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mini1013
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master_cate_bt_top6_test

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 +542 -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: 에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품
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+ > 화장솜/면봉
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+ - text: 트위저맨 슬랜트 트위저 족집게 로즈골드 × 1개 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn >
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+ 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프
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+ - text: 타투스티커 바디형 문신스티커 헤나 레터링 흉터커버 쇄골 반팔 J type 타투스티커 30종세트 LotteOn > 뷰티 > 뷰티기기/소품
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+ > 메이크업소품 > 헤나/타투 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 헤나/타투
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+ - text: 더툴랩 215 피니쉬 컨실러 파운데이션 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn >
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+ 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬
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+ - text: 에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품
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+ > 화장솜/면봉
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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: 0.8526100307062436
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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:** 8 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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+ | 7 | <ul><li>'모델링팩 제조 셀프 피부관리 용품 세트 스파츌러 할로윈분장 미용기구 분홍색 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스 메이크업 세트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스 메이크업 세트'</li><li>'프린시아 공용기 로션통 30g 옵션없음 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품 ssg > 뷰티 > 미용기기/소품 > 아이소품 > 인조속눈썹'</li><li>'[텐바이텐] 입생로랑 유광 레드 프레스티지 파우치 옵션선택_옵션선택 (#M)쿠팡 홈>뷰티>남성화장품>남성 쉐이빙 케어>애프터쉐이브 스킨/로션/크림 Coupang > 뷰티 > 남성화장품 > 남성 쉐이빙 케어 > 애프터쉐이브 스킨/로션/크림'</li></ul> |
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+ | 3 | <ul><li>'토니모리 아이래쉬 컬러_동수원점_동수원점 아이래쉬 컬러 (#M)SSG.COM/메이크업/아이메이크업/아이섀도우/글리터/팔레트 ssg > 뷰티 > 메이크업 > 아이메이크업'</li><li>'아리따움 아이돌 래쉬 프리미엄 9호리얼핏 (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'트위저맨 쁘띠 트위즈 족집게 세트 실버 × 1세트 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함'</li></ul> |
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+ | 6 | <ul><li>'토니모리 뿌리 볼륨 헤어 집게 (#M)쿠팡 홈>뷰티>헤어>헤어스타일링>헤어왁스 Coupang > 뷰티 > 로드샵 > 헤어 > 헤어스타일링 > 헤어왁스'</li><li>'갤리포니아 미니 갤리포니아 미니 ssg > 뷰티 > 메이크업 > 치크메이크업;ssg > 뷰티 > 메이크업 > 립메이크업 > 립밤 ssg > 뷰티 > 메이크업 > 립메이크업'</li><li>'보다나 두피케어 샴푸 브러쉬 보다나 두피케어 샴푸 브러쉬 홈>미용소품>헤어소품>헤어브러시;(#M)홈>헤어케어>헤어브러쉬>두피용 OLIVEYOUNG > 미용소품 > 헤어/바디 > 헤어브러시'</li></ul> |
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+ | 0 | <ul><li>'천연 자초 립밤 만들기 키트 diy 향 선택(8개) 사과+에탄올20ml (#M)홈>비누&립밤&세제 만들기>만들기키트 Naverstore > 화장품/미용 > 색조메이크업 > 립케어'</li></ul> |
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+ | 5 | <ul><li>'헤라 블랙 쿠션 제로 비티 핏 퍼프 2입 파워풀한 핏팅력 균일한 밀착 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프'</li><li>'[티타늄] 더마 MTS 롤러 헤어 두피 540 0.25mm 0.5mm 니들 앰플 티타늄_고급형_0.75mm 홈>화장품/미용>뷰티소품>페이스소품>마사지도구;홈>전체상품;(#M)홈>MTS 도구 Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 마사지도구'</li><li>'헤라 블랙 쿠션 퍼프 x 10개/설화수 쿠션 퍼프 x 10개 헤라블랙쿠션퍼프 x 5개 (#M)홈>화장품/미용>뷰티소품>페이스소품>퍼프 Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 퍼프'</li></ul> |
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+ | 1 | <ul><li>'[단품구매] 해피림 아이블랜딩 세트 5종 (10%할인) 235 펜슬 브러쉬 (#M)화장품/미용>뷰티소품>메이크업브러시>아이브러시 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 아이브러시'</li><li>'[안씨브러쉬] 여행용 블러셔, 파우더 브러쉬 - VELVET04 (맑은발색) 홈>라인별>(new) VELVET (Premium);(#M)홈>라인별>VELVET (Premium) Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 페이스브러시'</li><li>'[안씨브러쉬] 스몰 아이섀도, 블렌딩 아이섀도 브러쉬 - Eve316 (#M)홈>용도별>아이메이크업>스몰 - 메인컬러, 중간컬러 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 아이브러시'</li></ul> |
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+ | 2 | <ul><li>'바버샵 커트보 미용실 가운 컷트보 드라이보 파마보 염색보 넥셔터 03. 바버샵 그린 스트라이프 홈>전체상품;(#M)홈>커트보,앞치마 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 기타헤어소품'</li><li>'_ [!BEST_쿠_팡_픽!] _ 롱바디 브러시 각도 조절 가능 등브러쉬 목욕 용품샤워타올목욕용품 _ 5F9AD0 _ 00000EA_goldspo_on mall ★수저픽★ 베이지_JW (#M)쿠팡 홈>생활용품>헤어/바디/세안>샤워/입욕용품>입욕제>바스밤 Coupang > 뷰티 > 바디 > 샤워/입욕용품 > 입욕제'</li><li>'롱바디 브러시 각도 조절 가능 등브러쉬 목욕 용품 SQ+6242EA 밀키 블루 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샤워/입욕용품>입욕제>바스밤 Coupang > 뷰티 > 바디 > 샤워/입욕용품 > 입욕제'</li></ul> |
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+ | 4 | <ul><li>'타투바늘 DiRK 더크 카트리지 니들 라이너, 매그넘, 쉐더 반영구 라운드 매그넘_1211 (#M)홈>화장품/미용>뷰티소품>타투 Naverstore > 화장품/미용 > 뷰티소품 > 타투'</li><li>'[스킨알엑스] [타투미] 브레이슬릿 Chandelier Bracelet LotteOn > 뷰티 > 바디케어 > 바디케어세트 LotteOn > 뷰티 > 바디케어 > 바디케어세트'</li><li>'5초눈썹타투스티커5초11쌍 눈썹문신스티커 눈썹타투 눈썹 E14 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</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** | 0.8526 |
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+
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+ ## Uses
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+
88
+ ### Direct Use for Inference
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+
90
+ First install the SetFit library:
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+
92
+ ```bash
93
+ pip install setfit
94
+ ```
95
+
96
+ Then you can load this model and run inference.
97
+
98
+ ```python
99
+ from setfit import SetFitModel
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+
101
+ # Download from the 🤗 Hub
102
+ model = SetFitModel.from_pretrained("mini1013/master_cate_bt_top6_test")
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+ # Run inference
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+ preds = model("에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품 > 화장솜/면봉")
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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 | 20.66 | 66 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 1 |
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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 | 49 |
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+ | 7 | 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 | 0.4049 | - |
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+ | 0.0914 | 50 | 0.4426 | - |
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+ | 0.1828 | 100 | 0.4367 | - |
174
+ | 0.2742 | 150 | 0.4123 | - |
175
+ | 0.3656 | 200 | 0.3927 | - |
176
+ | 0.4570 | 250 | 0.3631 | - |
177
+ | 0.5484 | 300 | 0.3095 | - |
178
+ | 0.6399 | 350 | 0.2743 | - |
179
+ | 0.7313 | 400 | 0.2444 | - |
180
+ | 0.8227 | 450 | 0.2342 | - |
181
+ | 0.9141 | 500 | 0.2188 | - |
182
+ | 1.0055 | 550 | 0.2089 | - |
183
+ | 1.0969 | 600 | 0.1942 | - |
184
+ | 1.1883 | 650 | 0.1751 | - |
185
+ | 1.2797 | 700 | 0.1564 | - |
186
+ | 1.3711 | 750 | 0.1525 | - |
187
+ | 1.4625 | 800 | 0.1342 | - |
188
+ | 1.5539 | 850 | 0.1252 | - |
189
+ | 1.6453 | 900 | 0.1124 | - |
190
+ | 1.7367 | 950 | 0.1022 | - |
191
+ | 1.8282 | 1000 | 0.0877 | - |
192
+ | 1.9196 | 1050 | 0.0611 | - |
193
+ | 2.0110 | 1100 | 0.0447 | - |
194
+ | 2.1024 | 1150 | 0.0353 | - |
195
+ | 2.1938 | 1200 | 0.0305 | - |
196
+ | 2.2852 | 1250 | 0.0321 | - |
197
+ | 2.3766 | 1300 | 0.0299 | - |
198
+ | 2.4680 | 1350 | 0.0292 | - |
199
+ | 2.5594 | 1400 | 0.0307 | - |
200
+ | 2.6508 | 1450 | 0.0328 | - |
201
+ | 2.7422 | 1500 | 0.0277 | - |
202
+ | 2.8336 | 1550 | 0.0226 | - |
203
+ | 2.9250 | 1600 | 0.0103 | - |
204
+ | 3.0165 | 1650 | 0.007 | - |
205
+ | 3.1079 | 1700 | 0.0024 | - |
206
+ | 3.1993 | 1750 | 0.0012 | - |
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+ | 3.2907 | 1800 | 0.0012 | - |
208
+ | 3.3821 | 1850 | 0.0007 | - |
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+ | 3.4735 | 1900 | 0.0007 | - |
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+ | 3.5649 | 1950 | 0.0003 | - |
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+ | 3.6563 | 2000 | 0.0006 | - |
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+ | 3.7477 | 2050 | 0.0009 | - |
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+ | 3.8391 | 2100 | 0.0005 | - |
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+ | 3.9305 | 2150 | 0.0005 | - |
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+ | 4.0219 | 2200 | 0.001 | - |
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+ | 4.1133 | 2250 | 0.0044 | - |
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+ | 4.2048 | 2300 | 0.004 | - |
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+ | 4.2962 | 2350 | 0.0042 | - |
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+ | 4.3876 | 2400 | 0.0053 | - |
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+ | 4.4790 | 2450 | 0.0061 | - |
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+ | 4.5704 | 2500 | 0.008 | - |
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+ | 4.6618 | 2550 | 0.0057 | - |
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+ | 4.7532 | 2600 | 0.0063 | - |
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+ | 4.8446 | 2650 | 0.0064 | - |
225
+ | 4.9360 | 2700 | 0.0056 | - |
226
+ | 5.0274 | 2750 | 0.0033 | - |
227
+ | 5.1188 | 2800 | 0.0017 | - |
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+ | 5.2102 | 2850 | 0.0018 | - |
229
+ | 5.3016 | 2900 | 0.0012 | - |
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+ | 5.3931 | 2950 | 0.0007 | - |
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+ | 5.4845 | 3000 | 0.0026 | - |
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+ | 5.5759 | 3050 | 0.0038 | - |
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+ | 5.6673 | 3100 | 0.0019 | - |
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+ | 5.7587 | 3150 | 0.0009 | - |
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+ | 5.8501 | 3200 | 0.0005 | - |
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+ | 5.9415 | 3250 | 0.0002 | - |
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+ | 6.0329 | 3300 | 0.0002 | - |
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+ | 6.1243 | 3350 | 0.001 | - |
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+ | 6.2157 | 3400 | 0.0003 | - |
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+ | 6.3071 | 3450 | 0.001 | - |
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+ | 6.3985 | 3500 | 0.0003 | - |
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+ | 6.4899 | 3550 | 0.0008 | - |
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+ | 6.5814 | 3600 | 0.0 | - |
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+ | 6.6728 | 3650 | 0.0006 | - |
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+ | 6.7642 | 3700 | 0.0005 | - |
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+ | 6.8556 | 3750 | 0.0003 | - |
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+ | 6.9470 | 3800 | 0.0004 | - |
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+ | 7.0384 | 3850 | 0.002 | - |
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+ | 7.1298 | 3900 | 0.0012 | - |
250
+ | 7.2212 | 3950 | 0.0005 | - |
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+ | 7.3126 | 4000 | 0.0025 | - |
252
+ | 7.4040 | 4050 | 0.0029 | - |
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+ | 7.4954 | 4100 | 0.0017 | - |
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+ | 7.5868 | 4150 | 0.0006 | - |
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+ | 7.6782 | 4200 | 0.0002 | - |
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+ | 7.7697 | 4250 | 0.0008 | - |
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+ | 7.8611 | 4300 | 0.0003 | - |
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+ | 7.9525 | 4350 | 0.0005 | - |
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+ | 8.0439 | 4400 | 0.0003 | - |
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+ | 8.1353 | 4450 | 0.0005 | - |
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+ | 8.2267 | 4500 | 0.0012 | - |
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+ | 8.3181 | 4550 | 0.0003 | - |
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+ | 8.4095 | 4600 | 0.0003 | - |
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+ | 8.5009 | 4650 | 0.0008 | - |
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+ | 8.5923 | 4700 | 0.001 | - |
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+ | 8.6837 | 4750 | 0.0005 | - |
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+ | 8.7751 | 4800 | 0.0003 | - |
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+ | 8.8665 | 4850 | 0.0005 | - |
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+ | 8.9580 | 4900 | 0.0002 | - |
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+ | 9.0494 | 4950 | 0.0003 | - |
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+ | 9.1408 | 5000 | 0.0004 | - |
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+ | 9.2322 | 5050 | 0.0006 | - |
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+ | 9.3236 | 5100 | 0.0003 | - |
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+ | 9.4150 | 5150 | 0.0003 | - |
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+ | 9.5064 | 5200 | 0.0002 | - |
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+ | 9.5978 | 5250 | 0.0006 | - |
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+ | 9.6892 | 5300 | 0.0008 | - |
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+ | 9.7806 | 5350 | 0.0006 | - |
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+ | 9.8720 | 5400 | 0.0003 | - |
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+ | 9.9634 | 5450 | 0.0005 | - |
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+ | 10.0548 | 5500 | 0.0005 | - |
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+ | 10.1463 | 5550 | 0.0003 | - |
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+ | 10.2377 | 5600 | 0.0005 | - |
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+ | 10.3291 | 5650 | 0.0005 | - |
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+ | 10.4205 | 5700 | 0.0005 | - |
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+ | 10.5119 | 5750 | 0.0005 | - |
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+ | 10.6033 | 5800 | 0.0006 | - |
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+ | 10.6947 | 5850 | 0.0003 | - |
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+ | 10.7861 | 5900 | 0.0002 | - |
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+ | 10.8775 | 5950 | 0.0008 | - |
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+ | 10.9689 | 6000 | 0.0005 | - |
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+ | 11.0603 | 6050 | 0.0002 | - |
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+ | 11.1517 | 6100 | 0.0003 | - |
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+ | 11.2431 | 6150 | 0.0009 | - |
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+ | 11.3346 | 6200 | 0.0006 | - |
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+ | 11.4260 | 6250 | 0.0003 | - |
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+ | 11.5174 | 6300 | 0.0005 | - |
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+ | 11.6088 | 6350 | 0.0002 | - |
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+ | 11.7002 | 6400 | 0.0005 | - |
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+ | 11.7916 | 6450 | 0.0005 | - |
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+ | 11.8830 | 6500 | 0.0 | - |
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+ | 11.9744 | 6550 | 0.0008 | - |
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+ | 12.0658 | 6600 | 0.0008 | - |
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+ | 12.1572 | 6650 | 0.0005 | - |
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+ | 12.2486 | 6700 | 0.0003 | - |
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+ | 12.3400 | 6750 | 0.0002 | - |
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+ | 12.4314 | 6800 | 0.0006 | - |
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+ | 12.5229 | 6850 | 0.0003 | - |
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+ | 12.6143 | 6900 | 0.0005 | - |
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+ | 12.7057 | 6950 | 0.0029 | - |
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+ | 12.7971 | 7000 | 0.0013 | - |
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+ | 12.8885 | 7050 | 0.0038 | - |
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+ | 12.9799 | 7100 | 0.0029 | - |
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+ | 13.0713 | 7150 | 0.0011 | - |
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+ | 13.1627 | 7200 | 0.0009 | - |
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+ | 13.2541 | 7250 | 0.0004 | - |
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+ | 13.3455 | 7300 | 0.0002 | - |
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+ | 13.4369 | 7350 | 0.0018 | - |
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+ | 13.5283 | 7400 | 0.0044 | - |
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+ | 13.6197 | 7450 | 0.0007 | - |
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+ | 13.7112 | 7500 | 0.0005 | - |
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+ | 13.8026 | 7550 | 0.0005 | - |
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+ | 13.8940 | 7600 | 0.0006 | - |
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+ | 13.9854 | 7650 | 0.0003 | - |
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+ | 14.0768 | 7700 | 0.0005 | - |
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+ | 14.1682 | 7750 | 0.0003 | - |
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+ | 14.2596 | 7800 | 0.0005 | - |
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+ | 14.3510 | 7850 | 0.0003 | - |
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+ | 14.4424 | 7900 | 0.0003 | - |
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+ | 14.5338 | 7950 | 0.0005 | - |
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+ | 14.6252 | 8000 | 0.0009 | - |
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+ | 14.7166 | 8050 | 0.0 | - |
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+ | 14.8080 | 8100 | 0.0005 | - |
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+ | 14.8995 | 8150 | 0.0005 | - |
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+ | 14.9909 | 8200 | 0.0008 | - |
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+ | 15.0823 | 8250 | 0.0003 | - |
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+ | 15.1737 | 8300 | 0.0003 | - |
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+ | 15.2651 | 8350 | 0.0009 | - |
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+ | 15.3565 | 8400 | 0.0003 | - |
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+ | 15.4479 | 8450 | 0.0003 | - |
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+ | 15.5393 | 8500 | 0.0002 | - |
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+ | 15.6307 | 8550 | 0.0003 | - |
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+ | 15.7221 | 8600 | 0.0006 | - |
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+ | 15.8135 | 8650 | 0.0006 | - |
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+ | 15.9049 | 8700 | 0.0006 | - |
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+ | 15.9963 | 8750 | 0.0003 | - |
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+ | 16.0878 | 8800 | 0.0008 | - |
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+ | 16.1792 | 8850 | 0.0003 | - |
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+ | 16.2706 | 8900 | 0.0002 | - |
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+ | 16.3620 | 8950 | 0.0003 | - |
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+ | 16.4534 | 9000 | 0.0008 | - |
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+ | 16.5448 | 9050 | 0.0002 | - |
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+ | 16.6362 | 9100 | 0.0002 | - |
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+ | 16.7276 | 9150 | 0.0009 | - |
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+ | 16.8190 | 9200 | 0.0008 | - |
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+ | 16.9104 | 9250 | 0.0003 | - |
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+ | 17.0018 | 9300 | 0.0002 | - |
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+ | 17.0932 | 9350 | 0.0005 | - |
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+ | 17.1846 | 9400 | 0.0006 | - |
360
+ | 17.2761 | 9450 | 0.0005 | - |
361
+ | 17.3675 | 9500 | 0.0008 | - |
362
+ | 17.4589 | 9550 | 0.0003 | - |
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+ | 17.5503 | 9600 | 0.0005 | - |
364
+ | 17.6417 | 9650 | 0.0006 | - |
365
+ | 17.7331 | 9700 | 0.0006 | - |
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+ | 17.8245 | 9750 | 0.0005 | - |
367
+ | 17.9159 | 9800 | 0.0002 | - |
368
+ | 18.0073 | 9850 | 0.0002 | - |
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+ | 18.0987 | 9900 | 0.0003 | - |
370
+ | 18.1901 | 9950 | 0.0036 | - |
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+ | 18.2815 | 10000 | 0.0007 | - |
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+ | 18.3729 | 10050 | 0.0008 | - |
373
+ | 18.4644 | 10100 | 0.0006 | - |
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+ | 18.5558 | 10150 | 0.0005 | - |
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+ | 18.6472 | 10200 | 0.0003 | - |
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+ | 18.7386 | 10250 | 0.0006 | - |
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+ | 18.8300 | 10300 | 0.0006 | - |
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+ | 18.9214 | 10350 | 0.0003 | - |
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+ | 19.0128 | 10400 | 0.0005 | - |
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+ | 19.1042 | 10450 | 0.0003 | - |
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+ | 19.1956 | 10500 | 0.0005 | - |
382
+ | 19.2870 | 10550 | 0.0006 | - |
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+ | 19.3784 | 10600 | 0.0003 | - |
384
+ | 19.4698 | 10650 | 0.0003 | - |
385
+ | 19.5612 | 10700 | 0.0002 | - |
386
+ | 19.6527 | 10750 | 0.0008 | - |
387
+ | 19.7441 | 10800 | 0.0006 | - |
388
+ | 19.8355 | 10850 | 0.0005 | - |
389
+ | 19.9269 | 10900 | 0.0003 | - |
390
+ | 20.0183 | 10950 | 0.0005 | - |
391
+ | 20.1097 | 11000 | 0.0002 | - |
392
+ | 20.2011 | 11050 | 0.0002 | - |
393
+ | 20.2925 | 11100 | 0.0006 | - |
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+ | 20.3839 | 11150 | 0.0006 | - |
395
+ | 20.4753 | 11200 | 0.0008 | - |
396
+ | 20.5667 | 11250 | 0.0003 | - |
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+ | 20.6581 | 11300 | 0.0005 | - |
398
+ | 20.7495 | 11350 | 0.0002 | - |
399
+ | 20.8410 | 11400 | 0.0003 | - |
400
+ | 20.9324 | 11450 | 0.0005 | - |
401
+ | 21.0238 | 11500 | 0.0006 | - |
402
+ | 21.1152 | 11550 | 0.0002 | - |
403
+ | 21.2066 | 11600 | 0.0003 | - |
404
+ | 21.2980 | 11650 | 0.0008 | - |
405
+ | 21.3894 | 11700 | 0.0008 | - |
406
+ | 21.4808 | 11750 | 0.0002 | - |
407
+ | 21.5722 | 11800 | 0.0005 | - |
408
+ | 21.6636 | 11850 | 0.0005 | - |
409
+ | 21.7550 | 11900 | 0.0008 | - |
410
+ | 21.8464 | 11950 | 0.0005 | - |
411
+ | 21.9378 | 12000 | 0.0 | - |
412
+ | 22.0293 | 12050 | 0.0003 | - |
413
+ | 22.1207 | 12100 | 0.0002 | - |
414
+ | 22.2121 | 12150 | 0.0003 | - |
415
+ | 22.3035 | 12200 | 0.0005 | - |
416
+ | 22.3949 | 12250 | 0.0003 | - |
417
+ | 22.4863 | 12300 | 0.0005 | - |
418
+ | 22.5777 | 12350 | 0.0008 | - |
419
+ | 22.6691 | 12400 | 0.0002 | - |
420
+ | 22.7605 | 12450 | 0.0002 | - |
421
+ | 22.8519 | 12500 | 0.0006 | - |
422
+ | 22.9433 | 12550 | 0.0009 | - |
423
+ | 23.0347 | 12600 | 0.0006 | - |
424
+ | 23.1261 | 12650 | 0.0006 | - |
425
+ | 23.2176 | 12700 | 0.0008 | - |
426
+ | 23.3090 | 12750 | 0.0003 | - |
427
+ | 23.4004 | 12800 | 0.0005 | - |
428
+ | 23.4918 | 12850 | 0.0003 | - |
429
+ | 23.5832 | 12900 | 0.0005 | - |
430
+ | 23.6746 | 12950 | 0.0006 | - |
431
+ | 23.7660 | 13000 | 0.0005 | - |
432
+ | 23.8574 | 13050 | 0.0003 | - |
433
+ | 23.9488 | 13100 | 0.0002 | - |
434
+ | 24.0402 | 13150 | 0.0002 | - |
435
+ | 24.1316 | 13200 | 0.0005 | - |
436
+ | 24.2230 | 13250 | 0.0008 | - |
437
+ | 24.3144 | 13300 | 0.0003 | - |
438
+ | 24.4059 | 13350 | 0.0006 | - |
439
+ | 24.4973 | 13400 | 0.0008 | - |
440
+ | 24.5887 | 13450 | 0.0003 | - |
441
+ | 24.6801 | 13500 | 0.0002 | - |
442
+ | 24.7715 | 13550 | 0.0002 | - |
443
+ | 24.8629 | 13600 | 0.0005 | - |
444
+ | 24.9543 | 13650 | 0.0005 | - |
445
+ | 25.0457 | 13700 | 0.0006 | - |
446
+ | 25.1371 | 13750 | 0.0003 | - |
447
+ | 25.2285 | 13800 | 0.0005 | - |
448
+ | 25.3199 | 13850 | 0.0 | - |
449
+ | 25.4113 | 13900 | 0.0005 | - |
450
+ | 25.5027 | 13950 | 0.0009 | - |
451
+ | 25.5941 | 14000 | 0.0003 | - |
452
+ | 25.6856 | 14050 | 0.0003 | - |
453
+ | 25.7770 | 14100 | 0.0003 | - |
454
+ | 25.8684 | 14150 | 0.0005 | - |
455
+ | 25.9598 | 14200 | 0.0003 | - |
456
+ | 26.0512 | 14250 | 0.0011 | - |
457
+ | 26.1426 | 14300 | 0.0 | - |
458
+ | 26.2340 | 14350 | 0.0005 | - |
459
+ | 26.3254 | 14400 | 0.0014 | - |
460
+ | 26.4168 | 14450 | 0.0002 | - |
461
+ | 26.5082 | 14500 | 0.0003 | - |
462
+ | 26.5996 | 14550 | 0.0003 | - |
463
+ | 26.6910 | 14600 | 0.0008 | - |
464
+ | 26.7824 | 14650 | 0.0003 | - |
465
+ | 26.8739 | 14700 | 0.0 | - |
466
+ | 26.9653 | 14750 | 0.0003 | - |
467
+ | 27.0567 | 14800 | 0.0003 | - |
468
+ | 27.1481 | 14850 | 0.0008 | - |
469
+ | 27.2395 | 14900 | 0.0005 | - |
470
+ | 27.3309 | 14950 | 0.0003 | - |
471
+ | 27.4223 | 15000 | 0.0002 | - |
472
+ | 27.5137 | 15050 | 0.0006 | - |
473
+ | 27.6051 | 15100 | 0.0 | - |
474
+ | 27.6965 | 15150 | 0.0005 | - |
475
+ | 27.7879 | 15200 | 0.0003 | - |
476
+ | 27.8793 | 15250 | 0.0003 | - |
477
+ | 27.9707 | 15300 | 0.0012 | - |
478
+ | 28.0622 | 15350 | 0.0003 | - |
479
+ | 28.1536 | 15400 | 0.0006 | - |
480
+ | 28.2450 | 15450 | 0.0005 | - |
481
+ | 28.3364 | 15500 | 0.0003 | - |
482
+ | 28.4278 | 15550 | 0.0002 | - |
483
+ | 28.5192 | 15600 | 0.0011 | - |
484
+ | 28.6106 | 15650 | 0.0003 | - |
485
+ | 28.7020 | 15700 | 0.0002 | - |
486
+ | 28.7934 | 15750 | 0.0005 | - |
487
+ | 28.8848 | 15800 | 0.0005 | - |
488
+ | 28.9762 | 15850 | 0.0006 | - |
489
+ | 29.0676 | 15900 | 0.0 | - |
490
+ | 29.1590 | 15950 | 0.0 | - |
491
+ | 29.2505 | 16000 | 0.0002 | - |
492
+ | 29.3419 | 16050 | 0.0003 | - |
493
+ | 29.4333 | 16100 | 0.0008 | - |
494
+ | 29.5247 | 16150 | 0.0006 | - |
495
+ | 29.6161 | 16200 | 0.0005 | - |
496
+ | 29.7075 | 16250 | 0.0005 | - |
497
+ | 29.7989 | 16300 | 0.0008 | - |
498
+ | 29.8903 | 16350 | 0.0006 | - |
499
+ | 29.9817 | 16400 | 0.0005 | - |
500
+
501
+ ### Framework Versions
502
+ - Python: 3.10.12
503
+ - SetFit: 1.1.0
504
+ - Sentence Transformers: 3.3.1
505
+ - Transformers: 4.44.2
506
+ - PyTorch: 2.2.0a0+81ea7a4
507
+ - Datasets: 3.2.0
508
+ - Tokenizers: 0.19.1
509
+
510
+ ## Citation
511
+
512
+ ### BibTeX
513
+ ```bibtex
514
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
515
+ doi = {10.48550/ARXIV.2209.11055},
516
+ url = {https://arxiv.org/abs/2209.11055},
517
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
518
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
519
+ title = {Efficient Few-Shot Learning Without Prompts},
520
+ publisher = {arXiv},
521
+ year = {2022},
522
+ copyright = {Creative Commons Attribution 4.0 International}
523
+ }
524
+ ```
525
+
526
+ <!--
527
+ ## Glossary
528
+
529
+ *Clearly define terms in order to be accessible across audiences.*
530
+ -->
531
+
532
+ <!--
533
+ ## Model Card Authors
534
+
535
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
536
+ -->
537
+
538
+ <!--
539
+ ## Model Card Contact
540
+
541
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
542
+ -->
config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "mini1013/master_item_bt_test_flat_top",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "gradient_checkpointing": false,
11
+ "hidden_act": "gelu",
12
+ "hidden_dropout_prob": 0.1,
13
+ "hidden_size": 768,
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
16
+ "layer_norm_eps": 1e-05,
17
+ "max_position_embeddings": 514,
18
+ "model_type": "roberta",
19
+ "num_attention_heads": 12,
20
+ "num_hidden_layers": 12,
21
+ "pad_token_id": 1,
22
+ "position_embedding_type": "absolute",
23
+ "tokenizer_class": "BertTokenizer",
24
+ "torch_dtype": "float32",
25
+ "transformers_version": "4.44.2",
26
+ "type_vocab_size": 1,
27
+ "use_cache": true,
28
+ "vocab_size": 32000
29
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.2.0a0+81ea7a4"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
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