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mini1013
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master_cate_bt_top12_test

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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  1. 1_Pooling/config.json +10 -0
  2. README.md +541 -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: 폴미첼 프리즈 앤 샤인 슈퍼 스프레이 250ml x 3개 단일상품 (#M)11st>헤어케어>헤어스프레이>헤어스프레이 11st > 뷰티
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+ > 헤어케어 > 헤어스프레이
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+ - text: 댕기머리 뉴골드 새치머리용 한방 칼라 크림 염색약 염색제 5호 4개 Coupang > 뷰티 > 헤어 > 염색/파마 > 염색/헤어컬러
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+ > 한방염색제(새치);(#M)쿠팡 홈>뷰티>헤어>염색/파마>염색/헤어컬러>한방염색제(새치) Coupang > 뷰티 > 헤어 > 염색/파마 >
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+ 염색/헤어컬러 > 한방염색제(새치)
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+ - text: 갸스비 스타일링 왁스 익사이팅 하드 80g MinSellAmount (#M)바디/헤어>헤어스타일링>헤어왁스 Gmarket > 뷰티 >
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+ 바디/헤어 > 헤어스타일링 > 헤어왁스
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+ - text: 이지엔 쉐이킹 푸딩 헤어칼라 6.61(멋내기용-스모키 애쉬블러썸) (#M)홈>헤어케어>염색약>멋내기 염색 Naverstore > 화장품/미용
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+ > 헤어스타일링 > 염색약
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+ - text: '[웰라] 아이미 앱솔루트 셋 300ml LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이 LotteOn > 뷰티
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+ > 헤어/바디 > 헤어스타일링 > 헤어스프레이'
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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: 0.8061538461538461
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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>'이희 마블 에센스 헤어 팩트 블랙 본품14g / 흑채 상세페이지 참조 (#M)쿠팡 홈>뷰티>헤어>염색/파마>헤어메이크업>흑채/증모제 Coupang > 뷰티 > 헤어 > 염색/파마 > 헤어메이크업 > 흑채/증모제'</li><li>'[TS]리뉴얼 흑채23g/식물성펄프사용,순간증모제,대머리커버 검정 (#M)GSSHOP>뷰티>헤어케어>탈모케어 GSSHOP > 뷰티 > 헤어케어 > 탈모케어'</li><li>'이희 마블 에센스 헤어 팩트 다크브라운 본품1+리필2 (#M)홈>화장품/미용>헤어케어>���모케어 Naverstore > 화장품/미용 > 헤어케어 > 탈모케어'</li></ul> |
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+ | 2 | <ul><li>'아모스 아미노 매직 셋팅 펌 / 노멀 / 다운펌 / 스트레이트 매직약 1제노멀 2제 크림타입 (#M)홈>화장품/미용>헤어스타일링>파마약>스트레이트 Naverstore > 화장품/미용 > 헤어스타일링 > 파마약 > 스트레이트'</li><li>'아모스 아미노 매직셋팅펌 노멀 400ml+크림 500ml (#M)11st>헤어케어>파마약>파마약 11st > 뷰티 > 헤어케어 > 파마약 > 파마약'</li><li>'[눙크] 듀이트리 굿 릴랙스 애프터 수딩 패치 15g ssg > 뷰티 > 스킨케어 > 마스크/팩 > 아이패치 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 아이패치'</li></ul> |
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+ | 5 | <ul><li>'(2개)꽃을든남자 케라틴 실크프로테인 헤어젤 500ml MinSellAmount (#M)바디/헤어>헤어스타일링>헤어젤 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 헤어젤'</li><li>'아베다 맨 퓨어 포먼스 그루밍 클레이 75ml (#M)11st>헤어케어>샴푸>일반 11st > 뷰티 > 헤어케어 > 샴푸 > 일반'</li><li>'아베다 - 맨 퓨어 포맨스 리퀴드 포마드 200ml/6.7oz ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어앰플 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 헤어앰플'</li></ul> |
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+ | 0 | <ul><li>'로레알 뉴앙셀 보태닉 헤어 매니큐어 150g 왁싱 코팅 내츄럴브라운다크 (#M)11st>헤어케어>염색약>크림염색약 11st > 뷰티 > 헤어케어 > 염색약 > 크림염색약'</li><li>'사이오스 NEW 올레오 크림 프리미엄 헤어 컬러 1B 크리스탈베이지 3개 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'로레알파리 매직 리터치 75 ml1433989 10 브라운 x 5개143398910 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어'</li></ul> |
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+ | 4 | <ul><li>'핫템 미장센 스타일케어 헤어스프레이 300ml - O 화이트플로럴 (#M)SSG.COM/스킨케어/클렌징/클렌징오일 ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징오일'</li><li>'비레디 에어리 헤어 스프레이 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 컬크림 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 컬크림'</li><li>'[밀본] 밀본 니제르 홀드핏 베일 180g (#M)홈>헤어 스타일링 Naverstore > 화장품/미용 > 헤어스타일링 > 헤어스프레이'</li></ul> |
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+ | 1 | <ul><li>'컬링 에센스 150ml 1+1 (#M)뷰티>헤어/바디/미용기기>헤어케어>에센스/앰플/오일 CJmall > 뷰티 > 헤어/바디/미용기기 > 헤어케어 > 트리트먼트/팩/마스크'</li><li>'미쟝센 스테이지 컬링에센스 2X 150ml 1개 01_미쟝센 스테이지 컬링에센스 2X 150ml 1개 위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트;위메프 > 뷰티 > 바디/헤어 > 헤어염색/파마/왁스 > 염색약;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 바디워시/스크럽'</li><li>'[현대백화점][VDL] 스킨프로 미스트 아쿠아 밤 브이디엘 스킨프로 미스트 아쿠아 밤 (#M)홈>화장품/미용>베이스메이크업>베이스메이크업세트 Naverstore > 화장품/미용 > 베이스메이크업 > 베이스메이크업세트'</li></ul> |
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+ | 3 | <ul><li>'리르 퀵 헤어 커버 쿠션 빈모 새치커버 [0006]헤어쿠션_브라운 (#M)11st>헤어케어>탈모/두피관리제>클리닉용품 기타 11st > 뷰티 > 헤어케어 > 탈모/두피관리제'</li><li>'[12월기획]비레디 헤어라인 커버스틱 다크브라운 (#M)GSSHOP>뷰티>스킨케어>스킨케어세트 GSSHOP > 뷰티 > 스킨케어 > 뷰티 합포장'</li><li>'마몽드 팡팡 헤어 섀도우 - 3.5g/택1 3호 레디쉬 브라운 화장품/향수>립케어/블러셔>블러셔/볼터치;(#M)화장품/향수>색조메이크업>블러셔 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 블러셔'</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** | 0.8062 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_cate_bt_top12_test")
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+ # Run inference
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+ preds = model("폴미첼 프리즈 앤 샤인 슈퍼 스프레이 250ml x 3개 단일상품 (#M)11st>헤어케어>헤어스프레이>헤어스프레이 11st > 뷰티 > 헤어케어 > 헤어스프레이")
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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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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 11 | 21.3486 | 60 |
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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 | 0.4121 | - |
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+ | 0.0914 | 50 | 0.4364 | - |
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+ | 0.1828 | 100 | 0.4222 | - |
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+ | 0.2742 | 150 | 0.395 | - |
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+ | 0.3656 | 200 | 0.3538 | - |
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+ | 0.4570 | 250 | 0.3385 | - |
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+ | 0.5484 | 300 | 0.3178 | - |
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+ | 0.6399 | 350 | 0.2704 | - |
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+ | 0.7313 | 400 | 0.2307 | - |
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+ | 0.8227 | 450 | 0.1968 | - |
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+ | 0.9141 | 500 | 0.1908 | - |
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+ | 1.0055 | 550 | 0.1629 | - |
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+ | 1.0969 | 600 | 0.1568 | - |
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+ | 1.1883 | 650 | 0.1495 | - |
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+ | 1.2797 | 700 | 0.1378 | - |
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+ | 1.3711 | 750 | 0.1215 | - |
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+ | 1.4625 | 800 | 0.0961 | - |
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+ | 1.5539 | 850 | 0.0704 | - |
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+ | 1.6453 | 900 | 0.0564 | - |
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+ | 1.7367 | 950 | 0.0565 | - |
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+ | 1.8282 | 1000 | 0.0549 | - |
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+ | 1.9196 | 1050 | 0.0489 | - |
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+ | 2.0110 | 1100 | 0.0484 | - |
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+ | 2.1024 | 1150 | 0.0471 | - |
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+ | 2.1938 | 1200 | 0.042 | - |
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+ | 2.2852 | 1250 | 0.0456 | - |
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+ | 2.3766 | 1300 | 0.0329 | - |
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+
500
+ ### Framework Versions
501
+ - Python: 3.10.12
502
+ - SetFit: 1.1.0
503
+ - Sentence Transformers: 3.3.1
504
+ - Transformers: 4.44.2
505
+ - PyTorch: 2.2.0a0+81ea7a4
506
+ - Datasets: 3.2.0
507
+ - Tokenizers: 0.19.1
508
+
509
+ ## Citation
510
+
511
+ ### BibTeX
512
+ ```bibtex
513
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
514
+ doi = {10.48550/ARXIV.2209.11055},
515
+ url = {https://arxiv.org/abs/2209.11055},
516
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
517
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
518
+ title = {Efficient Few-Shot Learning Without Prompts},
519
+ publisher = {arXiv},
520
+ year = {2022},
521
+ copyright = {Creative Commons Attribution 4.0 International}
522
+ }
523
+ ```
524
+
525
+ <!--
526
+ ## Glossary
527
+
528
+ *Clearly define terms in order to be accessible across audiences.*
529
+ -->
530
+
531
+ <!--
532
+ ## Model Card Authors
533
+
534
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
535
+ -->
536
+
537
+ <!--
538
+ ## Model Card Contact
539
+
540
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
541
+ -->
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@@ -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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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "mask_token": {
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+ "content": "[MASK]",
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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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+ "pad_token": {
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+ "content": "[PAD]",
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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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+ "sep_token": {
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+ "content": "[SEP]",
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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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+ "unk_token": {
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+ "content": "[UNK]",
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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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+ },
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+ "content": "[PAD]",
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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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+ "special": true
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+ },
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+ "2": {
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+ "content": "[SEP]",
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "3": {
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+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "4": {
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+ "content": "[MASK]",
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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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",
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+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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