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mini1013
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master_cate_sl28

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +321 -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
@@ -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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+ 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: 캠핑 데크팩 타프팩 고정핀 단조 스토퍼 텐트비너 고강도 오토캠핑용품 백패킹 스포츠/레저>캠핑>텐트/타프용품>기타텐트/타프용품
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+ - text: 빅토리캠프 BLAZE 블레이즈 펠렛연소기 캠핑용 화목난로 펠렛난로 차박 야외용 스포츠/레저>캠핑>기타캠핑용품
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+ - text: 프리모리 세움 스탠다드 슬라이드 폴대 사이드 타프 가변 높이 조절 단품 캠핑 피크닉 스포츠/레저>캠핑>텐트/타프용품>폴대
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+ - text: 익시드 디자인 TIRANT RAZOR V3 티타늄 만능칼 EDC 포켓 나이프 스포츠/레저>캠핑>취사용품>다용도칼
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+ - text: 애몰라이트 후레쉬 AM1 표준슬립 손전등 스포츠/레저>캠핑>랜턴>손전등
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: mini1013/master_domain
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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: 1.0
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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:** 14 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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+ | 7.0 | <ul><li>'해먹 감성 침대 그네 캠핑 가정용 야외 세트 그물 휴대용 헤먹 차박 비박 스포츠/레저>캠핑>캠핑가구>해먹'</li><li>'그물침대 레인보우 캠핑 감성 휴대용 대형 해먹 스포츠/레저>캠핑>캠핑가구>해먹'</li><li>'야전침대 스웨이드 토퍼 간이 침대커버 매트 스포츠/레저>캠핑>캠핑가구>야전침대'</li></ul> |
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+ | 1.0 | <ul><li>'식스비 감성 LED C타입 충전식 조명 캠핑랜턴 스포츠/레저>캠핑>랜턴>실내등'</li><li>'원터치 캠핑용 휴대고리 LED 손전등 등산 랜턴 서치라이트 스포츠/레저>캠핑>랜턴>손전등'</li><li>'초슬림 디자인 휴대용후레쉬 9 캠핑후레쉬 소형후레쉬 등산후레쉬 초소형후레쉬 스포츠/레저>캠핑>랜턴>손전등'</li></ul> |
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+ | 6.0 | <ul><li>'제로그램 모나크250 침낭 스포츠/레저>캠핑>침낭'</li><li>'웜피스 바이킹 900-195CM 침낭 스포츠/레저>캠핑>침낭'</li><li>'다나산업 다나 골드익스페디션-M 침낭 스포츠/레저>캠핑>침낭'</li></ul> |
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+ | 12.0 | <ul><li>'기프템 캠핑 경량 알류미늄 팩 망치 파운딩 해머 백패킹 힙오렌지 컬러 캠핑장의 힙스터 스포츠/레저>캠핑>텐트/타프용품>기타텐트/타프용품'</li><li>'아캄파 일자3구스토퍼 4P 스포츠/레저>캠핑>텐트/타프용품>기타텐트/타프용품'</li><li>'코베아 고스트 플러스 전용 PVC 그라운드시트 방수포 스포츠/레저>캠핑>텐트/타프용품>방수포/그라운드시트'</li></ul> |
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+ | 5.0 | <ul><li>'제리캔 스틸 철제 캠핑통 기름통 휘발유 스포츠/레저>캠핑>취사용품>기타취사용품'</li><li>'워터백 폴딩 버킷12L 스포츠/레저>캠핑>취사용품>설거지용품'</li><li>'네이처하이크 캠핑 접이식 설거지통 20L 멀티 방수 다용도 원형 바스켓 스포츠/레저>캠핑>취사용품>설거지용품'</li></ul> |
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+ | 0.0 | <ul><li>'야외 캠핑 가스 스토브 휴대용 프로판 히터 전자 점화 장치 핸드 워머 화구 텐트 스포츠/레저>캠핑>기타캠핑용품'</li><li>'캠핑 팩백 팩파우치 휴대용 도구가방 툴백 스포츠/레저>캠핑>기타캠핑용품'</li><li>'등산 캠핑 비상용 파라코드 생존팔찌 조난생존팔찌 스포츠/레저>캠핑>기타캠핑용품'</li></ul> |
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+ | 10.0 | <ul><li>'캠핑클럽 사이드월 타프 420 스포츠/레저>캠핑>타프'</li><li>'쾌청 립스탑 렉타 타프 2 x 2m 스포츠/레저>캠핑>타프'</li><li>'네이처하이크 NH 4M 렉타 타프 스포츠/레저>캠핑>타프'</li></ul> |
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+ | 8.0 | <ul><li>'호토 에어 매트리스 QWOGJ003 스포츠/레저>캠핑>캠핑매트'</li><li>'토토비즈 힐링 에스닉 캠핑매트 TM-H017 스포츠/레저>캠핑>캠핑매트'</li><li>'어반카모 팽창식 침대 캠핑매트 40cm 싱글 스포츠/레저>캠핑>캠핑매트'</li></ul> |
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+ | 11.0 | <ul><li>'하이브로우 블랙테일 3 텐트 스포츠/레저>캠핑>텐트>2-3인용'</li><li>'코베아 몬스터 터널형 텐트 KECO9TO 4인용 스포츠/레저>캠핑>텐트>3-4인용'</li><li>'노스피크 스타쉽 텐트 4인용 스포츠/레저>캠핑>텐트>3-4인용'</li></ul> |
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+ | 13.0 | <ul><li>'다이팩토리 파워뱅크 DF 230A 스포츠/레저>캠핑>파워뱅크'</li><li>'에코플로우 리버 맥스 플러스 파워뱅크 60Ah 스포츠/레저>캠핑>파워뱅크'</li><li>'잭커리 휴대용 파워뱅크 L 사이즈 수납가방 ro ro 전용 1500P 2000P 스포츠/레저>캠핑>파워뱅크'</li></ul> |
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+ | 2.0 | <ul><li>'카즈미 소프트 쿨러 스카디 25L 보냉백 보냉가방 아이스박스 스포츠/레저>캠핑>아이스박스'</li><li>'피크닉가방 쿨러백 캠핑 보온보냉 대용량 25L 보냉백 스포츠/레저>캠핑>아이스박스'</li><li>'세븐플로어 POLARIS 폴라리스 C1 빙점하팩 아이스팩 영하 캠핑 휴대용 쿨러 얼음 냉동 스포츠/레저>캠핑>아이스박스'</li></ul> |
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+ | 9.0 | <ul><li>'헤비 듀티 대용량 접이식 왜건 쇼핑 비치 가든 풀 트롤리 야외 휴대용 유틸리티 카트 스포츠/레저>캠핑>캠핑왜건'</li><li>'캠파오 캠핑웨건 접이식 카트 뒷문개방 왜건 스포츠/레저>캠핑>캠핑왜건'</li><li>'캠핑용 수레 경량 접이식 폴딩 웨건카트 스포츠/레저>캠핑>캠핑왜건'</li></ul> |
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+ | 4.0 | <ul><li>'프리폼 캐노피 천막 2x2m 스포츠/레저>캠핑>천막'</li><li>'런웨이브 천막캐노피용 일반 바람막이 4면세트 3mX2m 스포츠/레저>캠핑>천막'</li><li>'리브포어스 접이식 캐노피 천막 벨크로 타입 투명벽면 풀세트 2m x 2m 스포츠/레저>캠핑>천막'</li></ul> |
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+ | 3.0 | <ul><li>'캠피어 워터저그 10L 스포츠/레저>캠핑>워터저그'</li><li>'코베아 하드 워터탱크 15L 스포츠/레저>캠핑>워터저그'</li><li>'스탠리 워터저그 수도꼭지 + 스텐인리스판 스포츠/레저>캠핑>워터저그'</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** | 1.0 |
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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_cate_sl28")
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+ # Run inference
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+ preds = model("애몰라이트 후레쉬 AM1 표준슬립 손전등 스포츠/레저>캠핑>랜턴>손전등")
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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 | 7.8108 | 22 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0.0 | 70 |
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+ | 1.0 | 70 |
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+ | 2.0 | 70 |
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+ | 3.0 | 25 |
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+ | 4.0 | 30 |
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+ | 5.0 | 70 |
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+ | 6.0 | 70 |
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+ | 7.0 | 70 |
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+ | 8.0 | 70 |
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+ | 9.0 | 70 |
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+ | 10.0 | 70 |
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+ | 11.0 | 70 |
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+ | 12.0 | 70 |
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+ | 13.0 | 26 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (256, 256)
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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: 50
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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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+ | 0.0060 | 1 | 0.5164 | - |
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+ | 0.2994 | 50 | 0.4984 | - |
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+ | 0.5988 | 100 | 0.4882 | - |
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+ | 0.8982 | 150 | 0.1544 | - |
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+ | 1.1976 | 200 | 0.0264 | - |
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+ | 1.4970 | 250 | 0.0089 | - |
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+ | 1.7964 | 300 | 0.0027 | - |
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+ | 2.0958 | 350 | 0.0003 | - |
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+ | 2.3952 | 400 | 0.0002 | - |
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+ | 2.6946 | 450 | 0.0001 | - |
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+ | 2.9940 | 500 | 0.0001 | - |
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+ | 3.2934 | 550 | 0.0001 | - |
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+ | 3.5928 | 600 | 0.0001 | - |
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+ | 3.8922 | 650 | 0.0001 | - |
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+ | 4.1916 | 700 | 0.0001 | - |
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+ | 4.4910 | 750 | 0.0 | - |
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+ | 4.7904 | 800 | 0.0 | - |
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+ | 5.0898 | 850 | 0.0 | - |
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+ | 5.3892 | 900 | 0.0 | - |
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+ | 5.6886 | 950 | 0.0 | - |
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+ | 5.9880 | 1000 | 0.0 | - |
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+ | 6.2874 | 1050 | 0.0 | - |
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+ | 6.5868 | 1100 | 0.0 | - |
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+ | 6.8862 | 1150 | 0.0 | - |
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+ | 7.1856 | 1200 | 0.0 | - |
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+ | 7.4850 | 1250 | 0.0 | - |
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+ | 7.7844 | 1300 | 0.0 | - |
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+ | 8.0838 | 1350 | 0.0 | - |
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+ | 8.3832 | 1400 | 0.0 | - |
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+ | 8.6826 | 1450 | 0.0 | - |
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+ | 8.9820 | 1500 | 0.0 | - |
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+ | 9.2814 | 1550 | 0.0 | - |
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+ | 9.5808 | 1600 | 0.0 | - |
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+ | 9.8802 | 1650 | 0.0 | - |
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+ | 10.1796 | 1700 | 0.0 | - |
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+
280
+ ### Framework Versions
281
+ - Python: 3.10.12
282
+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
284
+ - 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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+
289
+ ## Citation
290
+
291
+ ### BibTeX
292
+ ```bibtex
293
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
294
+ doi = {10.48550/ARXIV.2209.11055},
295
+ url = {https://arxiv.org/abs/2209.11055},
296
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
297
+ 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},
299
+ publisher = {arXiv},
300
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
303
+ ```
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+
305
+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
309
+ -->
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+
311
+ <!--
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+ ## Model Card Authors
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+
314
+ *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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+ -->
316
+
317
+ <!--
318
+ ## 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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