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mini1013
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master_cate_bt0_test_flat_top_cate

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 +705 -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: AK몰_[랩시리즈][11][남자에센스] 데일리 레스큐 리페어 세럼 기획 세트 (+탄력로션 14ml 증정) 단일상품 (#M)위메프 >
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+ 뷰티 > 명품화장품 > 남성화장품 > 남성화장품 위메프 > 뷰티 > 명품화장품 > 남성화장품 > 남성화장품
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+ - text: 이니스프리 그린티 로션 포맨 143159 150ml x 1개 (#M)홈>화장품/미용>남성화장품>스킨 Naverstore > 화장품/미용
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+ > 남성화장품 > 스킨
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+ - text: 맨 리차징 로션 150ml LotteOn > 뷰티 > 남성화장품 > 로션 LotteOn > 뷰티 > 남성화장품 > 로션
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+ - text: 헤라 옴므 에센스 인 에멀전 110ml LotteOn > 뷰티 > 스킨케어 > 로션/에멀젼 LotteOn > 뷰티 > 스킨케어 >
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+ 로션/에멀젼
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+ - text: 이니스프리 그린티 스킨 포맨 150ml × 14개 (#M)쿠팡 홈>뷰티>남성화장품>남성스킨케어>스킨/로션/크림 Coupang > 뷰티
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+ > 로드샵 > 남성화장품 > 남성스킨케어 > 스킨/로션/크림
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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: 0.8309572301425662
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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:** 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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+ | 3 | <ul><li>'[산타마리아노벨라]로지오네 도포 바르바 - 콜로니아 루사 100ml 화이트_Free (#M)화장품/미용>남성화장품>스킨 AD > Naverstore > smnovella브랜드스토어 > 전체상품'</li><li>'오딧세이 미니어처 스킨 에멀전 25ml x 10개 여행용 MinSellAmount (#M)화장품/향수>스킨케어>스킨/토너 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 스킨/토너'</li><li>'비오템 옴므 아쿠아파워 토너 200ml × 2개 (#M)쿠팡 홈>뷰티>남성화장품>남성스킨케어>스킨/로션/크림 Coupang > 뷰티 > 명품뷰티 > 남성화장품 > 남성스킨케어 > 스킨/로션/크림'</li></ul> |
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+ | 5 | <ul><li>'포스 수프림 메탈 아이 세럼 15ml/비오템 홈>화장품/미용>남성화장품>에센스;(#M)홈>화장품/미용>남성화장품>크림 Naverstore > 화장품/미용 > 남성화장품 > 크림'</li><li>'설화수 본윤에센스 140ml (#M)11st>스킨케어>로션/에멀션>로션/에멀션 11st > 뷰티 > 스킨케어 > 로션/에멀션 > 로션/에멀션'</li><li>'비오템 아쿠아파워 클리어에센스 100ml (#M)홈>화장품/미용>���성화장품>에센스 Naverstore > 화장품/미용 > 남성화장품 > 에센스'</li></ul> |
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+ | 1 | <ul><li>'[나인위시스] VB포맨 톤업크림 SPF21 50ml LotteOn > 뷰티 > 남성화장품 > 메이크업 LotteOn > 뷰티 > 남성화장품 > 메이크업'</li><li>'비레디 레벨업 파운데이션 포 히어로즈 SPF50+ PA++++ 30ml 3호 제프리 (#M)홈>화장품/미용>남성화장품>메이크업 Naverstore > 화장품/미용 > 남성화장품 > 메이크업'</li><li>'맨즈 프라이머 비비 세트 - 도자기피부 MinSellAmount 화장품/향수>남성화장품>남성BB크림;(#M)화장품/향수>남성화장품>남성메이크업/BB Gmarket > 뷰티 > 화장품/향수 > 남성화장품 > 남성메이크업/BB'</li></ul> |
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+ | 8 | <ul><li>'랩시리즈 그루밍 쿨링 쉐이브 크림 190ml (#M)화장품/미용>남성화장품>쉐이빙폼 Naverstore > 화장품/미용 > 남성화장품 > 쉐이빙폼'</li><li>'랩시리즈 안티에이지 맥스 LS 크림 50ml (#M)위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 남성스킨 위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 남성스킨'</li><li>'랩시리즈 안티에이지 맥스 LS 크림 50ml (#M)화장품/미용>남성화장품>에센스 Naverstore > 화장품/미용 > 남성화장품 > 에센스'</li></ul> |
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+ | 2 | <ul><li>'브로앤팁스 수퍼 라이트 선크림 SPR50+ PA++++ 70ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션 Coupang > 뷰티 > 남성화장품 > 남성스킨케어 > 선케어'</li><li>'MEN 릴랙싱 UV 프로펙터 SPF50+/PA+++ 릴랙싱UV프로텍터 LotteOn > 뷰티 > 명품화장품 > 남성화장품 LotteOn > 뷰티 > 명품화장품 > 남성화장품 > 선케어'</li><li>'유브이 디펜스 선 베이스 프레쉬 50ml(SPF50+) (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림'</li></ul> |
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+ | 10 | <ul><li>'세붐 스트라이크 맨테라피 마스크 MinSellAmount (#M)화장품/향수>팩/마스크>마스크시트 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 마스크시트'</li><li>'[퓨어덤] 릴랙스 하이드라 남성용 마스크팩 50매 릴랙스 하이드라 남성용 마스크팩 50매 (#M)쿠팡 홈>뷰티>남성화장품>남성스킨케어>마스크/팩 Coupang > 뷰티 > 남성화장품 > 남성스킨케어 > 마스크/팩'</li><li>'크로마티크 마스크 200ml+샘플2종/염색모전용 MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트'</li></ul> |
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+ | 7 | <ul><li>'쿤달 퓨어 앤 세이프 쿨링 남성청결제 2구 세트 300ml LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 청결제 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 청결제'</li><li>'바버501 이너부스터 남성청결제 263ml 2타입 진저민트 단품 홈>전체상품;홈>화장품/미용>남성화장품>남성청결제;홈>클렌징;(#M)홈>BEST Naverstore > 화장품/미용 > 남성화장품 > 남성청결제'</li><li>'프리메라 후리 엔 후리 맨 에너자이징 포인트 클렌저 200ml 남성 청결제 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 청결제 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 청결제'</li></ul> |
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+ | 4 | <ul><li>'수려한 효비담 정율 2종 기획세트 {SR3685} (#M)위메프 > 뷰티 > 스킨케어 > 스킨케어 세트 > 2종/3종 세트 위메프 > 뷰티 > 스킨케어 > 스킨케어 세트'</li><li>'수려한 건양 2종 기획세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li><li>'공진향 군 자양 2종 기획세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li></ul> |
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+ | 0 | <ul><li>'오딧세이 미니 에멀전 25ml 10개 로션 미니어쳐 여행 오딧세이 미니로션/10개 (#M)11st>남성화장품>남성로션>남성로션 11st > 뷰티 > 남성화장품 > 남성로션'</li><li>'더 후 공진향 군 자양 로션 100ml (#M)화장품/미용>남성화장품>로션 Naverstore > 화장품/미용 > 남성화장품 > 로션'</li><li>'키엘 훼이셜 퓨얼 에너자이징 모이스처 트리트먼트 포 맨 125ml 홈>전체상품;(#M)홈>키엘 Naverstore > 화장품/미용 > 남성화장품 > 로션'</li></ul> |
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+ | 9 | <ul><li>'질레트 프로글라이드 2in1젤 퓨어스포츠 4개 질레트 프로글라이드 2in1젤 퓨어스포츠 4개[G010*2] ssg > 뷰티 > 헤어/바디 > 면도/제모용품 ssg > 뷰티 > 헤어/바디 > 면도/제모용품'</li><li>'[질레트]질레트 포오미 멘솔 175g 2개 질레트 포오미 멘솔 175g 2개[G20*2] LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 목욕비누 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 목욕비누'</li><li>'[백화점][비오템] 티쀼르 씨솔트 스크럽 125ml (#M)GSSHOP>뷰티>명품화장품>현대백화점 GSSHOP > 뷰티 > 명품화장품 > 현대백화점 > 스킨케어'</li></ul> |
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+ | 6 | <ul><li>'(15%+15%)한스킨 클렌징대장 전품목 클리어런스 세일 11.한스킨 인기 마스크 30매_콜라겐(B0004441) 화장품/향수>메디컬/드럭스토어>스킨/로션/미스트;(#M)화장품/향수>스킨케어>스킨/토너 Gmarket > 뷰티 > 화장품/향수 > 스킨케어'</li><li>'(SF)라네즈 크림스킨 옴므 올인원 150ml + 추가 증정 (#M)위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 올인원 위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 올인원'</li><li>'랩 시리즈 프로 LS 올-인-원 훼이스 트리트먼트 50ml 없음 (#M)홈>스킨케어>에센스/앰플 HMALL > 뷰티 > 스킨케어 > 에센스/앰플'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.8310 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_cate_bt0_test_flat_top_cate")
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+ # Run inference
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+ preds = model("맨 리차징 로션 150ml LotteOn > 뷰티 > 남성화장품 > 로션 LotteOn > 뷰티 > 남성화장품 > 로션")
107
+ ```
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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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+
135
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 11 | 20.6699 | 63 |
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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 | 18 |
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+
154
+ ### Training Hyperparameters
155
+ - 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
166
+ - use_amp: False
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+ - warmup_proportion: 0.1
168
+ - 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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+
173
+ ### Training Results
174
+ | Epoch | Step | Training Loss | Validation Loss |
175
+ |:-------:|:-----:|:-------------:|:---------------:|
176
+ | 0.0012 | 1 | 0.3982 | - |
177
+ | 0.0617 | 50 | 0.4478 | - |
178
+ | 0.1235 | 100 | 0.433 | - |
179
+ | 0.1852 | 150 | 0.402 | - |
180
+ | 0.2469 | 200 | 0.3982 | - |
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+ | 0.3086 | 250 | 0.3669 | - |
182
+ | 0.3704 | 300 | 0.3331 | - |
183
+ | 0.4321 | 350 | 0.3142 | - |
184
+ | 0.4938 | 400 | 0.2879 | - |
185
+ | 0.5556 | 450 | 0.2728 | - |
186
+ | 0.6173 | 500 | 0.2562 | - |
187
+ | 0.6790 | 550 | 0.2449 | - |
188
+ | 0.7407 | 600 | 0.2335 | - |
189
+ | 0.8025 | 650 | 0.2113 | - |
190
+ | 0.8642 | 700 | 0.1952 | - |
191
+ | 0.9259 | 750 | 0.1881 | - |
192
+ | 0.9877 | 800 | 0.1775 | - |
193
+ | 1.0494 | 850 | 0.1609 | - |
194
+ | 1.1111 | 900 | 0.1559 | - |
195
+ | 1.1728 | 950 | 0.1385 | - |
196
+ | 1.2346 | 1000 | 0.1268 | - |
197
+ | 1.2963 | 1050 | 0.1115 | - |
198
+ | 1.3580 | 1100 | 0.1059 | - |
199
+ | 1.4198 | 1150 | 0.0861 | - |
200
+ | 1.4815 | 1200 | 0.0776 | - |
201
+ | 1.5432 | 1250 | 0.0676 | - |
202
+ | 1.6049 | 1300 | 0.0565 | - |
203
+ | 1.6667 | 1350 | 0.0511 | - |
204
+ | 1.7284 | 1400 | 0.0442 | - |
205
+ | 1.7901 | 1450 | 0.037 | - |
206
+ | 1.8519 | 1500 | 0.0375 | - |
207
+ | 1.9136 | 1550 | 0.0319 | - |
208
+ | 1.9753 | 1600 | 0.0272 | - |
209
+ | 2.0370 | 1650 | 0.0213 | - |
210
+ | 2.0988 | 1700 | 0.0173 | - |
211
+ | 2.1605 | 1750 | 0.0191 | - |
212
+ | 2.2222 | 1800 | 0.0152 | - |
213
+ | 2.2840 | 1850 | 0.0194 | - |
214
+ | 2.3457 | 1900 | 0.0152 | - |
215
+ | 2.4074 | 1950 | 0.0173 | - |
216
+ | 2.4691 | 2000 | 0.0123 | - |
217
+ | 2.5309 | 2050 | 0.0083 | - |
218
+ | 2.5926 | 2100 | 0.007 | - |
219
+ | 2.6543 | 2150 | 0.0066 | - |
220
+ | 2.7160 | 2200 | 0.0077 | - |
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+ | 2.7778 | 2250 | 0.0066 | - |
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+ | 2.8395 | 2300 | 0.0052 | - |
223
+ | 2.9012 | 2350 | 0.0055 | - |
224
+ | 2.9630 | 2400 | 0.0043 | - |
225
+ | 3.0247 | 2450 | 0.0032 | - |
226
+ | 3.0864 | 2500 | 0.0028 | - |
227
+ | 3.1481 | 2550 | 0.004 | - |
228
+ | 3.2099 | 2600 | 0.0039 | - |
229
+ | 3.2716 | 2650 | 0.0052 | - |
230
+ | 3.3333 | 2700 | 0.0056 | - |
231
+ | 3.3951 | 2750 | 0.0064 | - |
232
+ | 3.4568 | 2800 | 0.0055 | - |
233
+ | 3.5185 | 2850 | 0.0051 | - |
234
+ | 3.5802 | 2900 | 0.0041 | - |
235
+ | 3.6420 | 2950 | 0.0039 | - |
236
+ | 3.7037 | 3000 | 0.0045 | - |
237
+ | 3.7654 | 3050 | 0.0062 | - |
238
+ | 3.8272 | 3100 | 0.0036 | - |
239
+ | 3.8889 | 3150 | 0.0039 | - |
240
+ | 3.9506 | 3200 | 0.0035 | - |
241
+ | 4.0123 | 3250 | 0.0045 | - |
242
+ | 4.0741 | 3300 | 0.0033 | - |
243
+ | 4.1358 | 3350 | 0.0048 | - |
244
+ | 4.1975 | 3400 | 0.0036 | - |
245
+ | 4.2593 | 3450 | 0.0038 | - |
246
+ | 4.3210 | 3500 | 0.0045 | - |
247
+ | 4.3827 | 3550 | 0.0058 | - |
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+ | 4.4444 | 3600 | 0.0053 | - |
249
+ | 4.5062 | 3650 | 0.0073 | - |
250
+ | 4.5679 | 3700 | 0.0105 | - |
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+ | 4.6296 | 3750 | 0.0071 | - |
252
+ | 4.6914 | 3800 | 0.0045 | - |
253
+ | 4.7531 | 3850 | 0.004 | - |
254
+ | 4.8148 | 3900 | 0.0034 | - |
255
+ | 4.8765 | 3950 | 0.0052 | - |
256
+ | 4.9383 | 4000 | 0.0046 | - |
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+ | 5.0 | 4050 | 0.0035 | - |
258
+ | 5.0617 | 4100 | 0.003 | - |
259
+ | 5.1235 | 4150 | 0.0036 | - |
260
+ | 5.1852 | 4200 | 0.0034 | - |
261
+ | 5.2469 | 4250 | 0.0041 | - |
262
+ | 5.3086 | 4300 | 0.0039 | - |
263
+ | 5.3704 | 4350 | 0.0033 | - |
264
+ | 5.4321 | 4400 | 0.0028 | - |
265
+ | 5.4938 | 4450 | 0.0031 | - |
266
+ | 5.5556 | 4500 | 0.0033 | - |
267
+ | 5.6173 | 4550 | 0.0043 | - |
268
+ | 5.6790 | 4600 | 0.0052 | - |
269
+ | 5.7407 | 4650 | 0.004 | - |
270
+ | 5.8025 | 4700 | 0.0036 | - |
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+ | 5.8642 | 4750 | 0.0051 | - |
272
+ | 5.9259 | 4800 | 0.0047 | - |
273
+ | 5.9877 | 4850 | 0.0056 | - |
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+ | 6.0494 | 4900 | 0.0041 | - |
275
+ | 6.1111 | 4950 | 0.0036 | - |
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+ | 6.1728 | 5000 | 0.0049 | - |
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+ | 6.2346 | 5050 | 0.004 | - |
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+ | 6.2963 | 5100 | 0.0035 | - |
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+ | 6.3580 | 5150 | 0.0041 | - |
280
+ | 6.4198 | 5200 | 0.0025 | - |
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+ | 6.4815 | 5250 | 0.0027 | - |
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+ | 6.5432 | 5300 | 0.0042 | - |
283
+ | 6.6049 | 5350 | 0.0036 | - |
284
+ | 6.6667 | 5400 | 0.0041 | - |
285
+ | 6.7284 | 5450 | 0.0036 | - |
286
+ | 6.7901 | 5500 | 0.0044 | - |
287
+ | 6.8519 | 5550 | 0.0034 | - |
288
+ | 6.9136 | 5600 | 0.0041 | - |
289
+ | 6.9753 | 5650 | 0.0036 | - |
290
+ | 7.0370 | 5700 | 0.0034 | - |
291
+ | 7.0988 | 5750 | 0.0034 | - |
292
+ | 7.1605 | 5800 | 0.0039 | - |
293
+ | 7.2222 | 5850 | 0.0036 | - |
294
+ | 7.2840 | 5900 | 0.0041 | - |
295
+ | 7.3457 | 5950 | 0.0031 | - |
296
+ | 7.4074 | 6000 | 0.0032 | - |
297
+ | 7.4691 | 6050 | 0.0133 | - |
298
+ | 7.5309 | 6100 | 0.0154 | - |
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+ | 7.5926 | 6150 | 0.01 | - |
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+ | 7.6543 | 6200 | 0.0063 | - |
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+ | 7.7160 | 6250 | 0.0068 | - |
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+ | 7.7778 | 6300 | 0.0077 | - |
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+ | 7.8395 | 6350 | 0.0047 | - |
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+ | 7.9012 | 6400 | 0.0044 | - |
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+ | 7.9630 | 6450 | 0.0062 | - |
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+ | 8.0247 | 6500 | 0.0057 | - |
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+ | 8.0864 | 6550 | 0.0038 | - |
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+ | 8.1481 | 6600 | 0.0046 | - |
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+ | 8.2099 | 6650 | 0.0041 | - |
310
+ | 8.2716 | 6700 | 0.0031 | - |
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+ | 8.3333 | 6750 | 0.0033 | - |
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+ | 8.3951 | 6800 | 0.0042 | - |
313
+ | 8.4568 | 6850 | 0.0028 | - |
314
+ | 8.5185 | 6900 | 0.0038 | - |
315
+ | 8.5802 | 6950 | 0.0028 | - |
316
+ | 8.6420 | 7000 | 0.0042 | - |
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+ | 8.7037 | 7050 | 0.0034 | - |
318
+ | 8.7654 | 7100 | 0.005 | - |
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+ | 8.8272 | 7150 | 0.0034 | - |
320
+ | 8.8889 | 7200 | 0.0038 | - |
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+ | 8.9506 | 7250 | 0.003 | - |
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+ | 9.0123 | 7300 | 0.0031 | - |
323
+ | 9.0741 | 7350 | 0.0025 | - |
324
+ | 9.1358 | 7400 | 0.0042 | - |
325
+ | 9.1975 | 7450 | 0.0034 | - |
326
+ | 9.2593 | 7500 | 0.0053 | - |
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+ | 9.3210 | 7550 | 0.0041 | - |
328
+ | 9.3827 | 7600 | 0.0041 | - |
329
+ | 9.4444 | 7650 | 0.0045 | - |
330
+ | 9.5062 | 7700 | 0.0027 | - |
331
+ | 9.5679 | 7750 | 0.0044 | - |
332
+ | 9.6296 | 7800 | 0.0047 | - |
333
+ | 9.6914 | 7850 | 0.0028 | - |
334
+ | 9.7531 | 7900 | 0.0027 | - |
335
+ | 9.8148 | 7950 | 0.0025 | - |
336
+ | 9.8765 | 8000 | 0.0036 | - |
337
+ | 9.9383 | 8050 | 0.0033 | - |
338
+ | 10.0 | 8100 | 0.0028 | - |
339
+ | 10.0617 | 8150 | 0.0047 | - |
340
+ | 10.1235 | 8200 | 0.0043 | - |
341
+ | 10.1852 | 8250 | 0.0042 | - |
342
+ | 10.2469 | 8300 | 0.0057 | - |
343
+ | 10.3086 | 8350 | 0.0049 | - |
344
+ | 10.3704 | 8400 | 0.0042 | - |
345
+ | 10.4321 | 8450 | 0.0056 | - |
346
+ | 10.4938 | 8500 | 0.0072 | - |
347
+ | 10.5556 | 8550 | 0.0039 | - |
348
+ | 10.6173 | 8600 | 0.0056 | - |
349
+ | 10.6790 | 8650 | 0.0041 | - |
350
+ | 10.7407 | 8700 | 0.0047 | - |
351
+ | 10.8025 | 8750 | 0.0025 | - |
352
+ | 10.8642 | 8800 | 0.0034 | - |
353
+ | 10.9259 | 8850 | 0.0035 | - |
354
+ | 10.9877 | 8900 | 0.0038 | - |
355
+ | 11.0494 | 8950 | 0.0023 | - |
356
+ | 11.1111 | 9000 | 0.0039 | - |
357
+ | 11.1728 | 9050 | 0.0036 | - |
358
+ | 11.2346 | 9100 | 0.003 | - |
359
+ | 11.2963 | 9150 | 0.0034 | - |
360
+ | 11.3580 | 9200 | 0.0042 | - |
361
+ | 11.4198 | 9250 | 0.0033 | - |
362
+ | 11.4815 | 9300 | 0.0034 | - |
363
+ | 11.5432 | 9350 | 0.0036 | - |
364
+ | 11.6049 | 9400 | 0.0027 | - |
365
+ | 11.6667 | 9450 | 0.0036 | - |
366
+ | 11.7284 | 9500 | 0.0051 | - |
367
+ | 11.7901 | 9550 | 0.0048 | - |
368
+ | 11.8519 | 9600 | 0.0038 | - |
369
+ | 11.9136 | 9650 | 0.0037 | - |
370
+ | 11.9753 | 9700 | 0.0026 | - |
371
+ | 12.0370 | 9750 | 0.0035 | - |
372
+ | 12.0988 | 9800 | 0.0019 | - |
373
+ | 12.1605 | 9850 | 0.0 | - |
374
+ | 12.2222 | 9900 | 0.0 | - |
375
+ | 12.2840 | 9950 | 0.0 | - |
376
+ | 12.3457 | 10000 | 0.0 | - |
377
+ | 12.4074 | 10050 | 0.0 | - |
378
+ | 12.4691 | 10100 | 0.0006 | - |
379
+ | 12.5309 | 10150 | 0.0018 | - |
380
+ | 12.5926 | 10200 | 0.0006 | - |
381
+ | 12.6543 | 10250 | 0.0 | - |
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+ | 12.7160 | 10300 | 0.0 | - |
383
+ | 12.7778 | 10350 | 0.0003 | - |
384
+ | 12.8395 | 10400 | 0.0038 | - |
385
+ | 12.9012 | 10450 | 0.0025 | - |
386
+ | 12.9630 | 10500 | 0.0025 | - |
387
+ | 13.0247 | 10550 | 0.0024 | - |
388
+ | 13.0864 | 10600 | 0.0029 | - |
389
+ | 13.1481 | 10650 | 0.0034 | - |
390
+ | 13.2099 | 10700 | 0.0037 | - |
391
+ | 13.2716 | 10750 | 0.0039 | - |
392
+ | 13.3333 | 10800 | 0.0027 | - |
393
+ | 13.3951 | 10850 | 0.0023 | - |
394
+ | 13.4568 | 10900 | 0.0008 | - |
395
+ | 13.5185 | 10950 | 0.0 | - |
396
+ | 13.5802 | 11000 | 0.0 | - |
397
+ | 13.6420 | 11050 | 0.0 | - |
398
+ | 13.7037 | 11100 | 0.0 | - |
399
+ | 13.7654 | 11150 | 0.0 | - |
400
+ | 13.8272 | 11200 | 0.0 | - |
401
+ | 13.8889 | 11250 | 0.0 | - |
402
+ | 13.9506 | 11300 | 0.0 | - |
403
+ | 14.0123 | 11350 | 0.0 | - |
404
+ | 14.0741 | 11400 | 0.0 | - |
405
+ | 14.1358 | 11450 | 0.0 | - |
406
+ | 14.1975 | 11500 | 0.0 | - |
407
+ | 14.2593 | 11550 | 0.0 | - |
408
+ | 14.3210 | 11600 | 0.0 | - |
409
+ | 14.3827 | 11650 | 0.0 | - |
410
+ | 14.4444 | 11700 | 0.0 | - |
411
+ | 14.5062 | 11750 | 0.0 | - |
412
+ | 14.5679 | 11800 | 0.0 | - |
413
+ | 14.6296 | 11850 | 0.0 | - |
414
+ | 14.6914 | 11900 | 0.0 | - |
415
+ | 14.7531 | 11950 | 0.0 | - |
416
+ | 14.8148 | 12000 | 0.0 | - |
417
+ | 14.8765 | 12050 | 0.0 | - |
418
+ | 14.9383 | 12100 | 0.0 | - |
419
+ | 15.0 | 12150 | 0.0 | - |
420
+ | 15.0617 | 12200 | 0.0 | - |
421
+ | 15.1235 | 12250 | 0.0 | - |
422
+ | 15.1852 | 12300 | 0.0 | - |
423
+ | 15.2469 | 12350 | 0.0 | - |
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+ | 15.3086 | 12400 | 0.0 | - |
425
+ | 15.3704 | 12450 | 0.0 | - |
426
+ | 15.4321 | 12500 | 0.0 | - |
427
+ | 15.4938 | 12550 | 0.0 | - |
428
+ | 15.5556 | 12600 | 0.0 | - |
429
+ | 15.6173 | 12650 | 0.0 | - |
430
+ | 15.6790 | 12700 | 0.0 | - |
431
+ | 15.7407 | 12750 | 0.0 | - |
432
+ | 15.8025 | 12800 | 0.0 | - |
433
+ | 15.8642 | 12850 | 0.0 | - |
434
+ | 15.9259 | 12900 | 0.0 | - |
435
+ | 15.9877 | 12950 | 0.0 | - |
436
+ | 16.0494 | 13000 | 0.0 | - |
437
+ | 16.1111 | 13050 | 0.0 | - |
438
+ | 16.1728 | 13100 | 0.0 | - |
439
+ | 16.2346 | 13150 | 0.0 | - |
440
+ | 16.2963 | 13200 | 0.0 | - |
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+ | 16.3580 | 13250 | 0.0 | - |
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+ | 16.4198 | 13300 | 0.0 | - |
443
+ | 16.4815 | 13350 | 0.0 | - |
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+ | 16.5432 | 13400 | 0.0 | - |
445
+ | 16.6049 | 13450 | 0.0 | - |
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+ | 16.6667 | 13500 | 0.0 | - |
447
+ | 16.7284 | 13550 | 0.0 | - |
448
+ | 16.7901 | 13600 | 0.0 | - |
449
+ | 16.8519 | 13650 | 0.0 | - |
450
+ | 16.9136 | 13700 | 0.0 | - |
451
+ | 16.9753 | 13750 | 0.0 | - |
452
+ | 17.0370 | 13800 | 0.0 | - |
453
+ | 17.0988 | 13850 | 0.0 | - |
454
+ | 17.1605 | 13900 | 0.0 | - |
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+ | 17.2222 | 13950 | 0.0 | - |
456
+ | 17.2840 | 14000 | 0.0 | - |
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+ | 17.3457 | 14050 | 0.0 | - |
458
+ | 17.4074 | 14100 | 0.0 | - |
459
+ | 17.4691 | 14150 | 0.0 | - |
460
+ | 17.5309 | 14200 | 0.0 | - |
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+ | 17.5926 | 14250 | 0.0 | - |
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+ | 17.6543 | 14300 | 0.0 | - |
463
+ | 17.7160 | 14350 | 0.0 | - |
464
+ | 17.7778 | 14400 | 0.0 | - |
465
+ | 17.8395 | 14450 | 0.0 | - |
466
+ | 17.9012 | 14500 | 0.0 | - |
467
+ | 17.9630 | 14550 | 0.0 | - |
468
+ | 18.0247 | 14600 | 0.0 | - |
469
+ | 18.0864 | 14650 | 0.0 | - |
470
+ | 18.1481 | 14700 | 0.0 | - |
471
+ | 18.2099 | 14750 | 0.0 | - |
472
+ | 18.2716 | 14800 | 0.0 | - |
473
+ | 18.3333 | 14850 | 0.0 | - |
474
+ | 18.3951 | 14900 | 0.0 | - |
475
+ | 18.4568 | 14950 | 0.0 | - |
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+ | 18.5185 | 15000 | 0.0 | - |
477
+ | 18.5802 | 15050 | 0.0 | - |
478
+ | 18.6420 | 15100 | 0.0 | - |
479
+ | 18.7037 | 15150 | 0.0 | - |
480
+ | 18.7654 | 15200 | 0.0 | - |
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+ | 18.8272 | 15250 | 0.0 | - |
482
+ | 18.8889 | 15300 | 0.0 | - |
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+ | 18.9506 | 15350 | 0.0 | - |
484
+ | 19.0123 | 15400 | 0.0 | - |
485
+ | 19.0741 | 15450 | 0.0 | - |
486
+ | 19.1358 | 15500 | 0.0 | - |
487
+ | 19.1975 | 15550 | 0.0 | - |
488
+ | 19.2593 | 15600 | 0.0 | - |
489
+ | 19.3210 | 15650 | 0.0 | - |
490
+ | 19.3827 | 15700 | 0.0 | - |
491
+ | 19.4444 | 15750 | 0.0 | - |
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+ | 19.5062 | 15800 | 0.0 | - |
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+ | 19.5679 | 15850 | 0.0 | - |
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+ | 19.6296 | 15900 | 0.0 | - |
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+ | 19.6914 | 15950 | 0.0 | - |
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+ | 19.7531 | 16000 | 0.0 | - |
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+ | 19.8148 | 16050 | 0.0 | - |
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+ | 19.8765 | 16100 | 0.0 | - |
499
+ | 19.9383 | 16150 | 0.0 | - |
500
+ | 20.0 | 16200 | 0.0 | - |
501
+ | 20.0617 | 16250 | 0.0 | - |
502
+ | 20.1235 | 16300 | 0.0 | - |
503
+ | 20.1852 | 16350 | 0.0 | - |
504
+ | 20.2469 | 16400 | 0.0 | - |
505
+ | 20.3086 | 16450 | 0.0 | - |
506
+ | 20.3704 | 16500 | 0.0 | - |
507
+ | 20.4321 | 16550 | 0.0 | - |
508
+ | 20.4938 | 16600 | 0.0 | - |
509
+ | 20.5556 | 16650 | 0.0 | - |
510
+ | 20.6173 | 16700 | 0.0 | - |
511
+ | 20.6790 | 16750 | 0.0 | - |
512
+ | 20.7407 | 16800 | 0.0 | - |
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+ | 20.8025 | 16850 | 0.0 | - |
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+ | 20.8642 | 16900 | 0.0 | - |
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+ | 20.9259 | 16950 | 0.0 | - |
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+ | 20.9877 | 17000 | 0.0 | - |
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+ | 21.0494 | 17050 | 0.0 | - |
518
+ | 21.1111 | 17100 | 0.0 | - |
519
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+
664
+ ### Framework Versions
665
+ - Python: 3.10.12
666
+ - SetFit: 1.1.0
667
+ - Sentence Transformers: 3.3.1
668
+ - Transformers: 4.44.2
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+ - PyTorch: 2.2.0a0+81ea7a4
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
674
+
675
+ ### BibTeX
676
+ ```bibtex
677
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
678
+ doi = {10.48550/ARXIV.2209.11055},
679
+ url = {https://arxiv.org/abs/2209.11055},
680
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
681
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
684
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
701
+ <!--
702
+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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