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mini1013
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Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +785 -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: 이니스프리 퍼펙트9 리페어 EX 스킨케어 화장품 2종 세트 38세트 (#M)위메프 > 뷰티 > 스킨케어 > 스킨케어 세트 > 4종이상
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+ 세트 위메프 > 뷰티 > 스킨케어 > 스킨케어 세트 > 4종이상 세트
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+ - text: 동국제약 센텔리안 마데카 링클캡처 멀티밤 스틱 10gx2 동국제약 센텔리안 마데카 멀티밤 스틱 링클+멜라캡처 LotteOn > 뷰티
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+ > 남성화장품 > 로션 LotteOn > 뷰티 > 남성화장품 > 로션
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+ - text: 독일 발레아 앰플 5종 20개 골라담기 비타민 C 집중 앰플 5개ㅁ1254803-5ㅂ_아이케어 집중 앰플 7개입 5개ㅁ1249788-5ㅂ_레티놀
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+ 집중 앰플 5개ㅁ1000002337-5ㅂ_비타민 C 집중 앰플 5개ㅁ1254803-5ㅂ (#M)SSG.COM/커피/원두/차/드립백/캡슐/더치커피/캡슐커피
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+ LOREAL > Ssg > 헬레나 루빈스타인 > Generic > 앰플
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+ - text: 가히 kahi 가희 멀티밤 9g 7개 쿠팡 홈>뷰티>스킨케어>에센스/세럼/앰플;(#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>멀티밤/스틱
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+ Coupang > 뷰티 > 스킨케어 > 크림/올인원
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+ - text: 에뛰드하우스 수분가득 콜라겐 아이크림 28ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>아이/넥크림 Coupang > 뷰티
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+ > 스킨케어 > 크림/올인원 > 아이/넥크림
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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: 0.8553547392519817
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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:** 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>'11번가 단독! 라네즈 BEST 래디언씨크림/비타민앰플/피토알렉신/네오쿠션 15. 라네즈 워터 뱅크 크림 EX 50ml_하이드로 쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;11st>뷰티>선케어/메이크업>선블록;11st > 뷰티 > 스킨케어 > 스킨/토너;(#M)11st>뷰티>스킨케어>스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li><li>'유리���쥬 제모스 밤 500ml (#M)홈>해외직구관 (등록금지/2679838 매장 이용해주세요)>화장품>스킨케어>에센스/크림 HMALL > 뷰티 > 스킨케어 > 크림'</li><li>'[G][빌리프] [H몰]더 트루 크림 - 아쿠아 밤 75ml(수분크림) 홈>현대백화점>화장품>스킨케어>크림;(#M)홈>스킨케어>크림 HMALL > 현대백화점 > 화장품 > 스킨케어 > 크림'</li></ul> |
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+ | 8 | <ul><li>'SNP 에스엔피 골드 콜라겐 아이 패치 60개입 MinSellAmount (#M)화장품/향수>팩/마스크>마스크시트 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 마스크시트'</li><li>'메디필 히알루론 시카 펩타이드9 앰플 아이패치 60매 (#M)11st>스킨케어>팩/마스크>고무팩 11st > 뷰티 > 스킨케어 > 팩/마스크 > 고무팩'</li><li>'메디필 히알루론 펩타이드9 앰플 아이패치 미백 탄력 보습 기미 진정 영양 4종 1택 로즈 (#M)홈>화장품/미용>마스크/팩>마스크시트 Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트'</li></ul> |
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+ | 1 | <ul><li>'히로인 아이래쉬 세럼(속눈썹영양제)(K321A) 01.영양제(K321A) (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'</li><li>'딸리까 리포실 속눈썹 영양 젤 10ml / TALIKA - Lipocils - Gel pour la pousse des cils, 10ml (#M)홈>뷰티ㄱ-ㅂ>딸리카 Naverstore > 화장품/미용 > 색조메이크업 > 속눈썹영양제'</li><li>'코스노리 롱 액티브 아이래쉬 세럼 속눈썹영양제 ssg > 뷰티 > 메이크업 > 아이메이크업 > 마스카라 ssg > 뷰티 > 메이크업 > 아이메이크업 > 마스카라'</li></ul> |
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+ | 6 | <ul><li>'아이오페 리프트 라이브 2종세트 (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 팩/마스크 기타 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 팩/마스크 기타'</li><li>'설화수 탄력에센셜 3종 기획세트 AD20 (#M)쿠팡 홈>뷰티>스킨케어>기초세트 Coupang > 뷰티 > 스킨케어 > 기초세트'</li><li>'다나한 고율 3종 세트 /스킨+로션+크림 ssg > 유아동 > 출산/육아용품 > 스킨/바디케어 > 스킨케어세트;SSG.COM/출산/육아용품/스킨/바디케어/스킨케어세트;(#M)SSG.COM/스킨케어/스킨케어세트 ssg > 뷰티 > 스킨케어 > 스킨케어세트'</li></ul> |
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+ | 5 | <ul><li>'라네즈 크림 스킨 170ml(건성, 민감성) (#M)홈>스킨케어>스킨토너 Naverstore > 화장품/미용 > 스킨케어 > 스킨/토너'</li><li>'[10] 칼렌듈라 꽃잎 토너 점보 세트 LOREAL > DepartmentLotteOn > 키엘 > Branded > 칼렌듈라 꽃잎 토너 LOREAL > DepartmentLotteOn > 키엘 > Branded > 칼렌듈라 꽃잎 토너'</li><li>'라네즈 NEW 크림스킨 더블 구성 본품 170ml + 리필 170ml + 미스트펌프 (#M)위메프 > 뷰티 > 스킨케어 > 스킨/토너 > 스킨/토너 위메프 > 뷰티 > 스킨케어 > 스킨/토너 > 스킨/토너'</li></ul> |
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+ | 9 | <ul><li>'미르와르 두블르 화세뜨 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'독일 발레아 앰플 5종 20개 골라담기 비타민 C 집중 앰플 5개ㅁ1254803-5ㅂ_Q10 집중 앰플 7개입 5개ㅁ1249789-51ㅂ_레티놀 집중 앰플 5개ㅁ1000002337-5ㅂ_비타민 C 집중 앰플 5개ㅁ1254803-5ㅂ (#M)SSG.COM/커피/원두/차/드립백/캡슐/더치커피/캡슐커피 LOREAL > Ssg > 헬레나 루빈스타인 > Generic > 앰플'</li><li>'독일 발레아 앰플 5종 20개 골라담기 레티놀 집중 앰플 5개ㅁ1000002337-5ㅂ_레티놀 집중 앰플 5개ㅁ1000002337-5ㅂ_수분 집중 앰플 7개입 5개ㅁ1249790-5ㅂ_비타민 C 집중 앰플 5개ㅁ1254803-5ㅂ (#M)SSG.COM/커피/원두/차/드립백/캡슐/더치커피/캡슐커피 LOREAL > Ssg > 헬레나 루빈스타인 > Generic > 앰플'</li></ul> |
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+ | 4 | <ul><li>'kahi 가히멀티밤9g 가히 링클 바운스 멀티밤 9g (1개)(2개)(3개)(4개)(5개)(6개)(7개)(10개) + 제주발효오일1개 가희주름케어멀티밤 바르는뷰티가히 2개+오일1개 (#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>페이셜크림 Coupang > 뷰티 > 스킨케어 > 크림/올인원 > 페이셜크림'</li><li>'동국제약 센텔리안 마데카 멜라캡처 멀티밤 스틱 10g153455 동국제약 센텔리안 마데카 링클캡처 멀티밤 스틱 10g LotteOn > 뷰티 > 남성화장품 > 스킨 LotteOn > 뷰티 > 남성화장품 > 스킨'</li><li>'[Badger] 뱃져 오가닉 베이비 밤 캐모마일 & 카렌듈라 멀티밤 56 g (#M)쿠팡 홈>생활용품>헤어/바디/세안>바디로션/크림>바디버터 Coupang > 뷰티 > 바디 > 바디로션/크림 > 바디버터'</li></ul> |
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+ | 0 | <ul><li>'클라란스넥크림 클라랑스 엑스트라 퍼밍 넥 앤 데콜테 케어 75mL (#M)화장품/미용>스킨케어>넥케어 AD > Naverstore > 화장품/미용 > 스킨케어 > 넥케어'</li><li>'메디필 나이테 실 넥크림 100ml (#M)11st>스킨케어>탄력크림>탄력크림 11st > 뷰티 > 스킨케어 > 탄력크림 > 탄력크림'</li><li>'네츄라 비세 - 텐소리프트 넥 크림 50ml/1.7oz ssg > 뷰티 > 스킨케어 > 크림 ssg > 뷰티 > 스킨케어 > 크림'</li></ul> |
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+ | 7 | <ul><li>'(상시)엘렌실라 씨피피 베이비콜라겐 아이크림10개+갈바닉기기 싱글구성 (#M)11st>스킨케어>아이크림>아이크림 11st > 뷰티 > 스킨케어 > 아이크림'</li><li>'AHC 아이크림 시즌10 30ml x 4개 AHC아이크림 시즌10 30mlx4개(사은품) (#M)홈>화장품/미용>스킨케어>아이케어 Naverstore > 화장품/미용 > 스킨케어 > 아이케어'</li><li>'바비 브라운 엑스트라 아이 리페어 크림 15ml LotteOn > 뷰티 > 스킨케어 > 아이케어 LotteOn > 뷰티 > 스킨케어 > 아이케어'</li></ul> |
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+ | 11 | <ul><li>'청미정 비타민나무 페이스오일 LotteOn > 뷰티 > 스킨케어 > 오일 LotteOn > 뷰티 > 스킨케어 > 오일'</li><li>'티트리 오일 10ML (18400) LotteOn > 뷰티 > 스킨케어 > 오일 LotteOn > 뷰티 > 스킨케어 > 오일'</li><li>'메디힐 티트리 100 오일 10ml × 7개 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li></ul> |
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+ | 3 | <ul><li>'[달바] 화이트 트러플 미스트 세럼 100ml+50ml 100ml+50ml (#M)홈>현대백화점>화장품>스킨케어>스킨로션/미스트 HMALL > 현대백화점 > 화장품 > 스킨케어 > 스킨로션/미스트'</li><li>'라네즈 크림 스킨 미스트 120ml (#M)홈>전체상품 Naverstore > 화장품/미용 > 스킨케어 > 미스트'</li><li>'라네즈 크림스킨 미스트 120ml × 11개 (#M)쿠팡 홈>뷰티>스킨케어>미스트 Coupang > 뷰티 > 스킨케어 > 미스트'</li></ul> |
82
+ | 2 | <ul><li>'에스트라 아토베리어 MD로션 200ml 본사정품(병원전용) 아쿠아(10개입) 홈>화장품/미용>스킨케어>로션;홈>에스트라;홈>AESTURA(에스트라);(#M)홈>병원전용>로션&크림 Naverstore > 화장품/미용 > 스킨케어 > 로션'</li><li>'아벤느 트릭세라 밀크 400ml MinSellAmount (#M)화장품/향수>스킨케어>로션/에멀젼 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 로션/에멀젼'</li><li>'[에스티로더](신세계강남점)소프트 클린 인퓨전 하이드레이팅 에센스 로션 아미노산 + 워터릴리 (#M)홈>화장품/미용>스킨케어>로션 Naverstore > 화장품/미용 > 스킨케어 > 로션'</li></ul> |
83
+
84
+ ## Evaluation
85
+
86
+ ### Metrics
87
+ | Label | Accuracy |
88
+ |:--------|:---------|
89
+ | **all** | 0.8554 |
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+
91
+ ## Uses
92
+
93
+ ### Direct Use for Inference
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+
95
+ First install the SetFit library:
96
+
97
+ ```bash
98
+ pip install setfit
99
+ ```
100
+
101
+ Then you can load this model and run inference.
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+
103
+ ```python
104
+ from setfit import SetFitModel
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+
106
+ # Download from the 🤗 Hub
107
+ model = SetFitModel.from_pretrained("mini1013/master_item_top_bt9")
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+ # Run inference
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+ preds = model("에뛰드하우스 수분가득 콜라겐 아이크림 28ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>아이/넥크림 Coupang > 뷰티 > 스킨케어 > 크림/올인원 > 아이/넥크림")
110
+ ```
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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 | 22.6133 | 67 |
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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 |
150
+ | 5 | 50 |
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+ | 6 | 50 |
152
+ | 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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+
158
+ ### Training Hyperparameters
159
+ - 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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+
177
+ ### Training Results
178
+ | Epoch | Step | Training Loss | Validation Loss |
179
+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0011 | 1 | 0.5593 | - |
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+ | 0.0533 | 50 | 0.4415 | - |
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+ | 0.1066 | 100 | 0.4332 | - |
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+ | 0.1599 | 150 | 0.4122 | - |
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+ | 0.2132 | 200 | 0.3853 | - |
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+ | 0.2665 | 250 | 0.3342 | - |
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+ | 0.3198 | 300 | 0.2674 | - |
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+ | 0.3731 | 350 | 0.2174 | - |
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+ | 0.4264 | 400 | 0.1576 | - |
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+ | 0.4797 | 450 | 0.1076 | - |
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+ | 0.5330 | 500 | 0.0781 | - |
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+ | 0.5864 | 550 | 0.0634 | - |
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+ | 0.6397 | 600 | 0.0454 | - |
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+ | 0.6930 | 650 | 0.0326 | - |
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+ | 0.7463 | 700 | 0.0269 | - |
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+ | 0.7996 | 750 | 0.0251 | - |
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+ | 0.8529 | 800 | 0.0162 | - |
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+ | 0.9062 | 850 | 0.0151 | - |
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+ | 0.9595 | 900 | 0.0156 | - |
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+ | 1.0128 | 950 | 0.0105 | - |
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+ | 1.0661 | 1000 | 0.0079 | - |
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+ | 1.1194 | 1050 | 0.0106 | - |
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+ | 1.1727 | 1100 | 0.0079 | - |
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+ | 1.2260 | 1150 | 0.0075 | - |
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+ | 1.2793 | 1200 | 0.0085 | - |
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+ | 1.3326 | 1250 | 0.0071 | - |
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+ | 1.3859 | 1300 | 0.0059 | - |
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+ | 1.4392 | 1350 | 0.0073 | - |
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+ | 1.4925 | 1400 | 0.0082 | - |
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+ | 1.5458 | 1450 | 0.0072 | - |
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+ | 1.5991 | 1500 | 0.0094 | - |
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+ | 1.6525 | 1550 | 0.0101 | - |
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+ | 1.7058 | 1600 | 0.0034 | - |
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+ | 1.7591 | 1650 | 0.0011 | - |
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+ | 1.8124 | 1700 | 0.0016 | - |
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+ | 1.8657 | 1750 | 0.0004 | - |
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+ | 1.9190 | 1800 | 0.0001 | - |
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+ | 1.9723 | 1850 | 0.0001 | - |
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+ | 2.0256 | 1900 | 0.0001 | - |
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+ | 2.0789 | 1950 | 0.0001 | - |
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+ | 2.1322 | 2000 | 0.0001 | - |
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+ | 2.1855 | 2050 | 0.0001 | - |
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+ | 2.2388 | 2100 | 0.0001 | - |
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+ | 2.2921 | 2150 | 0.0001 | - |
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+ | 2.3454 | 2200 | 0.0001 | - |
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+ | 2.3987 | 2250 | 0.0001 | - |
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+ | 2.4520 | 2300 | 0.0 | - |
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+ | 2.5053 | 2350 | 0.0 | - |
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+ | 2.5586 | 2400 | 0.0 | - |
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+ | 2.6119 | 2450 | 0.0 | - |
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+ | 2.6652 | 2500 | 0.0 | - |
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+ | 2.7719 | 2600 | 0.0 | - |
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+ | 2.8252 | 2650 | 0.0 | - |
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+ | 2.8785 | 2700 | 0.0 | - |
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+ | 2.9318 | 2750 | 0.0 | - |
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+ | 2.9851 | 2800 | 0.0 | - |
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+ | 3.0384 | 2850 | 0.0 | - |
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+ | 3.0917 | 2900 | 0.0 | - |
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+ | 3.1450 | 2950 | 0.0 | - |
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+ | 3.1983 | 3000 | 0.0 | - |
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+ | 3.2516 | 3050 | 0.0002 | - |
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+ | 3.3049 | 3100 | 0.0022 | - |
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+ | 3.3582 | 3150 | 0.0057 | - |
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+ | 3.4115 | 3200 | 0.0019 | - |
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+ | 3.4648 | 3250 | 0.0034 | - |
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+ | 3.5181 | 3300 | 0.0001 | - |
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+ | 3.5714 | 3350 | 0.0 | - |
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+ | 3.6247 | 3400 | 0.0 | - |
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+ | 3.6780 | 3450 | 0.0 | - |
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+ | 3.7313 | 3500 | 0.0 | - |
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+ | 3.7846 | 3550 | 0.0 | - |
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+ | 3.9446 | 3700 | 0.0 | - |
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+ | 3.9979 | 3750 | 0.0 | - |
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+ | 4.0512 | 3800 | 0.0 | - |
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+ | 4.1045 | 3850 | 0.0 | - |
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+ | 4.1578 | 3900 | 0.0 | - |
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+ | 4.2111 | 3950 | 0.0 | - |
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+ | 4.2644 | 4000 | 0.0 | - |
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+ | 4.4243 | 4150 | 0.0 | - |
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+ | 4.5842 | 4300 | 0.0 | - |
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+ | 5.2239 | 4900 | 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 | 0.0 | - |
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+ | 6.1301 | 5750 | 0.0 | - |
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+ | 8.4222 | 7900 | 0.0101 | - |
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+ | 8.4755 | 7950 | 0.0032 | - |
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+ | 8.5288 | 8000 | 0.0016 | - |
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+ | 8.5821 | 8050 | 0.0012 | - |
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+ | 9.0085 | 8450 | 0.0 | - |
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+ | 9.0618 | 8500 | 0.0001 | - |
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+ | 9.1151 | 8550 | 0.0 | - |
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+ | 9.5949 | 9000 | 0.0001 | - |
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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 | 0.0003 | - |
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+ | 9.8081 | 9200 | 0.0001 | - |
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+ | 9.8614 | 9250 | 0.0001 | - |
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+ | 9.9147 | 9300 | 0.0 | - |
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+ | 9.9680 | 9350 | 0.0001 | - |
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+ | 10.0213 | 9400 | 0.0002 | - |
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+ | 10.0746 | 9450 | 0.0003 | - |
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+ | 10.1279 | 9500 | 0.0004 | - |
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+ | 10.1812 | 9550 | 0.0003 | - |
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+ | 10.2345 | 9600 | 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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+ | 11.0341 | 10350 | 0.0 | - |
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+ | 11.0874 | 10400 | 0.0 | - |
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+ | 11.1407 | 10450 | 0.0 | - |
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+ | 11.1940 | 10500 | 0.0 | - |
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+ | 11.2473 | 10550 | 0.0 | - |
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+ | 11.3006 | 10600 | 0.0 | - |
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+ | 11.3539 | 10650 | 0.0 | - |
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+ | 11.4072 | 10700 | 0.0 | - |
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+ | 11.4606 | 10750 | 0.0 | - |
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+ | 11.5139 | 10800 | 0.0 | - |
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+ | 11.5672 | 10850 | 0.0 | - |
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+ | 11.6205 | 10900 | 0.0 | - |
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+ | 11.6738 | 10950 | 0.0 | - |
400
+ | 11.7271 | 11000 | 0.0 | - |
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+ | 11.7804 | 11050 | 0.0 | - |
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+ | 11.8337 | 11100 | 0.0 | - |
403
+ | 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 | - |
408
+ | 12.1535 | 11400 | 0.0 | - |
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+ | 12.2068 | 11450 | 0.0 | - |
410
+ | 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 | - |
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+ | 12.6866 | 11900 | 0.0 | - |
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+ | 12.7399 | 11950 | 0.0 | - |
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+ | 12.8465 | 12050 | 0.0 | - |
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+ | 12.9531 | 12150 | 0.0 | - |
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+ | 13.0064 | 12200 | 0.0 | - |
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+ | 13.0597 | 12250 | 0.0 | - |
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+ | 13.1130 | 12300 | 0.0 | - |
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+ | 13.1663 | 12350 | 0.0 | - |
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+ | 13.2196 | 12400 | 0.0 | - |
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+ | 13.2729 | 12450 | 0.0 | - |
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+ | 13.3262 | 12500 | 0.0 | - |
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+ | 13.3795 | 12550 | 0.0 | - |
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+ | 13.4328 | 12600 | 0.0 | - |
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+ | 13.4861 | 12650 | 0.0 | - |
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+
744
+ ### Framework Versions
745
+ - Python: 3.10.12
746
+ - SetFit: 1.1.0
747
+ - Sentence Transformers: 3.3.1
748
+ - Transformers: 4.44.2
749
+ - PyTorch: 2.2.0a0+81ea7a4
750
+ - Datasets: 3.2.0
751
+ - Tokenizers: 0.19.1
752
+
753
+ ## Citation
754
+
755
+ ### BibTeX
756
+ ```bibtex
757
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
758
+ doi = {10.48550/ARXIV.2209.11055},
759
+ url = {https://arxiv.org/abs/2209.11055},
760
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
761
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
762
+ title = {Efficient Few-Shot Learning Without Prompts},
763
+ publisher = {arXiv},
764
+ year = {2022},
765
+ copyright = {Creative Commons Attribution 4.0 International}
766
+ }
767
+ ```
768
+
769
+ <!--
770
+ ## Glossary
771
+
772
+ *Clearly define terms in order to be accessible across audiences.*
773
+ -->
774
+
775
+ <!--
776
+ ## Model Card Authors
777
+
778
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
779
+ -->
780
+
781
+ <!--
782
+ ## Model Card Contact
783
+
784
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
785
+ -->
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+ },
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]
sentence_bert_config.json ADDED
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+ {
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+ "do_lower_case": false
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+ }
special_tokens_map.json ADDED
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tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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vocab.txt ADDED
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