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mini1013
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master_cate_top_bt5_4_test_flat

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +442 -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: '[텐바이텐] 조르지오 아르마니 투 고 에센스 쿠션 15g 2호 옵션선택 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파우더/파우더팩트
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+ Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/파우더팩트'
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+ - text: '[헤라] 블랙 파운데이션 21N 바닐라 35ml 1.블랙 파운데이션 21N 바닐라 35ml (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형
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+ Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'
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+ - text: 시세이도 UV 프로텍티브 컴팩트 파운데이션(리필) SPF35/PA+++ 12g 미디엄 아이보리 홈>현대백화점>화장품>화장품브랜드>시세이도>메이크업;(#M)홈>현대백화점>화장품>메이크업>페이스>파운데이션
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+ HMALL > 현대백화점 > 화장품 > 메이크업 > 페이스 > 파운데이션
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+ - text: 정샘물 스킨 세팅 글로잉 베이스 40ml LotteOn > 뷰티 > 베이스메이크업 > 베이스메이크업세트 LotteOn > 뷰티 >
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+ 메이크업 > 메이크업세트
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+ - text: 메이블린 슈퍼스테이 파운데이션 120호 클래식 아이보리 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 ssg > 뷰티
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+ > 메이크업 > 베이스메이크업 > 파운데이션
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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: 0.9687814702920443
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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:** 5 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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+ | 2 | <ul><li>'다슈 맨즈 멀티 커버 스틱 파운데이션 2호 [뉴트럴 베이지] (#M)홈>전체상품 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 스틱형'</li><li>'스킨 컨실러 스틱 웜 베이지 (#M)화장품/향수>베이스메이크업>BB크림/톤업크림 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > BB크림/톤업크림'</li><li>'갤러리아 UV 프로텍티브 스틱 파운데이션 SPF36/PA+++ 베이지 (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크��� > 파운데이션'</li></ul> |
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+ | 1 | <ul><li>'쏘내추럴 FIXX라인 메이크업픽서/젤픽서/픽스밤/매직실러 외 BEST 올데이 타이트 메이크업 세팅 픽서 대용량 120ml_본품 11st>뷰티>선케어/메이크업>페이스메이크업;11st > 뷰티 > 메이크업 > 페이스메이크업 > 쿠션팩트;(#M)11st>메이크업>페이스메이크업>쿠션팩트 11st > 뷰티 > 메이크업 > 페이스메이크업 > 쿠션팩트'</li><li>'로라메르시에 틴티드 모이스춰라이저 라이트 리빌러 1W1 포슬린 (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li><li>'매직 스타터 SPF25 PA++ 35ml 1호(로즈)_Y(뷰티포인트 회원시 적립가능)_예 DepartmentSsg > 명품화장품 > 메이크업 > 베이스 메이크업 > 메이크업베이스/프라이머 DepartmentSsg > 명품화장품 > 메이크업 > 베이스 메이크업 > 메이크업베이스/프라이머'</li></ul> |
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+ | 0 | <ul><li>'(중복쿠폰)헤라 6월 초여름 선크림 프로텍터 특별전 06 블랙 파운데이션 (선택)_21N1 백화점 명품화장품>남성화장품>남성화장품 세트;(#M)화장품/향수>남성화장품>남성화장품세트 Gmarket > 뷰티 > 화장품/향수 > 남성화장품'</li><li>'CHANEL 레 베쥬 뚜쉬 드 뗑 BR12 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'</li><li>'[L단독]NEW 파워 패브릭 플러스 파운데이션 세트/조르지오 아르마니 3호 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'</li></ul> |
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+ | 4 | <ul><li>'수블리마지 르 뗑 10 베쥬 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li><li>'에이지투웨니스 드롭드롭드롭 패턴플레이 에디션 케이스+리필3개 패턴플레이(레드) 케이스+화이트 21호 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트;홈 > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'</li><li>'에이지투웨니스 뉴오리지널 샤이닝 드롭스 에디션 퍼플 (SPF50+ PA+++) 핑크 21호 2개 (#M)화장품/미용>베이스메이크업>파운데이션>크림형 AD > Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'</li></ul> |
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+ | 3 | <ul><li>'[리필] 네츄럴 래디언트 롱웨어 쿠션 파운데이션 SPF 50/PA+++/나스 몽블랑 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 쿠션형'</li><li>'갤러리아 [지방시][정품 증정] 땡 꾸뛰르 쿠션 세트 (+딥 벨벳 N37 립스틱 증 C104 11st>메이크업>페이스메이크업>트윈케익;(#M)11st>메이크업>페이스메이크업>트윈케이크 11st > 뷰티 > 메이크업 > 페이스메이크업 > 트윈케이크'</li><li>'[롭스] [이글립스] 블러 피니싱 쿠션 21호 라이트베이지 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 쿠션팩트 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 쿠션팩트'</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** | 0.9688 |
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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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+
89
+ ```bash
90
+ 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_top_bt5_4_test_flat")
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+ # Run inference
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+ preds = model("정샘물 스킨 세팅 글로잉 베이스 40ml LotteOn > 뷰티 > 베이스메이크업 > 베이스메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")
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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 | 10 | 22.86 | 53 |
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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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+
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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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+ | 0.0026 | 1 | 0.39 | - |
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+ | 0.1279 | 50 | 0.4195 | - |
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+ | 0.2558 | 100 | 0.3762 | - |
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+ | 0.3836 | 150 | 0.3382 | - |
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+ | 0.5115 | 200 | 0.2995 | - |
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+ | 0.6394 | 250 | 0.2562 | - |
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+ | 0.7673 | 300 | 0.1775 | - |
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+ | 0.8951 | 350 | 0.1193 | - |
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+ | 1.0230 | 400 | 0.0637 | - |
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+ | 1.1509 | 450 | 0.02 | - |
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+ | 1.2788 | 500 | 0.0044 | - |
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+ | 1.4066 | 550 | 0.0025 | - |
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+ | 1.5345 | 600 | 0.0013 | - |
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+ | 1.6624 | 650 | 0.0009 | - |
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+ | 1.7903 | 700 | 0.0006 | - |
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+ | 1.9182 | 750 | 0.0005 | - |
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+ | 2.0460 | 800 | 0.0003 | - |
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+ | 2.1739 | 850 | 0.0002 | - |
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+ | 2.3018 | 900 | 0.0003 | - |
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+ | 2.4297 | 950 | 0.0001 | - |
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+ | 2.5575 | 1000 | 0.0001 | - |
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+ | 2.6854 | 1050 | 0.0004 | - |
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+ | 2.8133 | 1100 | 0.0007 | - |
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+ | 2.9412 | 1150 | 0.0001 | - |
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+ | 3.0691 | 1200 | 0.0001 | - |
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+ | 3.1969 | 1250 | 0.0009 | - |
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+ | 3.3248 | 1300 | 0.0001 | - |
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+ | 3.4527 | 1350 | 0.0001 | - |
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+ | 3.5806 | 1400 | 0.0 | - |
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+ | 3.7084 | 1450 | 0.0 | - |
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+ | 3.8363 | 1500 | 0.0 | - |
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+ | 3.9642 | 1550 | 0.0 | - |
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+ | 4.0921 | 1600 | 0.0 | - |
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+ | 4.2199 | 1650 | 0.0008 | - |
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+ | 4.3478 | 1700 | 0.0004 | - |
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+ | 4.4757 | 1750 | 0.0004 | - |
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+ | 4.6036 | 1800 | 0.0001 | - |
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+ | 4.7315 | 1850 | 0.0006 | - |
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+ | 4.8593 | 1900 | 0.0004 | - |
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+ | 4.9872 | 1950 | 0.0 | - |
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+ | 5.1151 | 2000 | 0.0001 | - |
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+ | 5.2430 | 2050 | 0.0 | - |
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+ | 5.3708 | 2100 | 0.0 | - |
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+ | 5.4987 | 2150 | 0.0 | - |
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+ | 5.6266 | 2200 | 0.0 | - |
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+ | 5.7545 | 2250 | 0.0 | - |
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+ | 5.8824 | 2300 | 0.0 | - |
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+ | 6.0102 | 2350 | 0.0 | - |
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+ | 6.1381 | 2400 | 0.0 | - |
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+ | 6.2660 | 2450 | 0.0 | - |
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+ | 6.3939 | 2500 | 0.0 | - |
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+ | 6.5217 | 2550 | 0.0 | - |
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+ | 6.6496 | 2600 | 0.0 | - |
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+ | 6.7775 | 2650 | 0.0 | - |
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+ | 6.9054 | 2700 | 0.0 | - |
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+ | 7.0332 | 2750 | 0.0 | - |
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+ | 7.1611 | 2800 | 0.0 | - |
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+ | 7.2890 | 2850 | 0.0 | - |
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+ | 7.4169 | 2900 | 0.0 | - |
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+ | 7.5448 | 2950 | 0.0 | - |
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+ | 7.6726 | 3000 | 0.0 | - |
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+ | 7.8005 | 3050 | 0.0 | - |
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+
401
+ ### Framework Versions
402
+ - Python: 3.10.12
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+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
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+ - Transformers: 4.44.2
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+ - PyTorch: 2.2.0a0+81ea7a4
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.19.1
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+
410
+ ## Citation
411
+
412
+ ### BibTeX
413
+ ```bibtex
414
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
415
+ doi = {10.48550/ARXIV.2209.11055},
416
+ url = {https://arxiv.org/abs/2209.11055},
417
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
418
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
420
+ publisher = {arXiv},
421
+ year = {2022},
422
+ copyright = {Creative Commons Attribution 4.0 International}
423
+ }
424
+ ```
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+
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+ <!--
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+ ## Glossary
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+
429
+ *Clearly define terms in order to be accessible across audiences.*
430
+ -->
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+
432
+ <!--
433
+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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