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mini1013
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master_cate_bt_top13_test

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 +686 -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: '[라벨영] 쇼킹 두피탄산팩/두피사이다 01. 두피탄산팩(두피사이다) 화장품|미용>헤어케어|염색>샴푸린스>샴푸;(#M)홈>화장품/미용>헤어케어|염색>샴푸린스>샴푸
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+ HMALL > 뷰티 > 화장품/미용 > 헤어케어 > 샴푸린스 > 샴푸'
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+ - text: 다봉쓰 미용실 헤어 컨디셔너 트리트먼트 린스 엔젤스 LPT ② 엔젤스LPT + 전용케이스&펌프 홈>♬ 다봉쓰 [MADE];홈>♬ 다봉쓰
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+ [대표템];홈>다봉쓰 [No.1];(#M)홈>1위~10위 Naverstore > 화장품/미용 > 헤어케어 > 린스
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+ - text: 라보에이치 탈모증상완화 트리트먼트 두피강화 200ml 1입 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩
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+ LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩
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+ - text: 오가니스트 히말라야 핑크솔트 샴푸 500ml X 5개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸 LotteOn >
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+ 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸
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+ - text: 15838957-닥터 방기원샴푸 랩 1000ml 2개 / SN 기본 홈 > 뷰티 > 헤어/바디 > 헤어케어 > 두피/탈모케어 LO >
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+ traverse > LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 두피/탈모케어
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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: 0.6191919191919192
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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:** 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>'미틱오일 크림 유니버셀레 150ml MinSellAmount (#M)바디/헤어>헤어케어>헤어에센스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어에센스'</li><li>'[토니모리] 촉촉한 영양 공급 및 탄력있는 컬 연출을 위한 헤어 로션 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어로션 Coupang > 뷰티 > 로드샵 > 헤어 > 헤어에센스/오일 > 헤어로션'</li><li>'아윤채 리프레싱 마스크 200ml LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스'</li></ul> |
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+ | 2 | <ul><li>'려 함빛/청아/흑운/함초수 500ml 4입 모음딜 01 함빛극손상케어샴푸 500ML 4개 (#M)홈>화장품/미용>헤어케어|염색>샴푸린스>샴푸 HMALL > 뷰티 > 화장품/미용 > 헤어케어 > 헤어관리 > 샴푸/린스'</li><li>'엘지 엘라스틴 여행용 휴대용 린스 50ml 50ml × 1개 Coupang > 뷰티 > 선물세트/키트 > 여행용키트;쿠팡 홈>여행용품>여행용화장품/용기>헤어/바디/멀티;(#M)쿠팡 홈>뷰티>선물세트/키트>여행용키트>헤어/바디케어 Coupang > 뷰티 > 선물세트/키트 > 여행용키트 > 헤어/바디케어'</li><li>'도브 인텐스 리페어 컨디셔너 660ml (#M)위메프 > 생활·주방용품 > 바디/헤어 > 바디케어/워시/제모 > 바디워시/스크럽 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 바디워시/스크럽'</li></ul> |
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+ | 0 | <ul><li>'티트리 퓨리파잉 토닉 100ml MinSellAmount (#M)바디/헤어>헤어케어>헤어에센스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어에센스'</li><li>'려 자양윤모 두피 딥클렌징 스케일러 EX 145ml 두피각질 두피스케일링 스칼프 두피 딥클렌징 스케일러 EX 145ml (#M)홈>화장품/미용>헤어케어>두피케어 Naverstore > 화장품/미용 > 헤어케어 > 두피케어'</li><li>'[아베다] 인바티 어드밴스드 스칼프 리바이탈라이저 150ml 백화점정품 (#M)화장품/미용>헤어케어>헤어에센스 Naverstore > 화장품/미용 > 헤어케어 > 헤어에센스'</li></ul> |
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+ | 4 | <ul><li>'아윤채 컴플리트 리뉴 에센스 미스트 100ml 위메프 > 뷰티 > 선케어 > 선밤/선스틱;위메프 > 뷰티 > 선케어 > 선밤/선스틱 > 선밤/선스틱;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 선케어 > 선밤/선스틱'</li><li>'(현대Hmall)츠바키 프리미엄 리페어 워터 220ml (#M)위메프 > 생활·주방용품 > 바디/헤어 > 헤어염색/파마/왁스 > 헤어스타일링 위메프 > 뷰티 > 바디/헤어 > 헤어염색/파마/왁스 > 헤어스타일링'</li><li>'할페티 헤어퍼퓸 30ML(공식수입정품) DepartmentLotteOn > 뷰티 > 향수 > 여성용 > 31ml~50ml DepartmentLotteOn > 뷰티 > 향수 > 여성용 > 51ml~100ml'</li></ul> |
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+ | 8 | <ul><li>'세라 샴푸 1.2L+트리트먼트 1.2L 화이트솝 MinSellAmount (#M)바디/헤어>헤어케어>샴푸/린스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 샴푸/린스'</li><li>'[12] 크리니크 iD (+ 벚꽃 부스터 추가 구성) 젤리 ssg > 뷰티 > 스킨케어 > 스킨케어세트;ssg > 뷰티 > 스킨케어 > 로션 ssg > 뷰티 > 스킨케어 > 로션'</li><li>'[4+1]애경 추석선물세트 케라시스 퍼퓸i-6호(총5개) 상세이미지참조 (#M)쿠팡 홈>생활용품>헤어/바디/세안>바디로션/크림>바디케어세트 Coupang > 뷰티 > 바디 > 바디로션/크림 > 바디케어세트'</li></ul> |
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+ | 6 | <ul><li>'실크테라피 갈색병 인리치드 액션 헤어에센스 150ml /SH (#M)11st>헤어케어>헤어에센스>헤어에센스 11st > 뷰티 > 헤어케어 > 헤어에센스 > 헤어에센스'</li><li>'꽃을든남자 레드플로 동백 헤어 에멀젼 에센스/ 로션 MinSellAmount (#M)바디/헤어>헤어케어>기타헤어케어용품 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 기타헤어케어용품'</li><li>'아윤채 컬플리뉴 에센스 오일 100ml (#M)11st>헤어케어>헤어에센스>헤어에센스 11st > 뷰티 > 헤어케어 > 헤어에센스'</li></ul> |
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+ | 3 | <ul><li>'어네이즈 컬루어 실버그레이 컬러 토닝 샴푸 보색샴푸 300ml 리얼핑크 보색샴푸 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 보색샴푸'</li><li>"[김혜윤's Pick] 바티스트 드라이샴푸 12종 중 택1 02_블러쉬 50ml (#M)11st>헤어케어>샴푸>일반 11st > 뷰티 > 헤어케어 > 샴푸"</li><li>'[K쇼핑][로레알파리] [세트] 키즈 스트로우베리 스무디 + 키즈 써니 오렌지 샴푸 써니 오렌지 x 2개_개당 중량_상세페이지참조 × 써니 오렌지 x 2개_개당 용량_상세페이 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸'</li></ul> |
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+ | 5 | <ul><li>'[SSG 단독 출시]5센스 골드 캐시미어 세트 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스'</li><li>'[CJ단독] 단백질 본드 앰플 95ml 4개+15ml 5개 (#M)뷰티>헤어/바디/미용기기>헤어케어>에센스/앰플/오일 CJmall > 뷰티 > 헤어/바디/미용기기 > 헤어케��� > 트리트먼트/팩/마스크'</li><li>'엑스트라 오디네리 오일 100ml (4종 선택1) 리치브라운100ml(극손상용) LotteOn > 뷰티 > 헤어케어 > 헤어케어세트 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어케어세트'</li></ul> |
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+ | 7 | <ul><li>'[케라스타즈][신세계 상품권 5천원 증정][건조 모발용 여신오일] 엘릭서 얼팀 오리지널 100ml 세트 (3만원 상당 기프트 증정) SsgChicor > CHICOR > 바디/헤어/향수 > 헤어케어 SsgChicor > CHICOR > 바디/헤어/향수 > 헤어케어'</li><li>'도깨비천국 로시크 숨마 엘릭서 에멀전130ml () LotteOn > 뷰티 > 스킨케어 > 로션/에멀전 LotteOn > 뷰티 > 스킨케어 > 로션/에멀전'</li><li>'엑스트라오디네리오일 100ml 2종 (8종택2) + 오일2ml 2종 (도착보장) 브라운_브라운 (#M)화장품/미용>헤어케어>헤어에센스 AD > Naverstore > lorealparis브랜드스토어 > ALL'</li></ul> |
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+ | 1 | <ul><li>'아모스 컬링 에센스 2X 투엑스 탄력 150ml LotteOn > 뷰티 > 헤어케어 > 헤어미스트 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 컬크림'</li><li>'실크테라피 샤인에센스 260ml세트130ml 1개 + 65ml 2개 없음 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'케라스타즈 헤어 오일 트리트먼트 헤어크림 모음/ 시몽 넥타 케라틴 테르미크 150ml/열활성화 리브인 트리트먼트 엘릭서 얼팀 오리지널 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어로션 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어로션'</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** | 0.6192 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
94
+ ```bash
95
+ pip install setfit
96
+ ```
97
+
98
+ Then you can load this model and run inference.
99
+
100
+ ```python
101
+ from setfit import SetFitModel
102
+
103
+ # Download from the 🤗 Hub
104
+ model = SetFitModel.from_pretrained("mini1013/master_cate_bt_top13_test")
105
+ # Run inference
106
+ preds = model("오가니스트 히말라야 핑크솔트 샴푸 500ml X 5개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸")
107
+ ```
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+
109
+ <!--
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+ ### Downstream Use
111
+
112
+ *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
117
+
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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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+ <!--
128
+ ### Recommendations
129
+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 10 | 22.5992 | 68 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 49 |
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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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+
153
+ ### Training Hyperparameters
154
+ - 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
159
+ - body_learning_rate: (2e-05, 1e-05)
160
+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
162
+ - distance_metric: cosine_distance
163
+ - margin: 0.25
164
+ - end_to_end: False
165
+ - use_amp: False
166
+ - warmup_proportion: 0.1
167
+ - l2_weight: 0.01
168
+ - seed: 42
169
+ - eval_max_steps: -1
170
+ - load_best_model_at_end: False
171
+
172
+ ### Training Results
173
+ | Epoch | Step | Training Loss | Validation Loss |
174
+ |:-------:|:-----:|:-------------:|:---------------:|
175
+ | 0.0013 | 1 | 0.442 | - |
176
+ | 0.0641 | 50 | 0.4677 | - |
177
+ | 0.1282 | 100 | 0.4517 | - |
178
+ | 0.1923 | 150 | 0.447 | - |
179
+ | 0.2564 | 200 | 0.4161 | - |
180
+ | 0.3205 | 250 | 0.4126 | - |
181
+ | 0.3846 | 300 | 0.3875 | - |
182
+ | 0.4487 | 350 | 0.3417 | - |
183
+ | 0.5128 | 400 | 0.308 | - |
184
+ | 0.5769 | 450 | 0.2932 | - |
185
+ | 0.6410 | 500 | 0.2789 | - |
186
+ | 0.7051 | 550 | 0.2712 | - |
187
+ | 0.7692 | 600 | 0.2653 | - |
188
+ | 0.8333 | 650 | 0.2654 | - |
189
+ | 0.8974 | 700 | 0.2578 | - |
190
+ | 0.9615 | 750 | 0.2583 | - |
191
+ | 1.0256 | 800 | 0.2569 | - |
192
+ | 1.0897 | 850 | 0.2542 | - |
193
+ | 1.1538 | 900 | 0.256 | - |
194
+ | 1.2179 | 950 | 0.25 | - |
195
+ | 1.2821 | 1000 | 0.2544 | - |
196
+ | 1.3462 | 1050 | 0.2548 | - |
197
+ | 1.4103 | 1100 | 0.2591 | - |
198
+ | 1.4744 | 1150 | 0.2654 | - |
199
+ | 1.5385 | 1200 | 0.2493 | - |
200
+ | 1.6026 | 1250 | 0.2422 | - |
201
+ | 1.6667 | 1300 | 0.2383 | - |
202
+ | 1.7308 | 1350 | 0.2355 | - |
203
+ | 1.7949 | 1400 | 0.2281 | - |
204
+ | 1.8590 | 1450 | 0.2256 | - |
205
+ | 1.9231 | 1500 | 0.2285 | - |
206
+ | 1.9872 | 1550 | 0.2211 | - |
207
+ | 2.0513 | 1600 | 0.2143 | - |
208
+ | 2.1154 | 1650 | 0.2197 | - |
209
+ | 2.1795 | 1700 | 0.2094 | - |
210
+ | 2.2436 | 1750 | 0.2076 | - |
211
+ | 2.3077 | 1800 | 0.1998 | - |
212
+ | 2.3718 | 1850 | 0.1963 | - |
213
+ | 2.4359 | 1900 | 0.1906 | - |
214
+ | 2.5 | 1950 | 0.1895 | - |
215
+ | 2.5641 | 2000 | 0.1776 | - |
216
+ | 2.6282 | 2050 | 0.1537 | - |
217
+ | 2.6923 | 2100 | 0.1414 | - |
218
+ | 2.7564 | 2150 | 0.1344 | - |
219
+ | 2.8205 | 2200 | 0.1231 | - |
220
+ | 2.8846 | 2250 | 0.1119 | - |
221
+ | 2.9487 | 2300 | 0.107 | - |
222
+ | 3.0128 | 2350 | 0.0911 | - |
223
+ | 3.0769 | 2400 | 0.0757 | - |
224
+ | 3.1410 | 2450 | 0.0708 | - |
225
+ | 3.2051 | 2500 | 0.0621 | - |
226
+ | 3.2692 | 2550 | 0.0573 | - |
227
+ | 3.3333 | 2600 | 0.0513 | - |
228
+ | 3.3974 | 2650 | 0.0405 | - |
229
+ | 3.4615 | 2700 | 0.0311 | - |
230
+ | 3.5256 | 2750 | 0.0253 | - |
231
+ | 3.5897 | 2800 | 0.0226 | - |
232
+ | 3.6538 | 2850 | 0.0139 | - |
233
+ | 3.7179 | 2900 | 0.011 | - |
234
+ | 3.7821 | 2950 | 0.0102 | - |
235
+ | 3.8462 | 3000 | 0.0076 | - |
236
+ | 3.9103 | 3050 | 0.0065 | - |
237
+ | 3.9744 | 3100 | 0.0064 | - |
238
+ | 4.0385 | 3150 | 0.0056 | - |
239
+ | 4.1026 | 3200 | 0.0054 | - |
240
+ | 4.1667 | 3250 | 0.004 | - |
241
+ | 4.2308 | 3300 | 0.0022 | - |
242
+ | 4.2949 | 3350 | 0.0019 | - |
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+ | 4.3590 | 3400 | 0.0024 | - |
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+ | 4.4231 | 3450 | 0.0018 | - |
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+ | 4.4872 | 3500 | 0.0014 | - |
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+ | 4.5513 | 3550 | 0.0005 | - |
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+ | 4.6154 | 3600 | 0.0006 | - |
248
+ | 4.6795 | 3650 | 0.0004 | - |
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+ | 4.7436 | 3700 | 0.0006 | - |
250
+ | 4.8077 | 3750 | 0.0011 | - |
251
+ | 4.8718 | 3800 | 0.0004 | - |
252
+ | 4.9359 | 3850 | 0.001 | - |
253
+ | 5.0 | 3900 | 0.0002 | - |
254
+ | 5.0641 | 3950 | 0.0002 | - |
255
+ | 5.1282 | 4000 | 0.0006 | - |
256
+ | 5.1923 | 4050 | 0.0013 | - |
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+ | 5.2564 | 4100 | 0.0009 | - |
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+ | 5.3205 | 4150 | 0.0004 | - |
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+ | 5.3846 | 4200 | 0.0001 | - |
260
+ | 5.4487 | 4250 | 0.0002 | - |
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+ | 5.5128 | 4300 | 0.0002 | - |
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+ | 5.5769 | 4350 | 0.0005 | - |
263
+ | 5.6410 | 4400 | 0.0041 | - |
264
+ | 5.7051 | 4450 | 0.0079 | - |
265
+ | 5.7692 | 4500 | 0.0071 | - |
266
+ | 5.8333 | 4550 | 0.0032 | - |
267
+ | 5.8974 | 4600 | 0.0045 | - |
268
+ | 5.9615 | 4650 | 0.0059 | - |
269
+ | 6.0256 | 4700 | 0.0066 | - |
270
+ | 6.0897 | 4750 | 0.0027 | - |
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+ | 6.1538 | 4800 | 0.0006 | - |
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+ | 6.2179 | 4850 | 0.0009 | - |
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+ | 6.2821 | 4900 | 0.0005 | - |
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+ | 6.3462 | 4950 | 0.0001 | - |
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+ | 6.4103 | 5000 | 0.0002 | - |
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+ | 6.4744 | 5050 | 0.0006 | - |
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+ | 6.5385 | 5100 | 0.0003 | - |
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+ | 6.6026 | 5150 | 0.0004 | - |
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+ | 6.6667 | 5200 | 0.0004 | - |
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+ | 6.7308 | 5250 | 0.0007 | - |
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+ | 6.7949 | 5300 | 0.0004 | - |
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+ | 6.8590 | 5350 | 0.0002 | - |
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+ | 6.9231 | 5400 | 0.0002 | - |
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+ | 6.9872 | 5450 | 0.0001 | - |
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+ | 7.0513 | 5500 | 0.0002 | - |
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+ | 7.1154 | 5550 | 0.0 | - |
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+ | 7.1795 | 5600 | 0.0002 | - |
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+ | 7.2436 | 5650 | 0.0001 | - |
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+ | 7.3077 | 5700 | 0.0001 | - |
290
+ | 7.3718 | 5750 | 0.0004 | - |
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+ | 7.4359 | 5800 | 0.0003 | - |
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+ | 7.5 | 5850 | 0.0013 | - |
293
+ | 7.5641 | 5900 | 0.0026 | - |
294
+ | 7.6282 | 5950 | 0.002 | - |
295
+ | 7.6923 | 6000 | 0.0018 | - |
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+ | 7.7564 | 6050 | 0.001 | - |
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+ | 7.8205 | 6100 | 0.002 | - |
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+ | 7.8846 | 6150 | 0.001 | - |
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+ | 7.9487 | 6200 | 0.0009 | - |
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+ | 8.0128 | 6250 | 0.0002 | - |
301
+ | 8.0769 | 6300 | 0.0 | - |
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+ | 8.1410 | 6350 | 0.0 | - |
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+ | 8.2051 | 6400 | 0.0 | - |
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+ | 8.2692 | 6450 | 0.0 | - |
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+ | 8.3333 | 6500 | 0.0 | - |
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+ | 8.3974 | 6550 | 0.0 | - |
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+ | 8.4615 | 6600 | 0.0 | - |
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+ | 8.5256 | 6650 | 0.0 | - |
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+ | 8.5897 | 6700 | 0.0 | - |
310
+ | 8.6538 | 6750 | 0.0 | - |
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+ | 8.7179 | 6800 | 0.0 | - |
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+ | 8.7821 | 6850 | 0.0 | - |
313
+ | 8.8462 | 6900 | 0.0019 | - |
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+ | 8.9103 | 6950 | 0.0018 | - |
315
+ | 8.9744 | 7000 | 0.0007 | - |
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+ | 9.0385 | 7050 | 0.001 | - |
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+ | 9.1026 | 7100 | 0.0031 | - |
318
+ | 9.1667 | 7150 | 0.0018 | - |
319
+ | 9.2308 | 7200 | 0.0014 | - |
320
+ | 9.2949 | 7250 | 0.0017 | - |
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+ | 9.3590 | 7300 | 0.0002 | - |
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+ | 9.4231 | 7350 | 0.0003 | - |
323
+ | 9.4872 | 7400 | 0.0001 | - |
324
+ | 9.5513 | 7450 | 0.0001 | - |
325
+ | 9.6154 | 7500 | 0.0002 | - |
326
+ | 9.6795 | 7550 | 0.0002 | - |
327
+ | 9.7436 | 7600 | 0.0002 | - |
328
+ | 9.8077 | 7650 | 0.0003 | - |
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+ | 9.8718 | 7700 | 0.0001 | - |
330
+ | 9.9359 | 7750 | 0.0 | - |
331
+ | 10.0 | 7800 | 0.0 | - |
332
+ | 10.0641 | 7850 | 0.0 | - |
333
+ | 10.1282 | 7900 | 0.0 | - |
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+ | 10.1923 | 7950 | 0.0 | - |
335
+ | 10.2564 | 8000 | 0.0 | - |
336
+ | 10.3205 | 8050 | 0.0 | - |
337
+ | 10.3846 | 8100 | 0.0002 | - |
338
+ | 10.4487 | 8150 | 0.0 | - |
339
+ | 10.5128 | 8200 | 0.0 | - |
340
+ | 10.5769 | 8250 | 0.0 | - |
341
+ | 10.6410 | 8300 | 0.0 | - |
342
+ | 10.7051 | 8350 | 0.0 | - |
343
+ | 10.7692 | 8400 | 0.0 | - |
344
+ | 10.8333 | 8450 | 0.0 | - |
345
+ | 10.8974 | 8500 | 0.0 | - |
346
+ | 10.9615 | 8550 | 0.0 | - |
347
+ | 11.0256 | 8600 | 0.0 | - |
348
+ | 11.0897 | 8650 | 0.0 | - |
349
+ | 11.1538 | 8700 | 0.0 | - |
350
+ | 11.2179 | 8750 | 0.0 | - |
351
+ | 11.2821 | 8800 | 0.0 | - |
352
+ | 11.3462 | 8850 | 0.0 | - |
353
+ | 11.4103 | 8900 | 0.0 | - |
354
+ | 11.4744 | 8950 | 0.0 | - |
355
+ | 11.5385 | 9000 | 0.0 | - |
356
+ | 11.6026 | 9050 | 0.0 | - |
357
+ | 11.6667 | 9100 | 0.0001 | - |
358
+ | 11.7308 | 9150 | 0.0014 | - |
359
+ | 11.7949 | 9200 | 0.0 | - |
360
+ | 11.8590 | 9250 | 0.0002 | - |
361
+ | 11.9231 | 9300 | 0.0021 | - |
362
+ | 11.9872 | 9350 | 0.0043 | - |
363
+ | 12.0513 | 9400 | 0.0054 | - |
364
+ | 12.1154 | 9450 | 0.0068 | - |
365
+ | 12.1795 | 9500 | 0.0051 | - |
366
+ | 12.2436 | 9550 | 0.0023 | - |
367
+ | 12.3077 | 9600 | 0.0007 | - |
368
+ | 12.3718 | 9650 | 0.0002 | - |
369
+ | 12.4359 | 9700 | 0.0001 | - |
370
+ | 12.5 | 9750 | 0.0 | - |
371
+ | 12.5641 | 9800 | 0.0 | - |
372
+ | 12.6282 | 9850 | 0.0006 | - |
373
+ | 12.6923 | 9900 | 0.0005 | - |
374
+ | 12.7564 | 9950 | 0.0001 | - |
375
+ | 12.8205 | 10000 | 0.0 | - |
376
+ | 12.8846 | 10050 | 0.0 | - |
377
+ | 12.9487 | 10100 | 0.0 | - |
378
+ | 13.0128 | 10150 | 0.0 | - |
379
+ | 13.0769 | 10200 | 0.0 | - |
380
+ | 13.1410 | 10250 | 0.0 | - |
381
+ | 13.2051 | 10300 | 0.0 | - |
382
+ | 13.2692 | 10350 | 0.0 | - |
383
+ | 13.3333 | 10400 | 0.0 | - |
384
+ | 13.3974 | 10450 | 0.0 | - |
385
+ | 13.4615 | 10500 | 0.0 | - |
386
+ | 13.5256 | 10550 | 0.0 | - |
387
+ | 13.5897 | 10600 | 0.0 | - |
388
+ | 13.6538 | 10650 | 0.0 | - |
389
+ | 13.7179 | 10700 | 0.0 | - |
390
+ | 13.7821 | 10750 | 0.0 | - |
391
+ | 13.8462 | 10800 | 0.0 | - |
392
+ | 13.9103 | 10850 | 0.0 | - |
393
+ | 13.9744 | 10900 | 0.0 | - |
394
+ | 14.0385 | 10950 | 0.0 | - |
395
+ | 14.1026 | 11000 | 0.0 | - |
396
+ | 14.1667 | 11050 | 0.0 | - |
397
+ | 14.2308 | 11100 | 0.0 | - |
398
+ | 14.2949 | 11150 | 0.0 | - |
399
+ | 14.3590 | 11200 | 0.0 | - |
400
+ | 14.4231 | 11250 | 0.0 | - |
401
+ | 14.4872 | 11300 | 0.0 | - |
402
+ | 14.5513 | 11350 | 0.0 | - |
403
+ | 14.6154 | 11400 | 0.0 | - |
404
+ | 14.6795 | 11450 | 0.0 | - |
405
+ | 14.7436 | 11500 | 0.0 | - |
406
+ | 14.8077 | 11550 | 0.0 | - |
407
+ | 14.8718 | 11600 | 0.0 | - |
408
+ | 14.9359 | 11650 | 0.0 | - |
409
+ | 15.0 | 11700 | 0.0 | - |
410
+ | 15.0641 | 11750 | 0.0 | - |
411
+ | 15.1282 | 11800 | 0.0 | - |
412
+ | 15.1923 | 11850 | 0.0 | - |
413
+ | 15.2564 | 11900 | 0.0 | - |
414
+ | 15.3205 | 11950 | 0.0 | - |
415
+ | 15.3846 | 12000 | 0.0 | - |
416
+ | 15.4487 | 12050 | 0.0 | - |
417
+ | 15.5128 | 12100 | 0.0 | - |
418
+ | 15.5769 | 12150 | 0.0 | - |
419
+ | 15.6410 | 12200 | 0.0 | - |
420
+ | 15.7051 | 12250 | 0.0 | - |
421
+ | 15.7692 | 12300 | 0.0 | - |
422
+ | 15.8333 | 12350 | 0.0 | - |
423
+ | 15.8974 | 12400 | 0.0 | - |
424
+ | 15.9615 | 12450 | 0.0 | - |
425
+ | 16.0256 | 12500 | 0.0 | - |
426
+ | 16.0897 | 12550 | 0.0003 | - |
427
+ | 16.1538 | 12600 | 0.0022 | - |
428
+ | 16.2179 | 12650 | 0.0041 | - |
429
+ | 16.2821 | 12700 | 0.0006 | - |
430
+ | 16.3462 | 12750 | 0.0005 | - |
431
+ | 16.4103 | 12800 | 0.0002 | - |
432
+ | 16.4744 | 12850 | 0.0003 | - |
433
+ | 16.5385 | 12900 | 0.0002 | - |
434
+ | 16.6026 | 12950 | 0.0003 | - |
435
+ | 16.6667 | 13000 | 0.0 | - |
436
+ | 16.7308 | 13050 | 0.0 | - |
437
+ | 16.7949 | 13100 | 0.0 | - |
438
+ | 16.8590 | 13150 | 0.0002 | - |
439
+ | 16.9231 | 13200 | 0.0 | - |
440
+ | 16.9872 | 13250 | 0.0 | - |
441
+ | 17.0513 | 13300 | 0.0 | - |
442
+ | 17.1154 | 13350 | 0.0 | - |
443
+ | 17.1795 | 13400 | 0.0 | - |
444
+ | 17.2436 | 13450 | 0.0 | - |
445
+ | 17.3077 | 13500 | 0.0001 | - |
446
+ | 17.3718 | 13550 | 0.0 | - |
447
+ | 17.4359 | 13600 | 0.0002 | - |
448
+ | 17.5 | 13650 | 0.0 | - |
449
+ | 17.5641 | 13700 | 0.0 | - |
450
+ | 17.6282 | 13750 | 0.0 | - |
451
+ | 17.6923 | 13800 | 0.0 | - |
452
+ | 17.7564 | 13850 | 0.0 | - |
453
+ | 17.8205 | 13900 | 0.0 | - |
454
+ | 17.8846 | 13950 | 0.0 | - |
455
+ | 17.9487 | 14000 | 0.0 | - |
456
+ | 18.0128 | 14050 | 0.0 | - |
457
+ | 18.0769 | 14100 | 0.0 | - |
458
+ | 18.1410 | 14150 | 0.0 | - |
459
+ | 18.2051 | 14200 | 0.0 | - |
460
+ | 18.2692 | 14250 | 0.0 | - |
461
+ | 18.3333 | 14300 | 0.0 | - |
462
+ | 18.3974 | 14350 | 0.0 | - |
463
+ | 18.4615 | 14400 | 0.0 | - |
464
+ | 18.5256 | 14450 | 0.0 | - |
465
+ | 18.5897 | 14500 | 0.0 | - |
466
+ | 18.6538 | 14550 | 0.0 | - |
467
+ | 18.7179 | 14600 | 0.0 | - |
468
+ | 18.7821 | 14650 | 0.0 | - |
469
+ | 18.8462 | 14700 | 0.0 | - |
470
+ | 18.9103 | 14750 | 0.0 | - |
471
+ | 18.9744 | 14800 | 0.0 | - |
472
+ | 19.0385 | 14850 | 0.0 | - |
473
+ | 19.1026 | 14900 | 0.0 | - |
474
+ | 19.1667 | 14950 | 0.0 | - |
475
+ | 19.2308 | 15000 | 0.0 | - |
476
+ | 19.2949 | 15050 | 0.0 | - |
477
+ | 19.3590 | 15100 | 0.0 | - |
478
+ | 19.4231 | 15150 | 0.0002 | - |
479
+ | 19.4872 | 15200 | 0.0 | - |
480
+ | 19.5513 | 15250 | 0.0 | - |
481
+ | 19.6154 | 15300 | 0.0 | - |
482
+ | 19.6795 | 15350 | 0.0 | - |
483
+ | 19.7436 | 15400 | 0.0 | - |
484
+ | 19.8077 | 15450 | 0.0 | - |
485
+ | 19.8718 | 15500 | 0.0002 | - |
486
+ | 19.9359 | 15550 | 0.0 | - |
487
+ | 20.0 | 15600 | 0.0 | - |
488
+ | 20.0641 | 15650 | 0.0 | - |
489
+ | 20.1282 | 15700 | 0.0 | - |
490
+ | 20.1923 | 15750 | 0.0 | - |
491
+ | 20.2564 | 15800 | 0.0 | - |
492
+ | 20.3205 | 15850 | 0.0 | - |
493
+ | 20.3846 | 15900 | 0.0 | - |
494
+ | 20.4487 | 15950 | 0.0 | - |
495
+ | 20.5128 | 16000 | 0.0 | - |
496
+ | 20.5769 | 16050 | 0.0 | - |
497
+ | 20.6410 | 16100 | 0.0 | - |
498
+ | 20.7051 | 16150 | 0.0 | - |
499
+ | 20.7692 | 16200 | 0.0 | - |
500
+ | 20.8333 | 16250 | 0.0001 | - |
501
+ | 20.8974 | 16300 | 0.0002 | - |
502
+ | 20.9615 | 16350 | 0.0001 | - |
503
+ | 21.0256 | 16400 | 0.0 | - |
504
+ | 21.0897 | 16450 | 0.0011 | - |
505
+ | 21.1538 | 16500 | 0.0009 | - |
506
+ | 21.2179 | 16550 | 0.0006 | - |
507
+ | 21.2821 | 16600 | 0.0009 | - |
508
+ | 21.3462 | 16650 | 0.0001 | - |
509
+ | 21.4103 | 16700 | 0.0 | - |
510
+ | 21.4744 | 16750 | 0.0002 | - |
511
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+
645
+ ### Framework Versions
646
+ - Python: 3.10.12
647
+ - SetFit: 1.1.0
648
+ - Sentence Transformers: 3.3.1
649
+ - Transformers: 4.44.2
650
+ - 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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+
654
+ ## Citation
655
+
656
+ ### BibTeX
657
+ ```bibtex
658
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
659
+ doi = {10.48550/ARXIV.2209.11055},
660
+ url = {https://arxiv.org/abs/2209.11055},
661
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
662
+ 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},
664
+ publisher = {arXiv},
665
+ year = {2022},
666
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
668
+ ```
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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.*
674
+ -->
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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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+
682
+ <!--
683
+ ## 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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