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mini1013
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master_item_bt_setfit

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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  1. 1_Pooling/config.json +10 -0
  2. README.md +381 -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 +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: klue/roberta-base
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+ library_name: setfit
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+ metrics:
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+ - metric
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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: 동구밭 올바른 린스바 극손상용 100g 린스바 2개입 (주) 동구밭
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+ - text: Burt’s Bees 홀리데이 선물 세트 옵션없음 샵인프랑
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+ - text: (2024년 수입품) 뭄 왁싱 클래식 제모 12oz MOOM classic TeaTree 옵션없음 영화실업
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+ - text: 볼라욘 스피넴 파우더500g(모델링 마스크)+샘플+팩도구세트 옵션없음 수애
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+ - text: 각질제거 4종 큐티클 세트 패디블레이드 손발톱 5W8BE42924 패디knife 4종세트 주도매
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+ inference: true
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+ model-index:
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+ - name: SetFit with klue/roberta-base
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: metric
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+ value: 0.8872727272727273
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+ name: Metric
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+ ---
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+
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+ # SetFit with klue/roberta-base
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 13 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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+ | 0 | <ul><li>'헤라 옴므 에센스 인 2종 단품세트 옵션없음 리앤햇'</li><li>'크리니크 포 맨 수퍼 에너자이저 안티-퍼티그 디퍼핑 아이 젤 15ml 1개 옵션없음 리틀리아'</li><li>'네오젠 브이 바이옴 퍼밍 크림 60g 1개 옵션없음 건강드림'</li></ul> |
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+ | 8 | <ul><li>'디오디너리 나이아신아마이드 10% + 징크 1% 60ml 9660115 옵션없음 비티엘파트너'</li><li>'피지오겔 레드 수딩 AI 로션 200ml 1021824 50ml 비매품 x4개-200ml 50ml 비매품 x4개-200ml 메가랜드'</li><li>'넘버즈인 시카 갈아만든 초록패드 70매 옵션없음 다물다선'</li></ul> |
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+ | 1 | <ul><li>'158408 플라워 손톱깎이 세트 중 6종 제작 판촉 홍보 200개이상 제작가능／인쇄비 별도문의 기프트오후'</li><li>'요거트젤네일 시럽젤 버니츄 10종 단품+오스트리아 네일스톤 2EA Syrup58(스카이츄) 코코네일 아울렛'</li><li>'임페리얼 네일드릴더쎈 전용 고급아크릭 케이스 정리함 옵션없음 임페리얼'</li></ul> |
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+ | 5 | <ul><li>'여드릅 뾰루지 관리 압출기 의료용 피지제 핀셋 거기 바늘 세트 11종 미용 짜는도구 옵션없음 호담컴퍼니'</li><li>'가열 전기 휴대용 빗 속눈썹 컬 파마 오래 지속되는 열 메이크업 1 5 개 Pink 2pcs 리마112'</li><li>'스테인리스 스틸 눈썹 족집게 다채로운 헤어 미세 모발 풀러 제거 Sky blue 4PCS 더블유파트너스P'</li></ul> |
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+ | 10 | <ul><li>'에르메스 르 자르뎅 드 무슈 리 우먼 오드뚜왈렛 100ml 옵션없음 트랜드퍼퓸'</li><li>'캐롤 앤 챈 - 미니 디퓨저 - 레드 로즈 (프레쉬 로즈 & 아시안 우드) 20ml 스트로베리넷 (홍콩)'</li><li>'아카 카파 - 화이트 무화과 & 시더우드 홈 디퓨저 250ml/8.25oz 스트로베리넷 (홍콩)'</li></ul> |
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+ | 12 | <ul><li>'닥터모리엔 헤어 토닉 100ml 150ml(가성비 대용량 출시) 주식회사 폴라인터내셔널'</li><li>'닥터멜락신 본덱스 단백질 결합 리모델링 클리닉 옵션없음 영주수'</li><li>'[정식수입통관상품] 에르메스 호텔 어메니티 5종 트래블킷 오도렌지베르테 옵션없음 유니스타'</li></ul> |
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+ | 3 | <ul><li>'Love from Santa Barbara 콤팩트 퍼스널 와일드 로즈 솔리드 향수 그녀를 위한 고급 장인 스파 선물 편리하고, 지갑이나 여행 가방에 들어갈 수 있다 옵션없음 캘리마트'</li><li>'[대구백화점] [더바디샵]슬립 릴렉싱 마사지 오일 100ML 옵션없음 (주)대구백화점 대백프라자'</li><li>'솔트 샤워 오일 350ml 단품 2KIT 솔티앤코(Saltea and Co.)'</li></ul> |
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+ | 7 | <ul><li>'넛세린 시카 넛 카밍 선스틱 18g 1개 (민감성, SPF50+) HI 옵션없음 주식회사 호인인터내셔널'</li><li>'[NEW] 최대 45% / 달바 비건 프레쉬 선쿠션 15g [세트] 선쿠션 15g(2개) 주식회사 달바글로벌'</li><li>'엠퀴리 코어 선 젤 50ml + 선스틱 18g 2종세트'</li></ul> |
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+ | 11 | <ul><li>'프로스펙터스 아이언 오레 포마드 수성 113g 1개(397g)콜마인 COME BY'</li><li>'아뉴 남자 셀프 옆머리 뒷머리 누르기 다운펌 밴드 기계 다운펌제 펌약 3개 옵션없음 지엘디'</li><li>'메온셀 아오모리 대용량 다운펌 1000ml 셀프다운펌 미용실 뜨는 옆머리 아오모리 대용량 다운펌 1000ml 셀프다운펌 구매확정몰'</li></ul> |
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+ | 4 | <ul><li>'스칸스킨 한 겹 톤업크림 메이크업베이스 SPF30+, PA++ 45ml 옵션없음 (주) 오퍼스아시아'</li><li>'력과 잡는 동시에 지속성을 트윈케익 21호 커버 옵션없음 소쿠리의잡화점'</li><li>'키핀터치 NEW 라벤더 컬러 출시 모공지우개 파우더 10g 2type (택1) 모공지우개 파우더 (백색) 10g 캐치세컨'</li></ul> |
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+ | 2 | <ul><li>'마리에 뱀독 에센스 마스크 10매 옵션없음 또옴1'</li><li>'장영란 아기코세트 멜팅클리어패드 30매 + 모공앰플 30ml 옵션없음 세일러15'</li><li>'올리브영 케어플러스 상처커버 스팟패치 102매 (5개) 옵션없음 다원샵앤샵'</li></ul> |
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+ | 6 | <ul><li>'삐아 오버 글레이즈 01 피치당 1개 옵션없음 밤구시'</li><li>'올리브영 클리오 벨벳 립 펜슬 + 샤프너 03 커피 브라운 옵션없음 비바글로우'</li><li>'바비브라운 립밤 15g (SPF15) 옵션없음 원쇼핑'</li></ul> |
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+ | 9 | <ul><li>'더마큐어마스터원샷클렌저 200ml 1개 옵션없음 주식회사 밀레'</li><li>'미샤 수퍼아쿠아 울트라 히알론 필링젤 미샤 수퍼아쿠아_울트라 히알론 필링젤 유민코스'</li><li>'바이오더마 세비엄 H2O 500ml 옵션없음 비티엘파트너'</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 | Metric |
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+ |:--------|:-------|
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+ | **all** | 0.8873 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_item_bt_setfit")
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+ # Run inference
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+ preds = model("Burt’s Bees 홀리데이 선물 세트 옵션없음 샵인프랑")
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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 | 3 | 9.8072 | 33 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 1229 |
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+ | 1 | 559 |
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+ | 2 | 654 |
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+ | 3 | 1528 |
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+ | 4 | 563 |
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+ | 5 | 677 |
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+ | 6 | 1157 |
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+ | 7 | 563 |
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+ | 8 | 1037 |
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+ | 9 | 1034 |
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+ | 10 | 219 |
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+ | 11 | 544 |
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+ | 12 | 671 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (512, 512)
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+ - num_epochs: (10, 10)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ - 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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+ | 0.0012 | 1 | 0.3181 | - |
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+ | 0.0613 | 50 | 0.2442 | - |
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+ | 0.1225 | 100 | 0.2202 | - |
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+ | 0.1838 | 150 | 0.2027 | - |
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+ | 0.2451 | 200 | 0.194 | - |
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+ | 0.3064 | 250 | 0.1582 | - |
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+ | 0.3676 | 300 | 0.1369 | - |
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+ | 0.4289 | 350 | 0.1328 | - |
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+ | 0.4902 | 400 | 0.1131 | - |
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+ | 0.5515 | 450 | 0.0927 | - |
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+ | 0.6127 | 500 | 0.0812 | - |
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+ | 0.6740 | 550 | 0.0617 | - |
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+ | 0.7353 | 600 | 0.0691 | - |
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+ | 0.7966 | 650 | 0.063 | - |
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+ | 0.8578 | 700 | 0.0404 | - |
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+ | 0.9191 | 750 | 0.0336 | - |
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+ | 0.9804 | 800 | 0.0305 | - |
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+ | 1.0417 | 850 | 0.0276 | - |
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+ | 1.1029 | 900 | 0.0232 | - |
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+ | 1.1642 | 950 | 0.0135 | - |
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+ | 1.2255 | 1000 | 0.0104 | - |
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+ | 1.2868 | 1050 | 0.0091 | - |
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+ | 1.3480 | 1100 | 0.005 | - |
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+ | 1.4093 | 1150 | 0.0055 | - |
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+ | 1.4706 | 1200 | 0.0087 | - |
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+ | 1.5319 | 1250 | 0.0094 | - |
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+ | 1.5931 | 1300 | 0.0039 | - |
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+ | 1.6544 | 1350 | 0.0026 | - |
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+ | 1.7157 | 1400 | 0.0039 | - |
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+ | 1.7770 | 1450 | 0.0023 | - |
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+ | 1.8382 | 1500 | 0.0074 | - |
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+ | 1.8995 | 1550 | 0.0027 | - |
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+ | 1.9608 | 1600 | 0.0029 | - |
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+ | 2.0221 | 1650 | 0.0008 | - |
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+ | 2.0833 | 1700 | 0.004 | - |
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+ | 2.1446 | 1750 | 0.0011 | - |
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+ | 2.2059 | 1800 | 0.0036 | - |
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+ | 2.2672 | 1850 | 0.0002 | - |
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+ | 2.3284 | 1900 | 0.0016 | - |
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+ | 2.3897 | 1950 | 0.0018 | - |
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+ | 2.4510 | 2000 | 0.0009 | - |
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+ | 2.5123 | 2050 | 0.0012 | - |
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+ | 2.5735 | 2100 | 0.0005 | - |
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+ | 2.6348 | 2150 | 0.0013 | - |
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+ | 2.6961 | 2200 | 0.0003 | - |
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+ | 2.7574 | 2250 | 0.0006 | - |
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+ | 2.8186 | 2300 | 0.0006 | - |
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+ | 2.8799 | 2350 | 0.0006 | - |
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+ | 2.9412 | 2400 | 0.0013 | - |
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+ | 3.0025 | 2450 | 0.0011 | - |
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+ | 3.0637 | 2500 | 0.0011 | - |
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+ | 3.125 | 2550 | 0.0003 | - |
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+ | 3.1863 | 2600 | 0.0018 | - |
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+ | 3.2475 | 2650 | 0.0003 | - |
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+ | 3.3088 | 2700 | 0.0008 | - |
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+ | 3.3701 | 2750 | 0.001 | - |
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+ | 3.4314 | 2800 | 0.0002 | - |
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+ | 3.4926 | 2850 | 0.0002 | - |
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+ | 3.5539 | 2900 | 0.0001 | - |
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+ | 3.6152 | 2950 | 0.0005 | - |
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+ | 3.6765 | 3000 | 0.0001 | - |
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+ | 3.7377 | 3050 | 0.0002 | - |
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+ | 3.7990 | 3100 | 0.0014 | - |
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+ | 3.8603 | 3150 | 0.0012 | - |
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+ | 3.9216 | 3200 | 0.0003 | - |
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+ | 3.9828 | 3250 | 0.0018 | - |
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+ | 4.0441 | 3300 | 0.0007 | - |
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+ | 4.1054 | 3350 | 0.0001 | - |
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+
340
+ ### Framework Versions
341
+ - Python: 3.10.12
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+ - SetFit: 1.1.0.dev0
343
+ - Sentence Transformers: 3.1.1
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+ - Transformers: 4.45.1
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+ - PyTorch: 2.4.0+cu121
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+ - Datasets: 2.20.0
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+ - Tokenizers: 0.20.0
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+
349
+ ## Citation
350
+
351
+ ### BibTeX
352
+ ```bibtex
353
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
354
+ doi = {10.48550/ARXIV.2209.11055},
355
+ url = {https://arxiv.org/abs/2209.11055},
356
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
357
+ 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},
359
+ publisher = {arXiv},
360
+ year = {2022},
361
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
363
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
369
+ -->
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+
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+ <!--
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+ ## 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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+ -->
376
+
377
+ <!--
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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.*
381
+ -->
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