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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 +687 -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: 케라스타즈 엘릭서 얼팀 헤어오일 엠페리얼 티 100ml × 1개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어오일 Coupang >
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+ 뷰티 > 헤어 > 헤어에센스/오일 > 헤어오일
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+ - text: 쿤달 네이처 샴푸 싱글파우치 베이비파우더향 10ml × 100개입 Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸;(#M)쿠팡
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+ 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸
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+ - text: 려 자양 탈모전문케어 트리트먼트 경주달밤/여수하늘 200ml 옵션 229318 02 여수하늘 200ml (#M)11st>남성화장품>남성에센스>남성에센스
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+ 11st > 뷰티 > 남성화장품 > 남성에센스
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+ - text: '[미쟝센] 퍼펙트세럼 모음 80ml 2입 04 로즈퍼퓸 세럼 2개 LotteOn > 뷰티 > 헤어케어 > 헤어에센스 LotteOn
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+ > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일'
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+ - text: 미쟝센 퍼펙트세럼 헤어에센스 오리지날 스타일링 슈퍼리치 미쟝센 NEW퍼펙트세럼80ml 코코워터 LotteOn > 뷰티 > 헤어/바디
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+ > 헤어케어 > 헤어에센스/오일 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일
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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: 0.7773372337596403
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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:** 10 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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+ | 9 | <ul><li>'려 탈모전문케어 퍼퓸트리트먼트 서울석양200ml ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어트리트먼트;ssg > 생활/주방 > 건강/위생용품 > 칫솔/치약/구강청결 > 치약;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸'</li><li>'●미쟝센 데미지케어 대용량 트리트먼트 1000ml x 2 위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모 > 바디워시/스크럽;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트'</li><li>'[초특가] 미쟝센/해피바스 BEST 상품 균일가전 1-2 미쟝센 트리트먼트_데미지케어 트리트먼트 180ml 4입 (#M)GSSHOP>뷰티>헤어케어>트리트먼트 GSSHOP > 뷰티 > 스킨케어 > 크림'</li></ul> |
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+ | 2 | <ul><li>'아베다 인바티 어드밴스드 씨크닝 컨디셔너 200ml LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스'</li><li>'케라시스 퍼퓸 샴푸 린스 980ml x3개 [0008]퓨어 린스 x3개 (#M)헤어케어>샴푸>일반샴푸 AD > 11st > 뷰티 > 헤어케어 > 샴푸 > 일반샴푸'</li><li>'오가니스트 아르간 리페어 손상 영양케어 컨디셔너 500ml X 2개 위메프 > 생활·주방·반려동물 > 세제/구강 > 세탁세제/섬유유연제 > 세탁세제;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 기능성 샴푸/린스'</li></ul> |
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+ | 0 | <ul><li>'헤어플러스 오프레시 각질제거 두피 스케일링 50ml (#M)화장품/미용>헤어케어>두피케어 Naverstore > 화장품/미용 > 헤어케어 > 두피케어'</li><li>'닥터포헤어 씨 솔트 스케일러 두피스케일링 200g 옵션1 : 300g (리뉴얼) (#M)홈>화장품/미용>헤어케어>두피케어 Naverstore > 화장품/미용 > 헤어케어 > 두피케어'</li><li>'[바이브랩] 4주 솔루션 두피 앰플 헤어라인 탈모증상완화 15ml x2 [67%할인] 두피앰플 3SET (#M)화장품/미용>헤어케어>두피케어 Naverstore > 헤어케어 > 탈모케어/두피케어'</li></ul> |
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+ | 4 | <ul><li>'샤넬 헤어미스트 샹스 오 땅드르 헤어 미스트 35ml 본품 (#M)홈>화장품/미용>헤어케어>헤어미스트 Naverstore > 화장품/미용 > 헤어케어 > 헤어미스트'</li><li>'모레모 프로틴 미스트 팩 M 115ml + 미라클 2X 헤어 트리트먼트 러브 에디션 60ml 세트 홈 > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'[기획] 아쿠아 디 파르마 피오니아 헤어미스트 50ml (르 노빌리 향수 1.5ml 1종 증정) 홈>현대백화점>화장품>향수>기타;홈>현대백화점>화장품>향수>여성용;(#M)홈>향수>여성향수 HMALL > 뷰티 > 향수 > 여성향수'</li></ul> |
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+ | 8 | <ul><li>'댕기머리 윤초 스페셜 선물세트 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>한방샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 한방샴푸'</li><li>'톤28 샴푸바 S19바오밥 100g+노워시트리트먼트바 탄력80g 2종세트[천연비오틴샴푸] S21(두피장벽/얇은모) 검은콩_탄력 (#M)화장품/미용>헤어케어>헤어케어세트 Naverstore > 화장품/미용 > 헤어케어 > 헤어케어세트'</li><li>'[오리지널픽_키렌][선.착.순.사.은.품/1 + 1 골라담기]키렌 천연샴푸/헤어트리트먼트/바디워시/바디로션/핸드워시 500ml 01_스위트부케 샴푸_02_베이비로즈 트리트먼트 (#M)헤어케어>샴푸>일반샴푸 AD > traverse > 11st > 뷰티 > 헤어케어 > 샴푸 > 일반샴푸'</li></ul> |
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+ | 6 | <ul><li>'뺑쏘 트라쐬르 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기/소품 > 기기액세서리'</li><li>'쿤달 울트라 헤어세럼 베이비파우더향 100ml × 5개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일'</li><li>'무코타 샤멘느 샤이닝 딥케어 세럼 50ml (손상모발용 세럼) ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어팩/마스크 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어팩/마스크'</li></ul> |
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+ | 3 | <ul><li>'닥터모발앤 맥주효모 탈모 완화 샴푸 프리미엄 독일 488000ppm 두피가려움 비듬 지성 건성 민감성 488 [12주 71% 할인] 건성+민감성두피 전용 (#M)화장품/미용>헤어케어>탈모케어 AD > Naverstore > 화장품/미용 > 헤어케어 > 탈모케어'</li><li>'부케가르니 딥 퍼퓸 샴푸/트리트먼트 1L 딥 퍼퓸 샴푸 1L 베이비파우더 (#M)11st>헤어케어>샴푸>일반 11st > 뷰티 > 헤어케어 > 샴푸'</li><li>'폴메디슨 딥레드 패스트 탈모 샴푸, 1077ml(Fast Shampoo) 딥레드패스트샴푸 베이비파우더 1개 (#M)11st>헤어케어>탈모/두피관리제>두피마사지기 11st > 뷰티 > 헤어케어 > 탈모/두피관리제 > 두피마사지기'</li></ul> |
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+ | 5 | <ul><li>'[아도르] 퍼펙트 헤어필업(단백질 헤어앰플)13ml-10개입1box (#M)GSSHOP>뷰티>헤어케어>헤어에센스 GSSHOP > 뷰티 > 헤어케어 > 헤어에센스'</li><li>'[온세일]모로칸오일 정품 세라믹 브러쉬/드라이빗/대왕롤빗 라운드 35mm LotteOn > 뷰티 > 뷰티소품 > 헤어소품 LotteOn > 뷰티 > 뷰티기기/소품 > 헤어소품 > 빗/헤어브러쉬'</li><li>'헤어플러스 단백질 본드 앰플 145ml 150ml(여행용15ml x 10개) (#M)홈>화장품/미용>헤어케어>헤어에센스 Naverstore > 화장품/미용 > 헤어케어 > 헤어에센스'</li></ul> |
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+ | 7 | <ul><li>'로더베르 약산성 샴푸1000g 대용량 천연 단백질 청소년 헤어 비듬 두피 지성 베이비파우더 로더베르 아르간 헤어오일100ml_화이트머스크 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'【해외직구】 모로칸오일 트리트먼트 오리지널 100ml+100ml / MOROCCANOIL 모로칸오일 / 펌핑기 포함 모로칸오일 트리트먼트 오리지널 100ml 1+1 ssg > 디지털 > 휴대폰/스마트기기 > [해외직구]스마트기기;ssg > 디지털 > 생활/소형가전 > 해외직구 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어트리트먼트'</li><li>'모도루 디럭스슈퍼프로틴 단백질 미용실 손상모트리트먼트 540ml+헤어에센스오일 100ml [2종세트] (#M)홈>★얼리썸머 초특가전★ Naverstore > 화장품/미용 > 헤어케어 > 헤어케어세트'</li></ul> |
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+ | 1 | <ul><li>'모레모 리커버리 밤 B (120ml) 리커버리 밤 B 루비 에디션 (120ml) 홈>화장품/미용>헤어케어>트리트먼트;(#M)홈>모레모>트리트먼트 Naverstore > 화장품/미용 > 헤어케어 > 트리트먼트'</li><li>'힐링버드 울트라 프로틴 노워시 앰플 트리트먼트 200ml 200ml+헤어오일 31ml (#M)홈>화장품/미용>헤어케어>트리트먼트 Naverstore > 화장품/미용 > 헤어케어 > 트리트먼트'</li><li>'아모스 리페어샤인 모이스트 헤어에센스1+1 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일'</li></ul> |
80
+
81
+ ## Evaluation
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+
83
+ ### Metrics
84
+ | Label | Accuracy |
85
+ |:--------|:---------|
86
+ | **all** | 0.7773 |
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+
88
+ ## Uses
89
+
90
+ ### Direct Use for Inference
91
+
92
+ First install the SetFit library:
93
+
94
+ ```bash
95
+ pip install setfit
96
+ ```
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+
98
+ Then you can load this model and run inference.
99
+
100
+ ```python
101
+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_item_top_bt13")
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+ # Run inference
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+ preds = model("케라스타즈 엘릭서 얼팀 헤어오일 엠페리얼 티 100ml × 1개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어오일 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어오일")
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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+
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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 | 13 | 24.996 | 125 |
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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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+
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+ ### Training Hyperparameters
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+ - batch_size: (64, 64)
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+ - num_epochs: (30, 30)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 100
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
172
+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
174
+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0013 | 1 | 0.4713 | - |
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+ | 0.0639 | 50 | 0.4253 | - |
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+ | 0.1279 | 100 | 0.3864 | - |
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+ | 0.1918 | 150 | 0.358 | - |
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+ | 0.2558 | 200 | 0.3284 | - |
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+ | 0.3197 | 250 | 0.3139 | - |
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+ | 0.3836 | 300 | 0.2877 | - |
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+ | 0.4476 | 350 | 0.2604 | - |
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+ | 0.5115 | 400 | 0.2218 | - |
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+ | 0.5754 | 450 | 0.1841 | - |
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+ | 0.6394 | 500 | 0.1548 | - |
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+ | 0.7033 | 550 | 0.1272 | - |
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+ | 0.7673 | 600 | 0.1068 | - |
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+ | 0.8312 | 650 | 0.0866 | - |
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+ | 0.8951 | 700 | 0.0656 | - |
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+ | 0.9591 | 750 | 0.0477 | - |
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+ | 1.0230 | 800 | 0.0377 | - |
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+ | 1.0870 | 850 | 0.0249 | - |
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+ | 1.1509 | 900 | 0.0144 | - |
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+ | 1.2148 | 950 | 0.0131 | - |
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+ | 1.2788 | 1000 | 0.0153 | - |
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+ | 1.3427 | 1050 | 0.012 | - |
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+ | 1.4066 | 1100 | 0.0104 | - |
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+ | 1.4706 | 1150 | 0.0102 | - |
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+ | 1.5345 | 1200 | 0.0079 | - |
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+ | 1.5985 | 1250 | 0.0039 | - |
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+ | 1.6624 | 1300 | 0.0026 | - |
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+ | 1.7263 | 1350 | 0.0015 | - |
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+ | 1.7903 | 1400 | 0.001 | - |
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+ | 1.8542 | 1450 | 0.0013 | - |
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+ | 1.9182 | 1500 | 0.0013 | - |
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+ | 1.9821 | 1550 | 0.001 | - |
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+ | 2.0460 | 1600 | 0.0009 | - |
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+ | 2.1100 | 1650 | 0.0012 | - |
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+ | 2.1739 | 1700 | 0.0007 | - |
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+ | 2.2379 | 1750 | 0.0009 | - |
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+ | 2.3018 | 1800 | 0.0009 | - |
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+ | 2.3657 | 1850 | 0.0007 | - |
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+ | 2.4297 | 1900 | 0.0011 | - |
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+ | 2.4936 | 1950 | 0.0008 | - |
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+ | 2.5575 | 2000 | 0.0015 | - |
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+ | 2.6215 | 2050 | 0.0028 | - |
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+ | 2.6854 | 2100 | 0.0032 | - |
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+ | 2.7494 | 2150 | 0.0019 | - |
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+ | 2.8133 | 2200 | 0.0017 | - |
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+ | 2.8772 | 2250 | 0.0008 | - |
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+ | 2.9412 | 2300 | 0.0019 | - |
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+ | 3.0051 | 2350 | 0.0016 | - |
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+ | 3.0691 | 2400 | 0.0018 | - |
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+ | 3.1330 | 2450 | 0.0013 | - |
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+ | 3.1969 | 2500 | 0.0007 | - |
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+ | 3.2609 | 2550 | 0.0006 | - |
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+ | 3.3248 | 2600 | 0.0009 | - |
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+ | 3.3887 | 2650 | 0.0016 | - |
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+ | 3.4527 | 2700 | 0.002 | - |
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+ | 3.5166 | 2750 | 0.0032 | - |
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+ | 3.5806 | 2800 | 0.0012 | - |
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+ | 3.6445 | 2850 | 0.0012 | - |
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+ | 3.7084 | 2900 | 0.0014 | - |
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+ | 3.7724 | 2950 | 0.0011 | - |
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+ | 3.8363 | 3000 | 0.0005 | - |
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+ | 3.9003 | 3050 | 0.0007 | - |
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+ | 3.9642 | 3100 | 0.0004 | - |
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+ | 4.0281 | 3150 | 0.0003 | - |
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+ | 4.0921 | 3200 | 0.0007 | - |
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+ | 4.1560 | 3250 | 0.0005 | - |
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+ | 4.2199 | 3300 | 0.0005 | - |
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+ | 4.2839 | 3350 | 0.0006 | - |
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+ | 4.3478 | 3400 | 0.0004 | - |
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+ | 4.4118 | 3450 | 0.0004 | - |
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+ | 4.4757 | 3500 | 0.0008 | - |
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+ | 4.5396 | 3550 | 0.0006 | - |
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+ | 4.6036 | 3600 | 0.0003 | - |
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+ | 4.6675 | 3650 | 0.0007 | - |
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+ | 4.7315 | 3700 | 0.0009 | - |
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+ | 4.7954 | 3750 | 0.0005 | - |
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+ | 4.8593 | 3800 | 0.0006 | - |
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+ | 4.9233 | 3850 | 0.0007 | - |
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+ | 4.9872 | 3900 | 0.0005 | - |
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+ | 5.0512 | 3950 | 0.0006 | - |
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+ | 5.1151 | 4000 | 0.0004 | - |
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+ | 5.1790 | 4050 | 0.0005 | - |
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+ | 5.2430 | 4100 | 0.0007 | - |
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+ | 5.3069 | 4150 | 0.0004 | - |
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+ | 5.3708 | 4200 | 0.0005 | - |
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+ | 5.4348 | 4250 | 0.0004 | - |
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+ | 5.4987 | 4300 | 0.0005 | - |
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+ | 5.5627 | 4350 | 0.0007 | - |
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+ | 5.6266 | 4400 | 0.0006 | - |
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+ | 5.6905 | 4450 | 0.0006 | - |
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+ | 5.7545 | 4500 | 0.0006 | - |
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+ | 5.8184 | 4550 | 0.0005 | - |
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+ | 5.8824 | 4600 | 0.0005 | - |
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+ | 5.9463 | 4650 | 0.0008 | - |
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+ | 6.0102 | 4700 | 0.0005 | - |
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+ | 6.1381 | 4800 | 0.0004 | - |
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+ | 6.2020 | 4850 | 0.0005 | - |
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+ | 6.2660 | 4900 | 0.0007 | - |
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+ | 6.3299 | 4950 | 0.0007 | - |
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+ | 6.3939 | 5000 | 0.0005 | - |
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+ | 6.6496 | 5200 | 0.0006 | - |
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+ | 6.8414 | 5350 | 0.0004 | - |
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+ | 6.9054 | 5400 | 0.0009 | - |
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+ | 6.9693 | 5450 | 0.0009 | - |
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+ | 7.0332 | 5500 | 0.0007 | - |
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+ | 7.0972 | 5550 | 0.0009 | - |
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+ | 7.1611 | 5600 | 0.0093 | - |
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+ | 7.2251 | 5650 | 0.0075 | - |
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+ | 7.2890 | 5700 | 0.0017 | - |
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+ | 7.3529 | 5750 | 0.0012 | - |
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+ | 7.4169 | 5800 | 0.001 | - |
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+ | 7.4808 | 5850 | 0.0008 | - |
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+ | 7.6726 | 6000 | 0.0006 | - |
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+ | 7.7366 | 6050 | 0.0007 | - |
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+ | 8.1202 | 6350 | 0.0005 | - |
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+ | 8.1841 | 6400 | 0.0005 | - |
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+ | 8.2481 | 6450 | 0.0006 | - |
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+ | 8.3120 | 6500 | 0.0005 | - |
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+ | 8.3760 | 6550 | 0.0006 | - |
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+ | 8.7596 | 6850 | 0.0009 | - |
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+ | 8.8235 | 6900 | 0.0006 | - |
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+ | 9.0153 | 7050 | 0.0003 | - |
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+ | 9.4629 | 7400 | 0.0006 | - |
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+ | 9.5269 | 7450 | 0.0006 | - |
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+ | 9.6547 | 7550 | 0.0006 | - |
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+ | 9.7187 | 7600 | 0.0005 | - |
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+ | 9.8465 | 7700 | 0.0006 | - |
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+ | 10.0384 | 7850 | 0.0018 | - |
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+ | 10.1023 | 7900 | 0.0045 | - |
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+ | 10.1662 | 7950 | 0.0024 | - |
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+ | 10.2302 | 8000 | 0.0013 | - |
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+ | 10.2941 | 8050 | 0.001 | - |
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+ | 10.9974 | 8600 | 0.0007 | - |
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+ | 11.0614 | 8650 | 0.0006 | - |
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+ | 11.1253 | 8700 | 0.0004 | - |
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+ | 11.1893 | 8750 | 0.0006 | - |
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+ | 11.2532 | 8800 | 0.0004 | - |
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+ | 11.3171 | 8850 | 0.0004 | - |
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+ | 11.3811 | 8900 | 0.0006 | - |
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+ | 11.4450 | 8950 | 0.0006 | - |
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+ | 11.5090 | 9000 | 0.0008 | - |
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+ | 11.7647 | 9200 | 0.0007 | - |
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+ | 11.8286 | 9250 | 0.0007 | - |
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+ | 11.8926 | 9300 | 0.0008 | - |
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+ | 11.9565 | 9350 | 0.0007 | - |
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+ | 12.0205 | 9400 | 0.0006 | - |
364
+ | 12.0844 | 9450 | 0.0009 | - |
365
+ | 12.1483 | 9500 | 0.0008 | - |
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+ | 12.2123 | 9550 | 0.0005 | - |
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+ | 12.2762 | 9600 | 0.0005 | - |
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+ | 12.3402 | 9650 | 0.0004 | - |
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+ | 12.4041 | 9700 | 0.0005 | - |
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+ | 12.4680 | 9750 | 0.0003 | - |
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+ | 12.5320 | 9800 | 0.0004 | - |
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+ | 12.5959 | 9850 | 0.0006 | - |
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+ | 12.6598 | 9900 | 0.0007 | - |
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+ | 12.7238 | 9950 | 0.0006 | - |
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+ | 12.7877 | 10000 | 0.0006 | - |
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+ | 12.8517 | 10050 | 0.0005 | - |
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+ | 12.9156 | 10100 | 0.0009 | - |
378
+ | 12.9795 | 10150 | 0.0004 | - |
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+ | 13.0435 | 10200 | 0.0003 | - |
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+ | 13.1074 | 10250 | 0.0007 | - |
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+ | 13.1714 | 10300 | 0.0005 | - |
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+ | 13.2353 | 10350 | 0.001 | - |
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+ | 13.2992 | 10400 | 0.001 | - |
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+ | 13.3632 | 10450 | 0.0006 | - |
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+ | 13.4271 | 10500 | 0.0006 | - |
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+ | 13.4910 | 10550 | 0.0007 | - |
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+ | 13.5550 | 10600 | 0.0005 | - |
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+ | 13.6189 | 10650 | 0.0004 | - |
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+ | 13.6829 | 10700 | 0.0006 | - |
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+ | 13.7468 | 10750 | 0.0005 | - |
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+ | 13.8107 | 10800 | 0.0006 | - |
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+ | 13.8747 | 10850 | 0.0005 | - |
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+ | 14.0026 | 10950 | 0.0005 | - |
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+ | 14.0665 | 11000 | 0.0004 | - |
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+ | 14.1304 | 11050 | 0.0005 | - |
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+ | 14.1944 | 11100 | 0.0006 | - |
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+ | 14.2583 | 11150 | 0.0004 | - |
399
+ | 14.3223 | 11200 | 0.0006 | - |
400
+ | 14.3862 | 11250 | 0.0006 | - |
401
+ | 14.4501 | 11300 | 0.0005 | - |
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+ | 14.5141 | 11350 | 0.0008 | - |
403
+ | 14.5780 | 11400 | 0.0007 | - |
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+ | 14.6419 | 11450 | 0.0005 | - |
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+ | 14.7059 | 11500 | 0.0005 | - |
406
+ | 14.7698 | 11550 | 0.0007 | - |
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+ | 14.8338 | 11600 | 0.0004 | - |
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+ | 14.8977 | 11650 | 0.0005 | - |
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+ | 14.9616 | 11700 | 0.0007 | - |
410
+ | 15.0256 | 11750 | 0.0007 | - |
411
+ | 15.0895 | 11800 | 0.0006 | - |
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+ | 15.1535 | 11850 | 0.0005 | - |
413
+ | 15.2174 | 11900 | 0.0002 | - |
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+ | 15.2813 | 11950 | 0.0006 | - |
415
+ | 15.3453 | 12000 | 0.0006 | - |
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+ | 15.4092 | 12050 | 0.0004 | - |
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+ | 15.4731 | 12100 | 0.0005 | - |
418
+ | 15.5371 | 12150 | 0.0038 | - |
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+ | 15.6010 | 12200 | 0.0088 | - |
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+ | 15.6650 | 12250 | 0.001 | - |
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+ | 15.7289 | 12300 | 0.0005 | - |
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+ | 15.7928 | 12350 | 0.0007 | - |
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+ | 15.8568 | 12400 | 0.0005 | - |
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+ | 15.9207 | 12450 | 0.0005 | - |
425
+ | 15.9847 | 12500 | 0.0006 | - |
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+ | 16.0486 | 12550 | 0.0012 | - |
427
+ | 16.1125 | 12600 | 0.0009 | - |
428
+ | 16.1765 | 12650 | 0.0029 | - |
429
+ | 16.2404 | 12700 | 0.0006 | - |
430
+ | 16.3043 | 12750 | 0.0007 | - |
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+
646
+ ### Framework Versions
647
+ - Python: 3.10.12
648
+ - SetFit: 1.1.0
649
+ - Sentence Transformers: 3.3.1
650
+ - Transformers: 4.44.2
651
+ - 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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+
655
+ ## Citation
656
+
657
+ ### BibTeX
658
+ ```bibtex
659
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
660
+ doi = {10.48550/ARXIV.2209.11055},
661
+ url = {https://arxiv.org/abs/2209.11055},
662
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
663
+ 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},
665
+ publisher = {arXiv},
666
+ year = {2022},
667
+ copyright = {Creative Commons Attribution 4.0 International}
668
+ }
669
+ ```
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+
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+ <!--
672
+ ## Glossary
673
+
674
+ *Clearly define terms in order to be accessible across audiences.*
675
+ -->
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+
677
+ <!--
678
+ ## Model Card Authors
679
+
680
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
681
+ -->
682
+
683
+ <!--
684
+ ## Model Card Contact
685
+
686
+ *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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