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mini1013
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master_item_top_bt10

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +539 -0
  3. config.json +29 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: klue/roberta-base
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: '[설화수][6월]순행클렌징오일 200ml (#M)11st>클렌징/필링>클렌징오일>클렌징오일 11st > 뷰티 > 클렌징/필링 >
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+ 클렌징오일 > 클렌징오일'
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+ - text: '[토니모리] 플로리아 브라이트닝 필링젤 (#M)GSSHOP>뷰티>클렌징>스크럽/필링 GSSHOP > 뷰티 > 클렌징 > 스크럽/필링'
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+ - text: 클린앤클리어 에센셜 포밍클렌져 150mL MinSellAmount (#M)스마일프레시 홈>바디/헤어>바디케어>바디클렌저 Gmarket
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+ > 뷰티 > 바디/헤어 > 바디케어 > 바디클렌저
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+ - text: 프레쉬 슈가 페이스 폴리쉬 125g 스크럽 125g × 1개 쿠팡 홈>생활용품>헤어/바디/세안>클렌징/필링>필링>파우더/스크럽;(#M)쿠팡
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+ 홈>뷰티>클렌징/필링>스크럽/필링>파우더/스크럽 Coupang > 뷰티 > 클렌징/필링 > 스크럽/필링 > 파우더/스크럽
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+ - text: 센카 퍼펙트 휩 콜라겐 인 클렌징 폼 120g × 100개 (#M)쿠팡 홈>싱글라이프>샤워/세안>클렌징>폼/젤/비누 Coupang >
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+ 뷰티 > 클렌징/필링 > 클렌징 폼
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+ inference: true
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+ model-index:
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+ - name: SetFit with klue/roberta-base
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.938438730374763
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with klue/roberta-base
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 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>'클렌징 버블 마유 말 폼 더블워시 세안제 마유더블워시폼 1개_케어나이트크림 1개 (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'시카 클렌징 폼 아하 세안제 클렌져 병풀 시카프라임폼 2개_마유율무스킨500 1개 (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'시카 클렌징 폼 세안제 클렌져 아침 바하. WB2838D 시카프라임폼 2개_쿨바디워시(파랑) 2개 (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li></ul> |
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+ | 2 | <ul><li>'뉴트로지나 딥클린 클렌징 오일 200ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'설화수 순행클렌징 오일200ml GM MinSellAmount 화장품/향수>클렌징/필링>클렌징오일/워터;(#M)화장품/향수>클렌징/필링>클렌징오일 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 클렌징오일'</li><li>'온더바디 라이스 화장 리무버 피지제거 클렌징오일 클렌징오일200ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li></ul> |
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+ | 5 | <ul><li>'[토니모리] 99 제비집 아이크림/그린티 클렌징티슈 1+1 9,900원 + 11번가 단독 92_프로클린 클렌징 티슈_200매 세트 11st>선케어>선크림/선블록>선크림/선블록;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st > 뷰티 > 선케어 > 선크림/선블록 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업'</li><li>'아임프롬 페어 수딩 패드 기획 (휴대용 패드 케이스 증정) [단독기획] 아임프롬 페어 수딩 패드(휴대용 패드 케이스 증정) (#M)홈>마스크팩>시트팩/패드>패드 OLIVEYOUNG > 마스크팩 > 시트팩/패드 > 패드'</li><li>'코스알엑스 원스텝 오리지널 클리어 패드 70매_토너패드,각질패드 원스텝 오리지널 클리어 패드 홈>뷰티 DAY;홈>패드 만원&시카세럼 1+1;홈>뷰티 위-크;홈>❣️9.24 오후7시~ 1+1(24시간)❣️;홈>❣️1+1❣️패드,풀핏,시카라인;홈>💗패드1+1💗 ~11.3 오후6시;홈>라이브 제품 전체보기;홈>LIVE 특가 🔥;홈>✨네이버 쇼핑 페스타🎪>⏰9,900원 뷰티데이 단 하루 특가;홈>✨네이버 쇼핑 페스타🎪>👍패드의 원조! UP TO 40% OFF!;홈>🎃해피 할로윈 깜짝 선물🎁;홈>원스텝 패드;홈>브랜드위크 SALE;홈>윈터세일 UP TO 50%;홈>패드;(#M)홈>라인별>원스텝 패드 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링'</li></ul> |
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+ | 0 | <ul><li>'아리따움 뽀오얀 미소 발효 립앤아이 리무버 250ml MinSellAmount (#M)화장품/향수>클렌징/필링>립앤아이리무버 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 립앤아이리무버'</li><li>'비파실 립 앤 아이 리무버 125ml LotteOn > 뷰티 > 클렌징 > 클렌징오일 LotteOn > 뷰티 > 럭셔리 스킨케어 > 클렌징 > 립아이리무버'</li><li>'아리따움 뽀오얀 미소 발효 립앤아이 리무버 250ml 위메프 > 뷰티 > 스킨케어 > 로션/에멀젼;위메프 > 뷰티 > 클렌징/필링 > 클렌징;(#M)위메프 > 뷰티 > 클렌징/필링 > 클렌징 > 클렌징오일 위메프 > 뷰티 > 스킨케어 > 로션/에멀젼'</li></ul> |
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+ | 4 | <ul><li>'맛사지클림 에코에니어 퓨어 아르간 리얼 클렌징 크림 300ml -O (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'이노벨라 클렌징 메이크업제거 크림 발아현미 300ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'이컬 알로에 클렌징 메이크업제거 크림 300ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li></ul> |
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+ | 1 | <ul><li>'셀리맥스 지우개 패드 60매 / 화해 어워드 1위 지우개패드 1개 홈>썸머위크🔥Big Sale;홈>[만원의 행복];홈>[50%]셀리맥스 브랜드위크;홈>만원의 행복 [패드];홈>셀리맥스 Best아이템;홈>[50%] 슈퍼위크;홈>[50%] 셀리맥스 베스트템;홈>[50%] 봄맞이 빅세일;홈>네이버 단독 특가;홈>네이버 특가;홈>1만원 패드 모음;홈>[기간한정] 6월 썸머위크;홈>패드1만원;홈>[패드] 만원의 행복;홈>[1만원] 패드;홈>패드;홈>1만원 패드;홈>[1위] 패드;홈>[화해 1위] 패드;(#M)홈>네이버Best Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링'</li><li>'아이오페 프로 필링 소프트 젤 (#M)SSG.COM/남성화장품/클렌징/쉐이빙 ssg > 뷰티 > 남성화장품 > 클렌징/쉐이빙'</li><li>'프리메라 페이셜 마일드 필링젤 150ml 얼굴각질제거제 저자극 때 제거 박피 페이스스크럽 페이셜 마일드 필링 250ml (대용량) (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링'</li></ul> |
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+ | 3 | <ul><li>'헤라 리프레싱 토닉 클렌징워터 200ML (#M)홈>화장품/미용>클렌징>클렌징워터 Naverstore > 화장품/미용 > 클렌징 > 클렌징워터'</li><li>'눅스 오 데마끼앙 미셀레르 미셀라 클렌징 워터 200ml (#M)11st>클렌징/필링>클렌징워터>클렌징워터 11st > 뷰티 > 클렌징/필링 > 클렌징워터'</li><li>'[82%+10%+T11%]토니모리 빅세일 UP TO 82%+그린티 클렌징워터 55_더 촉촉 그린티 노워시 클렌징 워터_500ml 쇼킹딜 홈>뷰티>스킨케어>스킨/로션;쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>스킨케어>스킨케어 세트>스킨케어 세트;11st>뷰티>스킨케어>스킨/로션;11st > 뷰티 > 스킨케어 > 스킨케어 세트 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</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** | 0.9384 |
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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_item_top_bt10")
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+ # Run inference
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+ preds = model("[토니모리] 플로리아 브라이트닝 필링젤 (#M)GSSHOP>뷰티>클렌징>스크럽/필링 GSSHOP > 뷰티 > 클렌징 > 스크럽/필링")
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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 | 13 | 24.4171 | 93 |
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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 | 0.4399 | - |
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+ | 0.0914 | 50 | 0.3733 | - |
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+ | 0.1828 | 100 | 0.3256 | - |
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+ | 0.2742 | 150 | 0.3157 | - |
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+ | 0.3656 | 200 | 0.3006 | - |
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+ | 0.4570 | 250 | 0.2302 | - |
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+ | 0.5484 | 300 | 0.1886 | - |
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+ | 0.6399 | 350 | 0.1366 | - |
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+ | 0.7313 | 400 | 0.1011 | - |
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+ | 0.8227 | 450 | 0.0516 | - |
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+ | 0.9141 | 500 | 0.0292 | - |
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+ | 1.0055 | 550 | 0.0129 | - |
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+ | 1.0969 | 600 | 0.0011 | - |
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+ | 1.1883 | 650 | 0.0005 | - |
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+ | 1.2797 | 700 | 0.0005 | - |
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+ | 1.3711 | 750 | 0.0004 | - |
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+ | 1.4625 | 800 | 0.0003 | - |
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+ | 1.5539 | 850 | 0.0003 | - |
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+ | 1.6453 | 900 | 0.0002 | - |
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+ | 1.7367 | 950 | 0.0002 | - |
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+ | 1.8282 | 1000 | 0.0001 | - |
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+ | 1.9196 | 1050 | 0.0001 | - |
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+ | 2.0110 | 1100 | 0.0001 | - |
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+ | 2.1024 | 1150 | 0.0001 | - |
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+ | 2.1938 | 1200 | 0.0001 | - |
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+ | 2.2852 | 1250 | 0.0001 | - |
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+ | 2.3766 | 1300 | 0.0001 | - |
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+ | 2.4680 | 1350 | 0.0 | - |
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+ | 2.5594 | 1400 | 0.0001 | - |
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+ | 2.6508 | 1450 | 0.0 | - |
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+ | 2.7422 | 1500 | 0.0 | - |
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+ | 2.8336 | 1550 | 0.0 | - |
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+ | 2.9250 | 1600 | 0.0 | - |
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+ | 3.0165 | 1650 | 0.0 | - |
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+ | 3.1079 | 1700 | 0.0 | - |
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+ | 3.1993 | 1750 | 0.0 | - |
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+ | 3.2907 | 1800 | 0.0 | - |
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+ | 3.3821 | 1850 | 0.0 | - |
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+ | 3.4735 | 1900 | 0.0 | - |
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+ | 3.5649 | 1950 | 0.0 | - |
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+ | 3.6563 | 2000 | 0.0 | - |
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+ | 3.7477 | 2050 | 0.0 | - |
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+ | 3.8391 | 2100 | 0.0 | - |
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+ | 3.9305 | 2150 | 0.0 | - |
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+ | 4.0219 | 2200 | 0.0 | - |
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+ | 4.1133 | 2250 | 0.0 | - |
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+ | 4.2048 | 2300 | 0.0 | - |
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+ | 4.2962 | 2350 | 0.0 | - |
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+ | 4.3876 | 2400 | 0.0 | - |
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+ | 4.4790 | 2450 | 0.0 | - |
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+ | 4.5704 | 2500 | 0.0 | - |
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+ | 4.6618 | 2550 | 0.0 | - |
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+ | 4.7532 | 2600 | 0.0 | - |
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+ | 4.8446 | 2650 | 0.0 | - |
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+ | 4.9360 | 2700 | 0.0 | - |
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+ | 5.0274 | 2750 | 0.0 | - |
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+ | 5.1188 | 2800 | 0.0 | - |
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+ | 5.2102 | 2850 | 0.0 | - |
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+ | 5.3016 | 2900 | 0.0 | - |
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+ | 5.3931 | 2950 | 0.0 | - |
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+ | 5.4845 | 3000 | 0.0 | - |
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+ | 5.5759 | 3050 | 0.0 | - |
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+ | 5.6673 | 3100 | 0.0 | - |
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+ | 5.9415 | 3250 | 0.0 | - |
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+ | 6.0329 | 3300 | 0.0 | - |
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+ | 6.1243 | 3350 | 0.0 | - |
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+ | 6.2157 | 3400 | 0.0 | - |
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+ | 6.3071 | 3450 | 0.0 | - |
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+ | 6.3985 | 3500 | 0.0 | - |
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+ | 6.6728 | 3650 | 0.0 | - |
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+ | 6.8556 | 3750 | 0.0 | - |
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+ | 6.9470 | 3800 | 0.0 | - |
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+ | 7.0384 | 3850 | 0.0 | - |
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+ | 7.1298 | 3900 | 0.0 | - |
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+ | 7.2212 | 3950 | 0.0 | - |
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+ | 7.3126 | 4000 | 0.0 | - |
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+ | 7.4040 | 4050 | 0.0051 | - |
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+ | 7.4954 | 4100 | 0.0091 | - |
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+ | 7.5868 | 4150 | 0.0032 | - |
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+ | 7.6782 | 4200 | 0.0014 | - |
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+ | 7.7697 | 4250 | 0.0 | - |
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+ | 7.8611 | 4300 | 0.0 | - |
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+ | 7.9525 | 4350 | 0.0 | - |
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+ | 8.0439 | 4400 | 0.0 | - |
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+ | 8.3181 | 4550 | 0.0 | - |
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+ | 8.4095 | 4600 | 0.0 | - |
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+ | 8.8665 | 4850 | 0.0 | - |
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+ | 11.0603 | 6050 | 0.0 | - |
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+ | 15.9963 | 8750 | 0.0 | - |
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+ | 16.0878 | 8800 | 0.0 | - |
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+ | 16.1792 | 8850 | 0.0 | - |
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+ | 17.6417 | 9650 | 0.0 | - |
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+ | 19.1956 | 10500 | 0.0 | - |
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+ | 21.3894 | 11700 | 0.0001 | - |
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+ | 21.4808 | 11750 | 0.0004 | - |
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+ | 21.5722 | 11800 | 0.0 | - |
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+ | 21.6636 | 11850 | 0.0 | - |
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+ | 21.7550 | 11900 | 0.0 | - |
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+ | 21.8464 | 11950 | 0.0002 | - |
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+ | 21.9378 | 12000 | 0.0002 | - |
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+ | 22.0293 | 12050 | 0.0 | - |
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+ | 22.1207 | 12100 | 0.0 | - |
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+ | 22.2121 | 12150 | 0.0 | - |
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+ | 22.3035 | 12200 | 0.0 | - |
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+ | 22.4863 | 12300 | 0.0 | - |
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+ | 22.5777 | 12350 | 0.0 | - |
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+ | 26.2340 | 14350 | 0.0 | - |
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+ | 26.3254 | 14400 | 0.0 | - |
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+ | 26.4168 | 14450 | 0.0 | - |
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+ | 26.5996 | 14550 | 0.0 | - |
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+ | 26.6910 | 14600 | 0.0 | - |
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+ | 26.7824 | 14650 | 0.0 | - |
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+ | 26.8739 | 14700 | 0.0 | - |
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+ | 26.9653 | 14750 | 0.0 | - |
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+ | 27.1481 | 14850 | 0.0 | - |
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+ | 27.2395 | 14900 | 0.0 | - |
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+ | 27.3309 | 14950 | 0.0 | - |
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+ | 27.4223 | 15000 | 0.0 | - |
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+ | 27.5137 | 15050 | 0.0 | - |
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+ | 27.6965 | 15150 | 0.0 | - |
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+ | 27.7879 | 15200 | 0.0 | - |
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+ | 27.8793 | 15250 | 0.0 | - |
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+ | 27.9707 | 15300 | 0.0 | - |
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+ | 28.0622 | 15350 | 0.0 | - |
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+ | 28.1536 | 15400 | 0.0 | - |
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+ | 28.2450 | 15450 | 0.0 | - |
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+ | 28.3364 | 15500 | 0.0 | - |
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+ | 28.4278 | 15550 | 0.0 | - |
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+ | 28.5192 | 15600 | 0.0 | - |
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+ | 28.6106 | 15650 | 0.0 | - |
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+ | 28.7020 | 15700 | 0.0 | - |
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+ | 28.7934 | 15750 | 0.0 | - |
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+ | 28.8848 | 15800 | 0.0 | - |
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+ | 28.9762 | 15850 | 0.0 | - |
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+ | 29.0676 | 15900 | 0.0 | - |
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+ | 29.1590 | 15950 | 0.0 | - |
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+ | 29.2505 | 16000 | 0.0 | - |
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+ | 29.3419 | 16050 | 0.0 | - |
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+ | 29.4333 | 16100 | 0.0 | - |
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+ | 29.5247 | 16150 | 0.0 | - |
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+ | 29.6161 | 16200 | 0.0 | - |
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+ | 29.7075 | 16250 | 0.0 | - |
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+ | 29.7989 | 16300 | 0.0 | - |
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+ | 29.8903 | 16350 | 0.0 | - |
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+ | 29.9817 | 16400 | 0.0 | - |
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+
498
+ ### Framework Versions
499
+ - Python: 3.10.12
500
+ - SetFit: 1.1.0
501
+ - Sentence Transformers: 3.3.1
502
+ - Transformers: 4.44.2
503
+ - PyTorch: 2.2.0a0+81ea7a4
504
+ - Datasets: 3.2.0
505
+ - Tokenizers: 0.19.1
506
+
507
+ ## Citation
508
+
509
+ ### BibTeX
510
+ ```bibtex
511
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
512
+ doi = {10.48550/ARXIV.2209.11055},
513
+ url = {https://arxiv.org/abs/2209.11055},
514
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
515
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
516
+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+ ## Glossary
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+
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+ -->
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+
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+ <!--
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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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