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mini1013
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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 +736 -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: klue/roberta-base
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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: '[5월/롯데단독] 더 슬림 벨벳 래디컬 듀오 세트(+향수 미니어처) 1966호_1966호 LotteOn > 백화점 > 뷰티 > 상단
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+ 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립스틱'
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+ - text: 파이버윅 마스카라 + 볼륨 마스카라(브라운) 홈>MAKE-UP;(#M)홈>MAKE-UP>아이>마스카라 Naverstore > 화장품/미용
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+ > 색조메이크업 > 마스카라
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+ - text: MAC 러스터글래스 립스틱 오 구디 (#M)화장품/향수>색조메이크업>립스틱 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 >
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+ 립스틱
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+ - text: 에르메스 루즈 새틴 립스틱 3.5g 64 LotteOn > 뷰티 > 색조메이크업 > 립메이크업 > 립스틱 LotteOn > 뷰티 >
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+ 색조메이크업 > 립메이크업 > 립스틱
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+ - text: 프리사이슬리 마이 브로우 펜슬 세트(+브로우 디럭스 증정) 3.5 미디움 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 LotteOn
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+ > 뷰티 > 메이크업 > 아이메이크업
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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: accuracy
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+ value: 0.8601678463697996
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+ name: Accuracy
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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:** 11 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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+ | 0 | <ul><li>'[4월/SSG단독] 따뚜아쥬 꾸뛰르 벨벳 틴트 듀오 세트(+레드 키링 핸드미러) 211호_201호 LOREAL > DepartmentSsg > 입생로랑 > Branded > 따뚜아쥬 꾸뛰르 LOREAL > DepartmentSsg > 입생로랑 > Branded > 따뚜아쥬 꾸뛰르'</li><li>'롬앤 11번가 런칭! 모든 취향 취급 중! 밀크 그로서리 외 BEST 1+1 UP TO 65% 옵션33. 제로 벨벳 틴트_14 피칸 타르트 쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>���이메이크업>마스카라;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'</li><li>'[15% 즉시할인 + 1만원이상 10%할인] 메이블린 뉴욕 슈퍼스테이 립 잉크 2개 [증정]고급 엠보싱 화장솜 2개 175호 링리더_시커 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸'</li></ul> |
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+ | 4 | <ul><li>'비디보브 VDIVOV 아이컷 마스카라 02 롱앤볼륨 × 1개 Coupang > 뷰티 > 메이크업 > 아이 메이크업 > 마스카라;(#M)쿠팡 홈>뷰티>메이크업>아이 메이크업>마스카라 Coupang > 뷰티 > 메이크업 > 아이 메이크업 > 마스카라'</li><li>'이니스프리 스키니 꼼꼼 마스카라 Zero 3.5g 2호 브라운 × 1개 LotteOn > 뷰티 > 메이크업 > 아이메이크업 > 마스카라 LotteOn > 뷰티 > 메이크업 > 아이메이크업 > 마스카라'</li><li>'이니스프리 스키니 꼼꼼카라 2호 브라운 2개 MinSellAmount (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'</li></ul> |
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+ | 8 | <ul><li>'[단독] 브로우 파이버 젤 + 브로우 스타일러 세트 (+테크나콜 라이너 정품 & 픽서 샘플 ) 스트러트_플링 (#M)DepartmentSsg > MAC > EYE > BROW LOREAL > DepartmentSsg > 슈에무라 > Generic > 립스틱'</li><li>'Maybelline New York Brow Precise Shaping Eyebrow Pencil, Auburn, 0.02 oz. Deep Brown_One Size LotteOn > 뷰티 > 뷰티소품 > 아이소품 > 샤프너 LotteOn > 뷰티 > 뷰티소품 > 아이소품 > 샤프너'</li><li>'[K쇼핑][메이블린뉴욕] 디파인&블렌드 아이브로우 펜슬 0.16g 그레이브라운 LotteOn > 뷰티 > 뷰티기기 > 눈썹관리 > 눈썹정리기 LotteOn > 뷰티 > 뷰티기기 > 눈썹관리 > 눈썹정리기'</li></ul> |
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+ | 9 | <ul><li>'컨실러 브러쉬 미디엄 176 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'[에뛰드] 룩 앳 마이 아이즈 쿠키칩스 2g 카페BR437오븐넣고5분 홈>신제품🌹;홈>신제품;홈>아이>섀도우;(#M)홈>아이>전체 Naverstore > 화장품/미용 > 색조메이크업 > 아이섀도'</li><li>'아티스트 컬러 아이 섀도우 I-520 핑키 샌드 홈>★정품 증정 & 할인세트★;홈>★단 7일간! 특별세트★;홈>★SPECIAL SET★;홈>EYE;홈>아이 & 립;홈>■할인 & 단독세트■;홈>★단 하루 포인트혜택★;홈>15% OFF;홈>■단독 구성■;홈>■2021 역대급 구성■;홈>■네이버 단독구성■;(#M)홈>EYE & LIP Naverstore > 화장품/미용 > 색조메이크업 > 아이섀도'</li></ul> |
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+ | 6 | <ul><li>'쓰리씨이 (3CE X TOILETPAPER) 3CE 페이스 블러쉬 LET ME STAY_FRE (#M)화장품/향수>색조메이크업>블러셔 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 블러셔'</li><li>'[베네피트] [태연픽] 베네피트 WANDERful World 블러쉬 & BOP 미니 9종 택1 피친 미니 (#M)11st>메이크업>치크/하이라이터>치크/블러셔 11st > 뷰티 > 메이크업 > 치크/하이라이터'</li><li>'로라메르시에 블러쉬 컬러 인퓨전 구아바 LotteOn > 뷰티 > 메이크업 > 블러셔 LotteOn > 뷰티 > 메이크업 > 블러셔'</li></ul> |
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+ | 1 | <ul><li>'[LG뷰티]비디보브 립컷 라이너 PK102 그런지 핑크 (#M)홈>화장품/미용>메이크업>립메이크업>립라이너 HMALL > 뷰티 > 화장품/미용 > 메이크업 > 립메이크업 > 립라이너'</li><li>'조르지오 아르마니 스무스 실크 립 펜슬 - 06 1.14g (#M)11st>선케어>선밤>선밤 11st > 뷰티 > 선케어 > 선밤 > 선밤'</li><li>'Maybelline New York Maybelline New York Color Sensational Shaping Lip Liner Makeup, Pink Wink, 0.01 LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트 LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트'</li></ul> |
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+ | 3 | <ul><li>'[온세일]페탈 키스 립스틱 4g (옵션) 01 딥로즈 LotteOn > 뷰티 > 색조메이크업 > 립메이크업 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립틴트'</li><li>'에스쁘아 컬러풀 누드 립스틱 노웨어 3.5g 4호 메멘토 × 1개 쿠팡 홈>선물스토어>생일선물>여성선물>화장품>립스틱;쿠팡 홈>선물스토어>생일>화장품>립스틱;Coupang > 뷰티 > 메이크업 > 립 메이크업;(#M)쿠팡 홈>뷰티>메이크업>립 메이크업>립스틱 Coupang > 뷰티 > 로드샵 > 메이크업 > 립 메이크업 > 립스틱'</li><li>'[4월/SSG단독] 루쥬 볼립떼 샤인 듀오 세트(+레드 키링 핸드미러) 83호_154호 LOREAL > DepartmentSsg > 입생로랑 > Branded > 입생로랑 LOREAL > DepartmentSsg > 입생로랑 > Branded > 입생로랑'</li></ul> |
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+ | 7 | <ul><li>'퍼펙트 디자이닝 아이라이너 펜슬 아이라이너 워터프루프 펜슬_03_딥 블랙 (#M)뷰티>화장품/향수>포인트메이크업>아이라이너 CJmall > 뷰티 > 화장품/향수 > 포인트메이크업 > 아이라이너'</li><li>'미샤 팔레트 페인트 라이너 6ml 02 브라운 홈>전체상품;홈>네이처리퍼블릭;(#M)홈>미샤 Naverstore > 화장품/미용 > 색조메이크업 > 아이라이너 > 젤형'</li><li>'리르 리얼 타투펜 아이라이너 리얼펜_딥블랙 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이라이너 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이라이너'</li></ul> |
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+ | 10 | <ul><li>'Maybelline New York Facestudio Master Chrome Metallic Highlighter Makeup Molten Topaz 019 Ounce Molten Topaz_One Size (#M)SSG.COM/미용기기/소품/메이크업브러쉬/페이스브러쉬 ssg > 뷰티 > 미용기기/소품 > 메이크업브러쉬 > 페이스브러쉬'</li><li>'페리페라 잉크 브이 쉐딩 [단품]003 헤이즐그레이 홈>메이크업>베이스>쉐이딩/컨투어링;(#M)홈>메이크업>베이스메이크업>쉐이딩/컨투어링 OLIVEYOUNG > 메이크업 > 베이스메이크업 > 쉐이딩/컨투어링'</li><li>'샤넬 바움 에쌍씨엘 멀티 스틱 8g 2종 1. 스컬프팅 Sculpting 홈>화장품/미용>색조메이크업>하이라이터;(#M)홈>화장품/미용>색조메이크업>하이라이터/쉐이딩 Naverstore > 화장품/미용 > 색조메이크업 > 하이라이터/쉐이딩'</li></ul> |
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+ | 2 | <ul><li>'유리아쥬 제모스 무향 스틱 3개 무향 × 3개 (#M)쿠팡 홈>뷰티>메이크업>립 메이크업>립케어 Coupang > 뷰티 > 메이크업 > 립 메이크업 > 립케어'</li><li>'(+필로우케이스 )버츠비 틴티드립밤 x2 6종 택2 01_로즈_03_매그놀리아 (#M)GSSHOP>뷰티>포인트메이크업>립케어 GSSHOP > 뷰티 > 포인트메이크업 > 립케어'</li><li>'[각인서비스/벨벳파우치 증정] 봄 데 뮤제 립 밤 7g 레드_바이올렛 파우치_개인화 문구(특수문자 포함 최대 4글자/배송메세지 기재) ssg > 뷰티 > 향수 > 여성향수;ssg > 뷰티 > 스킨케어 > 립케어 ssg > 뷰티 > 메이크업 > 립메이크업'</li></ul> |
80
+ | 5 | <ul><li>'5 꿀뢰르 꾸뛰르 079 블랙 보우 LotteOn > 뷰티 > 명품화장품 > 메이크업 LotteOn > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우'</li><li>'[뽀샵브러쉬] 블러쉬 브러쉬 키트 160 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'퓨어 컬러 엔비 립스틱 크림 - 블레임레스 ssg > 뷰티 > 메이크업 > 립메이크업 ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱'</li></ul> |
81
+
82
+ ## Evaluation
83
+
84
+ ### Metrics
85
+ | Label | Accuracy |
86
+ |:--------|:---------|
87
+ | **all** | 0.8602 |
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+
89
+ ## Uses
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+
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+ ### Direct Use for Inference
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+
93
+ First install the SetFit library:
94
+
95
+ ```bash
96
+ pip install setfit
97
+ ```
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+
99
+ Then you can load this model and run inference.
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+
101
+ ```python
102
+ 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_top_bt7")
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+ # Run inference
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+ preds = model("MAC 러스터글래스 립스틱 오 구디 (#M)화장품/향수>색조메이크업>립스틱 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 립스틱")
108
+ ```
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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 | 10 | 23.2382 | 81 |
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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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+
155
+ ### 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
168
+ - 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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+
174
+ ### Training Results
175
+ | Epoch | Step | Training Loss | Validation Loss |
176
+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0012 | 1 | 0.4386 | - |
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+ | 0.0581 | 50 | 0.4443 | - |
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+ | 0.1163 | 100 | 0.4215 | - |
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+ | 0.1744 | 150 | 0.3998 | - |
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+ | 0.2326 | 200 | 0.3843 | - |
182
+ | 0.2907 | 250 | 0.3551 | - |
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+ | 0.3488 | 300 | 0.3063 | - |
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+ | 0.4070 | 350 | 0.2439 | - |
185
+ | 0.4651 | 400 | 0.1803 | - |
186
+ | 0.5233 | 450 | 0.1286 | - |
187
+ | 0.5814 | 500 | 0.0885 | - |
188
+ | 0.6395 | 550 | 0.0653 | - |
189
+ | 0.6977 | 600 | 0.0515 | - |
190
+ | 0.7558 | 650 | 0.034 | - |
191
+ | 0.8140 | 700 | 0.0305 | - |
192
+ | 0.8721 | 750 | 0.0291 | - |
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+ | 0.9302 | 800 | 0.028 | - |
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+ | 0.9884 | 850 | 0.028 | - |
195
+ | 1.0465 | 900 | 0.0249 | - |
196
+ | 1.1047 | 950 | 0.0242 | - |
197
+ | 1.1628 | 1000 | 0.0244 | - |
198
+ | 1.2209 | 1050 | 0.0173 | - |
199
+ | 1.2791 | 1100 | 0.0099 | - |
200
+ | 1.3372 | 1150 | 0.0063 | - |
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+ | 1.3953 | 1200 | 0.0038 | - |
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+ | 1.4535 | 1250 | 0.0009 | - |
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+ | 1.5116 | 1300 | 0.0005 | - |
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+ | 1.5698 | 1350 | 0.0004 | - |
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+ | 1.6279 | 1400 | 0.0004 | - |
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+ | 1.6860 | 1450 | 0.0003 | - |
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+ | 1.7442 | 1500 | 0.0003 | - |
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+ | 1.8023 | 1550 | 0.0004 | - |
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+ | 1.8605 | 1600 | 0.0005 | - |
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+ | 1.9186 | 1650 | 0.0003 | - |
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+ | 1.9767 | 1700 | 0.0002 | - |
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+ | 2.0349 | 1750 | 0.0001 | - |
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+ | 2.0930 | 1800 | 0.0001 | - |
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+ | 2.1512 | 1850 | 0.0001 | - |
215
+ | 2.2093 | 1900 | 0.0001 | - |
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+ | 2.2674 | 1950 | 0.0001 | - |
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+ | 2.3256 | 2000 | 0.0001 | - |
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+ | 2.3837 | 2050 | 0.0001 | - |
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+ | 2.4419 | 2100 | 0.0001 | - |
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+ | 2.5 | 2150 | 0.0001 | - |
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+ | 2.5581 | 2200 | 0.0001 | - |
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+ | 2.6163 | 2250 | 0.0001 | - |
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+ | 2.6744 | 2300 | 0.0001 | - |
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+ | 2.7326 | 2350 | 0.0007 | - |
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+ | 2.7907 | 2400 | 0.0028 | - |
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+ | 2.8488 | 2450 | 0.0016 | - |
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+ | 2.9070 | 2500 | 0.0015 | - |
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+ | 2.9651 | 2550 | 0.0026 | - |
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+ | 3.0233 | 2600 | 0.0001 | - |
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+ | 3.0814 | 2650 | 0.0001 | - |
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+ | 3.1395 | 2700 | 0.0001 | - |
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+ | 3.1977 | 2750 | 0.0 | - |
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+ | 3.2558 | 2800 | 0.0002 | - |
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+ | 3.3140 | 2850 | 0.0003 | - |
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+ | 3.3721 | 2900 | 0.0 | - |
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+ | 3.4302 | 2950 | 0.0 | - |
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+ | 3.4884 | 3000 | 0.0 | - |
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+ | 3.5465 | 3050 | 0.0 | - |
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+ | 3.6047 | 3100 | 0.0 | - |
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+ | 3.6628 | 3150 | 0.0 | - |
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+ | 3.7209 | 3200 | 0.0 | - |
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+ | 3.7791 | 3250 | 0.0 | - |
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+ | 3.8372 | 3300 | 0.0 | - |
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+ | 3.8953 | 3350 | 0.0 | - |
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+ | 3.9535 | 3400 | 0.0 | - |
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+ | 4.0116 | 3450 | 0.0 | - |
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+ | 4.0698 | 3500 | 0.0 | - |
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+ | 4.1279 | 3550 | 0.0 | - |
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+ | 4.1860 | 3600 | 0.0 | - |
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+ | 4.2442 | 3650 | 0.0 | - |
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+ | 4.3023 | 3700 | 0.0 | - |
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+ | 4.3605 | 3750 | 0.0 | - |
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+ | 4.4186 | 3800 | 0.0 | - |
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+ | 4.4767 | 3850 | 0.0 | - |
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+ | 4.5349 | 3900 | 0.0 | - |
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+ | 4.5930 | 3950 | 0.0 | - |
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+ | 4.6512 | 4000 | 0.0 | - |
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+ | 4.7093 | 4050 | 0.0 | - |
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+ | 4.7674 | 4100 | 0.0 | - |
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+ | 4.8256 | 4150 | 0.0 | - |
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+ | 4.8837 | 4200 | 0.0 | - |
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+ | 4.9419 | 4250 | 0.0 | - |
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+ | 5.0 | 4300 | 0.0 | - |
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+ | 5.0581 | 4350 | 0.0 | - |
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+ | 5.1163 | 4400 | 0.0 | - |
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+ | 5.1744 | 4450 | 0.0 | - |
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+ | 5.2326 | 4500 | 0.0 | - |
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+ | 5.2907 | 4550 | 0.0 | - |
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+ | 5.3488 | 4600 | 0.0 | - |
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+ | 5.4070 | 4650 | 0.0 | - |
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+ | 5.4651 | 4700 | 0.0 | - |
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+ | 5.5233 | 4750 | 0.0 | - |
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+ | 5.5814 | 4800 | 0.0 | - |
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+ | 5.6395 | 4850 | 0.0 | - |
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+ | 5.6977 | 4900 | 0.0 | - |
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+ | 5.7558 | 4950 | 0.0 | - |
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+ | 5.8140 | 5000 | 0.0 | - |
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+ | 5.8721 | 5050 | 0.0 | - |
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+ | 5.9302 | 5100 | 0.0 | - |
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+ | 5.9884 | 5150 | 0.0 | - |
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+ | 6.0465 | 5200 | 0.0 | - |
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+ | 6.1047 | 5250 | 0.0 | - |
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+ | 6.1628 | 5300 | 0.0 | - |
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+ | 6.2209 | 5350 | 0.0 | - |
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+ | 6.2791 | 5400 | 0.0 | - |
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+ | 6.3372 | 5450 | 0.0 | - |
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+ | 6.3953 | 5500 | 0.0 | - |
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+ | 6.4535 | 5550 | 0.0 | - |
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+ | 6.5116 | 5600 | 0.0 | - |
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+ | 6.5698 | 5650 | 0.0 | - |
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+ | 6.6279 | 5700 | 0.0 | - |
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+ | 6.6860 | 5750 | 0.0 | - |
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+ | 6.7442 | 5800 | 0.0 | - |
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+ | 6.8023 | 5850 | 0.0 | - |
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+ | 6.8605 | 5900 | 0.0 | - |
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+ | 6.9186 | 5950 | 0.0 | - |
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+ | 6.9767 | 6000 | 0.0 | - |
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+ | 7.0349 | 6050 | 0.0 | - |
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+ | 7.0930 | 6100 | 0.0 | - |
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+ | 7.1512 | 6150 | 0.0 | - |
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+ | 7.2093 | 6200 | 0.0 | - |
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+ | 7.2674 | 6250 | 0.0 | - |
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+ | 7.3256 | 6300 | 0.0 | - |
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+ | 7.3837 | 6350 | 0.0 | - |
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+ | 7.4419 | 6400 | 0.0 | - |
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+ | 7.5 | 6450 | 0.0 | - |
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+ | 7.5581 | 6500 | 0.0 | - |
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+ | 7.6163 | 6550 | 0.0 | - |
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+ | 7.6744 | 6600 | 0.0 | - |
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+ | 7.7326 | 6650 | 0.0 | - |
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+ | 7.7907 | 6700 | 0.0 | - |
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+ | 7.8488 | 6750 | 0.0 | - |
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+ | 7.9070 | 6800 | 0.0 | - |
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+ | 7.9651 | 6850 | 0.0 | - |
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+ | 8.0233 | 6900 | 0.0 | - |
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+ | 8.0814 | 6950 | 0.0 | - |
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+ | 8.1977 | 7050 | 0.0 | - |
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+ | 8.3140 | 7150 | 0.0 | - |
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+ | 8.3721 | 7200 | 0.0 | - |
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+ | 8.4302 | 7250 | 0.0 | - |
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+ | 8.5465 | 7350 | 0.0 | - |
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+ | 8.6047 | 7400 | 0.0 | - |
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+ | 8.6628 | 7450 | 0.0 | - |
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+ | 8.7209 | 7500 | 0.0 | - |
328
+ | 8.7791 | 7550 | 0.0 | - |
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+ | 8.8372 | 7600 | 0.0 | - |
330
+ | 8.8953 | 7650 | 0.0 | - |
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+ | 8.9535 | 7700 | 0.0 | - |
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+ | 9.0116 | 7750 | 0.0 | - |
333
+ | 9.0698 | 7800 | 0.0 | - |
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+ | 9.1279 | 7850 | 0.0 | - |
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+ | 9.1860 | 7900 | 0.0 | - |
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+ | 9.2442 | 7950 | 0.0 | - |
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+ | 9.3023 | 8000 | 0.0 | - |
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+ | 9.3605 | 8050 | 0.0 | - |
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+ | 9.4186 | 8100 | 0.0 | - |
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+ | 9.4767 | 8150 | 0.0 | - |
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+ | 9.5349 | 8200 | 0.0 | - |
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+ | 9.5930 | 8250 | 0.0 | - |
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+ | 9.6512 | 8300 | 0.0 | - |
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+ | 9.7093 | 8350 | 0.0 | - |
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+ | 9.7674 | 8400 | 0.0 | - |
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+ | 9.8256 | 8450 | 0.0 | - |
347
+ | 9.8837 | 8500 | 0.0 | - |
348
+ | 9.9419 | 8550 | 0.0 | - |
349
+ | 10.0 | 8600 | 0.0 | - |
350
+ | 10.0581 | 8650 | 0.0 | - |
351
+ | 10.1163 | 8700 | 0.0 | - |
352
+ | 10.1744 | 8750 | 0.0 | - |
353
+ | 10.2326 | 8800 | 0.0 | - |
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+ | 10.2907 | 8850 | 0.0 | - |
355
+ | 10.3488 | 8900 | 0.0 | - |
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+ | 10.4070 | 8950 | 0.0 | - |
357
+ | 10.4651 | 9000 | 0.0 | - |
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+ | 10.5233 | 9050 | 0.0 | - |
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+ | 10.5814 | 9100 | 0.0 | - |
360
+ | 10.6395 | 9150 | 0.0 | - |
361
+ | 10.6977 | 9200 | 0.0241 | - |
362
+ | 10.7558 | 9250 | 0.0134 | - |
363
+ | 10.8140 | 9300 | 0.0109 | - |
364
+ | 10.8721 | 9350 | 0.0096 | - |
365
+ | 10.9302 | 9400 | 0.009 | - |
366
+ | 10.9884 | 9450 | 0.0083 | - |
367
+ | 11.0465 | 9500 | 0.0115 | - |
368
+ | 11.1047 | 9550 | 0.0086 | - |
369
+ | 11.1628 | 9600 | 0.0043 | - |
370
+ | 11.2209 | 9650 | 0.0002 | - |
371
+ | 11.2791 | 9700 | 0.0005 | - |
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+ | 11.3372 | 9750 | 0.0001 | - |
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+ | 11.3953 | 9800 | 0.0 | - |
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+ | 11.4535 | 9850 | 0.0 | - |
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+ | 11.5116 | 9900 | 0.0 | - |
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+ | 11.5698 | 9950 | 0.0 | - |
377
+ | 11.6279 | 10000 | 0.0 | - |
378
+ | 11.6860 | 10050 | 0.0 | - |
379
+ | 11.7442 | 10100 | 0.0 | - |
380
+ | 11.8023 | 10150 | 0.0 | - |
381
+ | 11.8605 | 10200 | 0.0 | - |
382
+ | 11.9186 | 10250 | 0.0 | - |
383
+ | 11.9767 | 10300 | 0.0 | - |
384
+ | 12.0349 | 10350 | 0.0 | - |
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+ | 12.0930 | 10400 | 0.0 | - |
386
+ | 12.1512 | 10450 | 0.0 | - |
387
+ | 12.2093 | 10500 | 0.0 | - |
388
+ | 12.2674 | 10550 | 0.0 | - |
389
+ | 12.3256 | 10600 | 0.0 | - |
390
+ | 12.3837 | 10650 | 0.0 | - |
391
+ | 12.4419 | 10700 | 0.0 | - |
392
+ | 12.5 | 10750 | 0.0 | - |
393
+ | 12.5581 | 10800 | 0.0 | - |
394
+ | 12.6163 | 10850 | 0.0 | - |
395
+ | 12.6744 | 10900 | 0.0 | - |
396
+ | 12.7326 | 10950 | 0.0 | - |
397
+ | 12.7907 | 11000 | 0.0 | - |
398
+ | 12.8488 | 11050 | 0.0 | - |
399
+ | 12.9070 | 11100 | 0.0 | - |
400
+ | 12.9651 | 11150 | 0.0 | - |
401
+ | 13.0233 | 11200 | 0.0 | - |
402
+ | 13.0814 | 11250 | 0.0 | - |
403
+ | 13.1395 | 11300 | 0.0 | - |
404
+ | 13.1977 | 11350 | 0.0 | - |
405
+ | 13.2558 | 11400 | 0.0 | - |
406
+ | 13.3140 | 11450 | 0.0 | - |
407
+ | 13.3721 | 11500 | 0.0 | - |
408
+ | 13.4302 | 11550 | 0.0 | - |
409
+ | 13.4884 | 11600 | 0.0 | - |
410
+ | 13.5465 | 11650 | 0.0 | - |
411
+ | 13.6047 | 11700 | 0.0 | - |
412
+ | 13.6628 | 11750 | 0.0 | - |
413
+ | 13.7209 | 11800 | 0.0 | - |
414
+ | 13.7791 | 11850 | 0.0 | - |
415
+ | 13.8372 | 11900 | 0.0 | - |
416
+ | 13.8953 | 11950 | 0.0 | - |
417
+ | 13.9535 | 12000 | 0.0 | - |
418
+ | 14.0116 | 12050 | 0.0 | - |
419
+ | 14.0698 | 12100 | 0.0 | - |
420
+ | 14.1279 | 12150 | 0.0 | - |
421
+ | 14.1860 | 12200 | 0.0 | - |
422
+ | 14.2442 | 12250 | 0.0 | - |
423
+ | 14.3023 | 12300 | 0.0 | - |
424
+ | 14.3605 | 12350 | 0.0 | - |
425
+ | 14.4186 | 12400 | 0.0 | - |
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+ | 14.4767 | 12450 | 0.0 | - |
427
+ | 14.5349 | 12500 | 0.0 | - |
428
+ | 14.5930 | 12550 | 0.0 | - |
429
+ | 14.6512 | 12600 | 0.0 | - |
430
+ | 14.7093 | 12650 | 0.0 | - |
431
+ | 14.7674 | 12700 | 0.0 | - |
432
+ | 14.8256 | 12750 | 0.0 | - |
433
+ | 14.8837 | 12800 | 0.0 | - |
434
+ | 14.9419 | 12850 | 0.0 | - |
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+ | 15.0 | 12900 | 0.0 | - |
436
+ | 15.0581 | 12950 | 0.0 | - |
437
+ | 15.1163 | 13000 | 0.0 | - |
438
+ | 15.1744 | 13050 | 0.0 | - |
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+
695
+ ### Framework Versions
696
+ - Python: 3.10.12
697
+ - SetFit: 1.1.0
698
+ - Sentence Transformers: 3.3.1
699
+ - Transformers: 4.44.2
700
+ - PyTorch: 2.2.0a0+81ea7a4
701
+ - Datasets: 3.2.0
702
+ - Tokenizers: 0.19.1
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+
704
+ ## Citation
705
+
706
+ ### BibTeX
707
+ ```bibtex
708
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
709
+ doi = {10.48550/ARXIV.2209.11055},
710
+ url = {https://arxiv.org/abs/2209.11055},
711
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
712
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
713
+ title = {Efficient Few-Shot Learning Without Prompts},
714
+ publisher = {arXiv},
715
+ year = {2022},
716
+ copyright = {Creative Commons Attribution 4.0 International}
717
+ }
718
+ ```
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+
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+ <!--
721
+ ## Glossary
722
+
723
+ *Clearly define terms in order to be accessible across audiences.*
724
+ -->
725
+
726
+ <!--
727
+ ## Model Card Authors
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+
729
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
731
+
732
+ <!--
733
+ ## Model Card Contact
734
+
735
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
736
+ -->
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+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "[UNK]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "[CLS]",
45
+ "clean_up_tokenization_spaces": false,
46
+ "cls_token": "[CLS]",
47
+ "do_basic_tokenize": true,
48
+ "do_lower_case": false,
49
+ "eos_token": "[SEP]",
50
+ "mask_token": "[MASK]",
51
+ "max_length": 512,
52
+ "model_max_length": 512,
53
+ "never_split": null,
54
+ "pad_to_multiple_of": null,
55
+ "pad_token": "[PAD]",
56
+ "pad_token_type_id": 0,
57
+ "padding_side": "right",
58
+ "sep_token": "[SEP]",
59
+ "stride": 0,
60
+ "strip_accents": null,
61
+ "tokenize_chinese_chars": true,
62
+ "tokenizer_class": "BertTokenizer",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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