ | 11.3539 | 10650 | 0.0027 | - |
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+ | 11.4072 | 10700 | 0.002 | - |
392
+ | 11.4606 | 10750 | 0.0013 | - |
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+ | 11.5139 | 10800 | 0.002 | - |
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+ | 11.5672 | 10850 | 0.0012 | - |
395
+ | 11.6205 | 10900 | 0.001 | - |
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+ | 11.6738 | 10950 | 0.0007 | - |
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+ | 11.7271 | 11000 | 0.001 | - |
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+ | 11.7804 | 11050 | 0.0006 | - |
399
+ | 11.8337 | 11100 | 0.0 | - |
400
+ | 11.8870 | 11150 | 0.0 | - |
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+ | 11.9403 | 11200 | 0.0 | - |
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+ | 11.9936 | 11250 | 0.0 | - |
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+ | 12.0469 | 11300 | 0.0 | - |
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+ | 12.1002 | 11350 | 0.0 | - |
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+ | 12.1535 | 11400 | 0.0 | - |
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+ | 12.2068 | 11450 | 0.0 | - |
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+ | 12.2601 | 11500 | 0.0 | - |
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+ | 12.3134 | 11550 | 0.0 | - |
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+ | 12.3667 | 11600 | 0.0 | - |
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+ | 12.4200 | 11650 | 0.0 | - |
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+ | 12.4733 | 11700 | 0.0 | - |
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+ | 12.5267 | 11750 | 0.0 | - |
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+ | 12.5800 | 11800 | 0.0 | - |
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+ | 12.6333 | 11850 | 0.0 | - |
415
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416
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418
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454
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456
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457
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485
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486
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488
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496
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497
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
+ | 27.9851 | 26250 | 0.0 | - |
703
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704
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705
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706
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707
+ | 28.2516 | 26500 | 0.0 | - |
708
+ | 28.3049 | 26550 | 0.0 | - |
709
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710
+ | 28.4115 | 26650 | 0.0 | - |
711
+ | 28.4648 | 26700 | 0.0 | - |
712
+ | 28.5181 | 26750 | 0.0 | - |
713
+ | 28.5714 | 26800 | 0.0 | - |
714
+ | 28.6247 | 26850 | 0.0 | - |
715
+ | 28.6780 | 26900 | 0.0 | - |
716
+ | 28.7313 | 26950 | 0.0 | - |
717
+ | 28.7846 | 27000 | 0.0 | - |
718
+ | 28.8380 | 27050 | 0.0 | - |
719
+ | 28.8913 | 27100 | 0.0 | - |
720
+ | 28.9446 | 27150 | 0.0 | - |
721
+ | 28.9979 | 27200 | 0.0 | - |
722
+ | 29.0512 | 27250 | 0.0 | - |
723
+ | 29.1045 | 27300 | 0.0 | - |
724
+ | 29.1578 | 27350 | 0.0 | - |
725
+ | 29.2111 | 27400 | 0.0 | - |
726
+ | 29.2644 | 27450 | 0.0 | - |
727
+ | 29.3177 | 27500 | 0.0 | - |
728
+ | 29.3710 | 27550 | 0.0 | - |
729
+ | 29.4243 | 27600 | 0.0 | - |
730
+ | 29.4776 | 27650 | 0.0 | - |
731
+ | 29.5309 | 27700 | 0.0 | - |
732
+ | 29.5842 | 27750 | 0.0 | - |
733
+ | 29.6375 | 27800 | 0.0 | - |
734
+ | 29.6908 | 27850 | 0.0 | - |
735
+ | 29.7441 | 27900 | 0.0 | - |
736
+ | 29.7974 | 27950 | 0.0 | - |
737
+ | 29.8507 | 28000 | 0.0 | - |
738
+ | 29.9041 | 28050 | 0.0 | - |
739
+ | 29.9574 | 28100 | 0.0 | - |
740
+
741
+ ### Framework Versions
742
+ - Python: 3.10.12
743
+ - SetFit: 1.1.0
744
+ - Sentence Transformers: 3.3.1
745
+ - Transformers: 4.44.2
746
+ - PyTorch: 2.2.0a0+81ea7a4
747
+ - Datasets: 3.2.0
748
+ - Tokenizers: 0.19.1
749
+
750
+ ## Citation
751
+
752
+ ### BibTeX
753
+ ```bibtex
754
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
755
+ doi = {10.48550/ARXIV.2209.11055},
756
+ url = {https://arxiv.org/abs/2209.11055},
757
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
758
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
759
+ title = {Efficient Few-Shot Learning Without Prompts},
760
+ publisher = {arXiv},
761
+ year = {2022},
762
+ copyright = {Creative Commons Attribution 4.0 International}
763
+ }
764
+ ```
765
+
766
+ <!--
767
+ ## Glossary
768
+
769
+ *Clearly define terms in order to be accessible across audiences.*
770
+ -->
771
+
772
+ <!--
773
+ ## Model Card Authors
774
+
775
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
776
+ -->
777
+
778
+ <!--
779
+ ## Model Card Contact
780
+
781
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
782
+ -->
config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "mini1013/master_item_bt_test_flat_top",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "gradient_checkpointing": false,
11
+ "hidden_act": "gelu",
12
+ "hidden_dropout_prob": 0.1,
13
+ "hidden_size": 768,
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
16
+ "layer_norm_eps": 1e-05,
17
+ "max_position_embeddings": 514,
18
+ "model_type": "roberta",
19
+ "num_attention_heads": 12,
20
+ "num_hidden_layers": 12,
21
+ "pad_token_id": 1,
22
+ "position_embedding_type": "absolute",
23
+ "tokenizer_class": "BertTokenizer",
24
+ "torch_dtype": "float32",
25
+ "transformers_version": "4.44.2",
26
+ "type_vocab_size": 1,
27
+ "use_cache": true,
28
+ "vocab_size": 32000
29
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.2.0a0+81ea7a4"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
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