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mini1013
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master_cate_bt7_test_flat_top_cate

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 +444 -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: '[기획세트][로레알파리] UV 디펜더 50ml+유브이 디펜더 베이지 15ml 매트&프레쉬 위메프 > 생활·주방·반려동물 > 바디/헤어
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+ > 샴푸/린스/헤어케어;위메프 > 뷰티 > 메이크업 > 아이 메이크업;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트;위메프
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+ > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션;위메프 > 뷰티 > 메이크업 > 아이 메이크업 > 마스카라;위메프 > 뷰티 > 메이크업
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+ > 립 메이크업;(#M)위메프 > 뷰티 > 선케어 > 선크림/선블록 > 선크림/선블록 위메프 > 뷰티 > 선케어 > 선크림/선블록 > 선크림/선블록'
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+ - text: 이니스프리 트루 히알루론 수분 선크림 SPF50+ PA++++ 50ml × 4개 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션
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+ Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝
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+ - text: 이니스프리 인텐시브 롱래스팅 선스크린 EX SPF50+ PA++++ 20개_50ml (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션
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+ Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝 > 선케어 > 선블록/선크림/선로션
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+ - text: 헤라 선 메이트 레포츠 프로 워터프루프 70ml(SPF50+) (#M)홈>화장품/미용>선케어>선크림 Naverstore > 화장품/미용
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+ > 선케어 > 선크림
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+ - text: 이니스프리 트루 마일드 시카 무기자차 선크림 SPF50+ PA4+ 50mL 1 +1 MinSellAmount (#M)화장품/향수>선케어>선크림
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+ Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선크림
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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: 0.3319713993871297
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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>'이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 8개 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림'</li><li>'1+1 / 달바 비건 톤업 선쿠션 15gX2개 / 촉촉 간편하게 혼합자차 핑크빛 물광 윤광 / 베이스 프리 데일리 필수템 [단품] 톤업 선쿠션 15g(1개) (#M)홈>제품유형별>선케어 Naverstore > 화장품/미용 > 선케어 > 선파우더/쿠션'</li><li>'UV 프로텍티브 컴팩트 파운데이션(리필+케이스) SPF35/PA+++ 12g 페어 아이보리 DepartmentLotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림/선로션 DepartmentLotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림/선로션'</li></ul> |
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+ | 1 | <ul><li>'비쉬 까삐탈 솔레일 선 스프레이 LSF50 200ml ssg > 뷰티 > 스킨케어 > 선케어 > 선크림 ssg > 뷰티 > 스킨케어 > 선케어 > 선크림'</li><li>'끈적임없는 SNP 선스프레이 자외선차단 쿨링 뿌리는 선크림 (#M)홈>화장품/미용>선케어>선스프레이 Naverstore > 화장품/미용 > 선케어 > 선스프레이'</li><li>'리더스 자외선차단 썬버디 올 오버 선 스프레이 180ml MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'</li></ul> |
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+ | 0 | <ul><li>'[AHC 썸머세일] 박세리감독 기획 마스터즈 에어리치 선스틱 14g+[ ] 마... 옵션선택:002P01)선스틱 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디케어용품 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디케어용품'</li><li>'3CE PRIMER SUN STICK 프라이머 선스틱 BEIGE_FRE ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱;ssg > 뷰티 > 스킨케어 > 선케어 > 선스틱 ssg > 뷰티 > 스킨케어 > 선케어'</li><li>'에스케이-투 피테라 풀라인 세트 1개 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어;쿠팡 홈;(#M)쿠팡 홈>뷰티>선물세트/키트>선물세트>스킨케어 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어'</li></ul> |
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+ | 4 | <ul><li>'마몽드 로즈워터 수딩젤 300 ml ssg > 뷰티 > 스킨케어 > 크림 ssg > 뷰티 > 스킨케어 > 크림'</li><li>'푸드어홀릭 알로에/스네일 수딩젤 300ml MinSellAmount 화장품/향수>크림>안티에이징크림;(#M)화장품/향수>스킨케어>크림/젤 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 크림/젤'</li><li>'메디플라워 알로에베라 프레시 수딩젤 300ml 300ml × 1개 쿠팡 홈>뷰티>스킨케어>기초화장품>크림/올인원>페이셜크림;쿠팡 홈;Coupang > 뷰티 > 스킨케어 > 기초화장품;(#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>페이셜크림 Coupang > 뷰티 > 스킨케어 > 크림/올인원 > 페이셜크림'</li></ul> |
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+ | 3 | <ul><li>'싸이닉 엔조이 올라운드 워터리 선크림 200g 2개 LotteOn > 뷰티 > 남성화장품 > 선크림 LotteOn > 뷰티 > 남성화장품 > 선크림'</li><li>' 상백크림 30ml/50ml SPF50+/PA++++ 1호 크리미 글로우 30ml LotteOn > 뷰티 > 스킨케어 > 크림 LotteOn > 뷰티 > 스킨케어 > 크림'</li><li>'(485387) 이니스프리 트루히알루론수분선크림기획 SPF50+ PA4+ 50mL+20mL 무료배송 (#M)SSG.COM/스킨케어/스킨케어세트 ssg > 뷰티 > 스킨케어 > 스킨케어세트'</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** | 0.3320 |
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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
92
+ 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_bt7_test_flat_top_cate")
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+ # Run inference
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+ preds = model("헤라 선 메이트 레포츠 프로 워터프루프 70ml(SPF50+) (#M)홈>화장품/미용>선케어>선크림 Naverstore > 화장품/미용 > 선케어 > 선크림")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 11 | 21.836 | 72 |
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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 | 0.4309 | - |
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+ | 0.1279 | 50 | 0.4454 | - |
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+ | 0.2558 | 100 | 0.4001 | - |
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+ | 0.3836 | 150 | 0.3616 | - |
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+ | 0.5115 | 200 | 0.3104 | - |
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+ | 0.6394 | 250 | 0.2446 | - |
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+ | 0.7673 | 300 | 0.1921 | - |
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+ | 0.8951 | 350 | 0.1521 | - |
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+ | 1.0230 | 400 | 0.1177 | - |
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+ | 1.1509 | 450 | 0.0973 | - |
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+ | 1.2788 | 500 | 0.0926 | - |
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+ | 1.4066 | 550 | 0.0866 | - |
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+ | 1.5345 | 600 | 0.0826 | - |
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+ | 1.6624 | 650 | 0.078 | - |
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+ | 1.7903 | 700 | 0.0741 | - |
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+ | 1.9182 | 750 | 0.0709 | - |
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+ | 2.0460 | 800 | 0.0658 | - |
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+ | 2.1739 | 850 | 0.0657 | - |
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+ | 2.3018 | 900 | 0.0566 | - |
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+ | 2.4297 | 950 | 0.0549 | - |
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+ | 2.5575 | 1000 | 0.043 | - |
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+ | 2.6854 | 1050 | 0.0391 | - |
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+ | 2.8133 | 1100 | 0.0197 | - |
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+ | 2.9412 | 1150 | 0.0108 | - |
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+ | 3.0691 | 1200 | 0.0085 | - |
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+ | 3.1969 | 1250 | 0.0082 | - |
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+ | 3.3248 | 1300 | 0.0067 | - |
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+ | 3.4527 | 1350 | 0.0082 | - |
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+ | 3.5806 | 1400 | 0.0077 | - |
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+ | 3.7084 | 1450 | 0.007 | - |
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+ | 3.8363 | 1500 | 0.0046 | - |
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+ | 3.9642 | 1550 | 0.0049 | - |
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+ | 4.0921 | 1600 | 0.0041 | - |
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+ | 4.2199 | 1650 | 0.003 | - |
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+ | 4.3478 | 1700 | 0.0003 | - |
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+ | 4.4757 | 1750 | 0.0002 | - |
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+ | 4.6036 | 1800 | 0.0 | - |
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+ | 4.7315 | 1850 | 0.0 | - |
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+ | 4.8593 | 1900 | 0.0 | - |
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+ | 4.9872 | 1950 | 0.0 | - |
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+ | 5.1151 | 2000 | 0.0 | - |
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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 | 0.0001 | - |
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+ | 5.8824 | 2300 | 0.0001 | - |
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+ | 6.0102 | 2350 | 0.0 | - |
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+ | 6.1381 | 2400 | 0.0003 | - |
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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 | 0.0002 | - |
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+ | 6.6496 | 2600 | 0.0007 | - |
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+ | 6.7775 | 2650 | 0.0008 | - |
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+ | 6.9054 | 2700 | 0.0028 | - |
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+ | 7.0332 | 2750 | 0.0024 | - |
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+ | 7.1611 | 2800 | 0.0002 | - |
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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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+ | 7.9284 | 3100 | 0.0 | - |
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+ | 8.0563 | 3150 | 0.0001 | - |
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+ | 8.1841 | 3200 | 0.0 | - |
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+ | 8.3120 | 3250 | 0.0 | - |
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+ | 8.4399 | 3300 | 0.0002 | - |
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+ | 8.5678 | 3350 | 0.0002 | - |
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+ | 8.6957 | 3400 | 0.0 | - |
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+ | 8.8235 | 3450 | 0.0002 | - |
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+ | 8.9514 | 3500 | 0.0 | - |
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+ | 9.0793 | 3550 | 0.0 | - |
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+ | 9.2072 | 3600 | 0.0 | - |
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+
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+ ### Framework Versions
404
+ - 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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+
412
+ ## Citation
413
+
414
+ ### BibTeX
415
+ ```bibtex
416
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
417
+ doi = {10.48550/ARXIV.2209.11055},
418
+ url = {https://arxiv.org/abs/2209.11055},
419
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
420
+ 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},
422
+ publisher = {arXiv},
423
+ year = {2022},
424
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
426
+ ```
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+
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+ <!--
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+ ## Glossary
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+
431
+ *Clearly define terms in order to be accessible across audiences.*
432
+ -->
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+
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+ <!--
435
+ ## 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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+
440
+ <!--
441
+ ## 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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