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mini1013
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master_cate_bt_top5_test

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +540 -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
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -0,0 +1,540 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: '[7월/롯데단독] 엉크르 드 뽀 쿠션 리필 듀오 세트(+립 미니어처+파데5ml) 20호_35호 LotteOn > 백화점 > 뷰티 >
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+ 상단 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'
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+ - text: '[기획]블랙쿠션 리뉴얼 리필 듀오 21N1_23N1 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn
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+ > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'
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+ - text: 랑콤 비비크림 spf50 50ml 0.1kg 1팩 솔에일 브론저 선 비비 선 (#M)SSG.COM/헤어/바디/세정/입욕용품/비누 ssg
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+ > 뷰티 > 헤어/바디 > 세정/입욕용품 > 비누
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+ - text: (1+1) 더샘 커버 퍼펙션 팟 컨실러 4g (당일발송) MinSellAmount (#M)화장품/향수>베이스메이크업>컨실러 Gmarket
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+ > 뷰티 > 화장품/향수 > 베이스메이크업 > 컨실러
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+ - text: 헤라 메이크업픽서 110ml × 4개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>메이크업픽서 Coupang > 뷰티 > 메이크업
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+ > 베이스 메이크업 > 메이크업픽서
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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: 0.6730190571715146
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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:** 7 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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+ | 6 | <ul><li>'글램 업 메이크업 픽서 100ml 글램 업 메이크업 하이라이터 7g LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'달바 블랑 드 런웨이 올데이 세럼 메이크업 픽서 80ml × 1개 LotteOn > 뷰티 > 메이크업 > 하이라이터 LotteOn > 뷰티 > 메이크업 > 하이라이터'</li><li>'메이블린 마스터 픽서 메이크업 리무버 펜 3ml ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징워터 ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징워터'</li></ul> |
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+ | 2 | <ul><li>'데이지크 프로 컨실러 팔레트 9g 01 커버 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림'</li><li>'블레미쉬커버 퍼프 7매입 세트 (#M)뷰티>화장품/향수>미용소품>퍼프/스폰지/브러쉬 CJmall > 뷰티 > 화장품/향수 > 미용소품 > 퍼프/스폰지/브러쉬'</li><li>'동성제약 메디커버 DHA 펜 10퍼센트(보통 피부색) (#M)11st>메이크업>페이스메이크업>메이크업베이스 11st > 뷰티 > 메이크업 > 페이스메이크업 > 메이크업베이스'</li></ul> |
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+ | 5 | <ul><li>'헤라 하이드레이팅 래디언스 프라이머 35ml (#M)11st>남성화장품>남성크림>남성크림 11st > 뷰티 > 남성화장품 > 남성크림 > 남성크림'</li><li>'맥 라이트풀 C+ 코랄 그라스 틴티드 프라이머 (#M)11st>메이크업>페이스메이크업>프라이머 11st > 뷰티 > 메이크업 > 페이스메이크업 > 프라이머'</li><li>'코드글로컬러 엠.하이드로 프라이머 기획 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 아이섀도우 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 아이섀도우'</li></ul> |
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+ | 0 | <ul><li>'스웨거 페이스 터미네이터 올인원 비비 크림 SPF 50+ PA+++ 스웨거 페이스 터미네이터 × 1개 (#M)쿠팡 홈>뷰티>남성화장품>남성메이크업>베이스메이크업 Coupang > 뷰티 > 남성화장품 > 남성메이크업 > 베이스메이크업'</li><li>'인셀덤 원빈화장품 엑티브크림EX 외 전제품 선택 데일리 아쿠아 비비크림30g (#M)홈>화장품/미용>스킨케어>화장품세트 Naverstore > 화장품/미용 > 스킨케어 > 화장품세트'</li><li>'AHC 프리미엄 인텐스 컨튜어밤 10ml 2개 MinSellAmount (#M)스마일배송 홈>뷰티>메이크업/선케어>베이스메이크업 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > BB크림/톤업크림'</li></ul> |
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+ | 4 | <ul><li>'[SSG-단독]블랙쿠션 리뉴얼 리필 듀오 17N1_17C1 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션;ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션파운데이션;신세계백화점/메이크업/베이스메이크업/쿠션파운데이션;(#M)SSG.COM/메이크업/베이스메이크업/쿠션파운데이션 ssg > 뷰티 > 메이크업 > 립메이크업'</li><li>'라네즈 맨즈 그루밍 네오 쿠션 매트 SPF42 PA++ 15g (옵션)+남성크림스킨 25ml 4개 증정 23C 쿨 샌드 (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션'</li><li>'아이오페 에어쿠션 스킨핏 톤업 15g x 2 단일상품 (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션'</li></ul> |
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+ | 1 | <ul><li>'[특별] 톤업 프라이머 쿠션 세트 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 프라이머 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 프라이머'</li><li>'[한스킨] 핑크물밤(톤업팩트) 2개 세트 본품*2_[B0008449] (#M)11st>메이크업>페이스메이크업>파운데이션 11st > 뷰티 > 메이크업 > 페이스메이크업 > 파운데이션'</li><li>'[2특별] NEW 래디언트 파운데이션 세트(+컨실러 정품+키트 2종) 110 알라바스터 LotteOn > 뷰티 > 명품화장품 > 메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li></ul> |
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+ | 3 | <ul><li>'[본사직영] 래디언스 팩트 SPF27/PA++ (바닐라) 위메프 > 뷰티 > 스킨케어 > 스킨/토너;위메프 > 뷰티 > 메이크업 > 립 메이크업;위메프 > 뷰티 > 메이크업 > 립 메이크업 > 립글로즈;위메프 > 뷰티 > 선케어 > 선크림/선블록 > 선크림/선블록;위메프 > 뷰티 > 메이크업 > 베이스 메이크업;(#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 쿠션팩트 위메프 > 뷰티 > 선케어 > 선크림/선블록'</li><li>'[AK PLAZA][DIOR] 캡춰 토탈 퍼펙션 앤 유쓰 래디언스 루스 파우더 단일상품 (#M)홈>화장품/미용>향수>향수세트 Naverstore > 화장품/미용 > 향수 > 향수세트'</li><li>'설화수 NEW 진설파우더팩트 리필 23N1 (#M)화장품/향수>스킨케어>에센스/세럼 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼'</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** | 0.6730 |
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+
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+ ## Uses
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+
87
+ ### Direct Use for Inference
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+
89
+ First install the SetFit library:
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+
91
+ ```bash
92
+ pip install setfit
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+ ```
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+
95
+ Then you can load this model and run inference.
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+
97
+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_cate_bt_top5_test")
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+ # Run inference
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+ preds = model("헤라 메이크업픽서 110ml × 4개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>메이크업픽서 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 메이크업픽서")
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+ ```
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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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+
130
+ ## Training Details
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+
132
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 12 | 24.3657 | 87 |
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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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+
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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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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0018 | 1 | 0.4623 | - |
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+ | 0.0914 | 50 | 0.4618 | - |
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+ | 0.1828 | 100 | 0.4384 | - |
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+ | 0.2742 | 150 | 0.4275 | - |
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+ | 0.3656 | 200 | 0.3889 | - |
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+ | 0.4570 | 250 | 0.3422 | - |
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+ | 0.5484 | 300 | 0.3055 | - |
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+ | 0.6399 | 350 | 0.2795 | - |
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+ | 0.7313 | 400 | 0.2616 | - |
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+ | 0.8227 | 450 | 0.252 | - |
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+ | 0.9141 | 500 | 0.2394 | - |
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+ | 1.0055 | 550 | 0.2274 | - |
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+ | 1.0969 | 600 | 0.2154 | - |
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+ | 1.1883 | 650 | 0.2031 | - |
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+ | 1.2797 | 700 | 0.197 | - |
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+ | 1.3711 | 750 | 0.1768 | - |
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+ | 1.4625 | 800 | 0.1752 | - |
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+ | 1.5539 | 850 | 0.1631 | - |
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+ | 1.6453 | 900 | 0.1513 | - |
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+ | 1.7367 | 950 | 0.1368 | - |
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+ | 1.8282 | 1000 | 0.1354 | - |
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+ | 1.9196 | 1050 | 0.1235 | - |
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+ | 2.0110 | 1100 | 0.1113 | - |
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+
499
+ ### Framework Versions
500
+ - Python: 3.10.12
501
+ - SetFit: 1.1.0
502
+ - Sentence Transformers: 3.3.1
503
+ - Transformers: 4.44.2
504
+ - PyTorch: 2.2.0a0+81ea7a4
505
+ - Datasets: 3.2.0
506
+ - Tokenizers: 0.19.1
507
+
508
+ ## Citation
509
+
510
+ ### BibTeX
511
+ ```bibtex
512
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
513
+ doi = {10.48550/ARXIV.2209.11055},
514
+ url = {https://arxiv.org/abs/2209.11055},
515
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
516
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
517
+ title = {Efficient Few-Shot Learning Without Prompts},
518
+ publisher = {arXiv},
519
+ year = {2022},
520
+ copyright = {Creative Commons Attribution 4.0 International}
521
+ }
522
+ ```
523
+
524
+ <!--
525
+ ## Glossary
526
+
527
+ *Clearly define terms in order to be accessible across audiences.*
528
+ -->
529
+
530
+ <!--
531
+ ## Model Card Authors
532
+
533
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
534
+ -->
535
+
536
+ <!--
537
+ ## Model Card Contact
538
+
539
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
540
+ -->
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@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "bos_token": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "cls_token": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "eos_token": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "single_word": false
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+ "mask_token": {
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "single_word": false
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+ },
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+ "sep_token": {
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+ "normalized": false,
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[CLS]",
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+ "special": true
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+ "2": {
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+ },
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+ "4": {
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+ "special": true
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+ }
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+ },
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+ "bos_token": "[CLS]",
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": false,
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+ "eos_token": "[SEP]",
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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