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mini1013
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Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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  1. 1_Pooling/config.json +10 -0
  2. README.md +388 -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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+ 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: 닥터 브로너스 그린티 퓨어 캐스틸 바솝 140g 3개 옵션없음 (주)엠아이인터내셔널
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+ - text: 에치앤지 코스노리 아이래쉬 틴팅 세럼 9g 옵션없음 탑서비스
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+ - text: '[VT] 피디알엔 리들샷 옵션없음 (주)지에스리테일 홈쇼핑'
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+ - text: 1950년대 영국체어 옵션없음 4Umall (포유몰)
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+ - text: Tip Top 팁탑 포마드 오리지널 120g [한정수량할인] 바르노 포마드_01 바르노 오리지널(수성) 주식회사 설빈
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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: 0.8909090909090909
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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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+ | 1 | <ul><li>'바디콕 발톱솔루션 문제성 발톱 영양제 손발톱 강화제 진정 손톱 케어제 큐어유 [갯수한정] 3개월분 ⭐패밀리 패키지⭐ 주식회사 씨앤알커머스'</li><li>'요거트젤 젤라또 파스텔시럽젤 9종 [단품] S29 네일컴퍼니'</li><li>'그라시아 크레이지 탑 띠크 25g 크레이지탑젤 스탠다드 40g 지구글로벌 스토어'</li></ul> |
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+ | 6 | <ul><li>'래쉬홀릭 3D 속눈썹 영양제 옵션없음 뷰티맨이야'</li><li>'키치캐치 치키 컬러 밤 (8 Colors) PLAYFUL 주식회사 링크스'</li><li>'입큰 플러피 듀얼 립 펜슬 라이너 1호 미닝피치 X 2개 옵션없음 비엔엠코리아'</li></ul> |
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+ | 8 | <ul><li>'에스트라 아스트라 아토베리어 로션 MD 200ml 1개 옵션없음 케이니크'</li><li>'Alpinestars 넥 튜브블랙 레드 플루오 블랙 레드_One 사이즈 브릭스썬'</li><li>'테라로직 정상가 38000원 펜타마이드 리얼 브라이트닝 10C 앰플 50ml 옵션없음 엠비즈에이'</li></ul> |
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+ | 2 | <ul><li>'토시도 아이존 시트 20매 / 면 눈가 전용 아이 시트지 부직포 옵션없음 페이즈'</li><li>'[NEW] 해서린 포어 멜팅 와이드 슈링코팩 4매 옵션없음 휴리빙'</li><li>'하비언니X노모크 수면팩 48구 주식회사 디스토리'</li></ul> |
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+ | 7 | <ul><li>'바나나보트 에프터썬 알로에젤 수딩젤 473ml 3개입 바나나보트 알로에젤 473ml/3개입 스테디세일러'</li><li>'코레스 코코넛-아몬드 키즈 컴포트 썬 스프레이 SPF50 150ml / KORRES 옵션없음 Minsun Kim'</li><li>'바이오가 수딩젤 피토스핑고신 스쿠알렌 글루세린 250ML 1개 바이오가 수딩젤 피토스핑고신 250ML 1�� 레몽샵'</li></ul> |
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+ | 5 | <ul><li>'쌍테 누드스킨 슬림형 쌍커풀테이프 쌍커플테이프 쌍거풀테이프 옵션없음 셀렉트림'</li><li>'새로핸즈 스프레이 투명 유리 검정캡 10ml (스크류타입) 옵션없음 (주)새로핸즈'</li><li>'원씽 원형 빅 패드 70매입 옵션없음 (주)원씽'</li></ul> |
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+ | 3 | <ul><li>'Lush Silky Underwear Dusting Powder 러쉬 실키 언더웨어 더스트 더스팅 파우더 60g 2팩 옵션없음 이펄 Effal2'</li><li>'다이나믹스킨 왁싱재료 왁싱부직포 100매 무슬린천 옵션없음 코리안집시'</li><li>'누리네 때비누 황토/쑥 누리네 때비누 황토 주식회사 억조상사'</li></ul> |
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+ | 0 | <ul><li>'니베아 립케어 립밤 5.5ml 모이스처 옵션없음 삼성메디원'</li><li>'라끄베르 옴므 리:차지 2종세트 옵션없음 뷰티트리'</li><li>'피지오겔 DMT 크림150 옵션없음 (주)지에스리테일 홈쇼핑'</li></ul> |
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+ | 4 | <ul><li>'에이지투웨니스 블랙 골드 팩트 본품+리필+커피쿠폰 23호 미디움 베이지+커피쿠폰 주식회사 아이홀릭'</li><li>'입생로랑 NEW 메쉬 핑크 쿠션 12g 리필 B25 옵션없음 쇼핑사거리'</li><li>'힌스 세컨 스킨 메쉬 매트 쿠션(본품+리필) [본품+리필] 17 포슬린 하늘바다너머'</li></ul> |
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+ | 9 | <ul><li>'[본사제품최신제조] 인셀덤 엑티브 클린업 파우더 90g 폼 클렌징 가루 효소 세안제 옵션없음 미라클'</li><li>'[제로이드] 인텐시브 페이셜 크림 클렌저 180ml 옵션없음 주식회사 트리티스'</li><li>'라미 라피네 야채 딥 300ml 옵션없음 행복이'</li></ul> |
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+ | 11 | <ul><li>'미쟝센 스타일 케어 프로 스트롱 홀드 스프레이 300ml/ (2개이상 복수구매시 추가할인) 옵션없음 다판다'</li><li>'웰라프로페셔널 일루미나 컬러 염색약 80g 산화제미포함 일루미나산화제미포함_웰라 일루미나 블로썸-6 [80g] 티비'</li><li>'아모스 에센셜 시스테인 펌 150ml + 150ml 시스테인펌제 옵션없음 티비'</li></ul> |
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+ | 12 | <ul><li>'이브로셰 라즈베리 헤어식초 400ml 8750943 옵션없음 비티엘파트너'</li><li>'이브로쉐 리프레쉬 헤어식초(모링가) 400ml 옵션없음 스루치로 유한책임회사'</li><li>'[우신] R&B(알앤비) 아로마 pH 컨트롤 500ml 피에이치 컨트롤 컬강화제 옵션없음 송이뷰티'</li></ul> |
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+ | 10 | <ul><li>'지미추 플로럴 EDT 40ml+랜덤쇼핑백 옵션없음 아이스큐브'</li><li>'라벤더 프렌치 유기농 에센셜오일 100ml 대용량 고농축 허브 아로마오일 불면증에좋은 숙면 테라피 디퓨저 옵션없음 마이소르 주식회사'</li><li>'조말론 블랙베리 앤 베이 오 드 코롱 50ml 옵션없음 블랑블랑'</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** | 0.8909 |
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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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+
90
+ First install the SetFit library:
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+
92
+ ```bash
93
+ pip install setfit
94
+ ```
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+
96
+ Then you can load this model and run inference.
97
+
98
+ ```python
99
+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("setfit_model_id")
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+ # Run inference
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+ preds = model("1950년대 영국체어 옵션없음 4Umall (포유몰)")
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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 | 9.8008 | 33 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 1281 |
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+ | 1 | 582 |
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+ | 2 | 681 |
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+ | 3 | 1592 |
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+ | 4 | 587 |
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+ | 5 | 706 |
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+ | 6 | 1206 |
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+ | 7 | 587 |
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+ | 8 | 1081 |
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+ | 9 | 1077 |
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+ | 10 | 224 |
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+ | 11 | 567 |
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+ | 12 | 699 |
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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 | 0.4342 | - |
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+ | 0.0588 | 50 | 0.3693 | - |
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+ | 0.1176 | 100 | 0.3229 | - |
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+ | 0.1765 | 150 | 0.2888 | - |
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+ | 0.2353 | 200 | 0.2413 | - |
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+ | 0.2941 | 250 | 0.2136 | - |
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+ | 0.3529 | 300 | 0.1925 | - |
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+ | 0.4118 | 350 | 0.1672 | - |
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+ | 0.4706 | 400 | 0.1529 | - |
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+ | 0.5294 | 450 | 0.13 | - |
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+ | 0.5882 | 500 | 0.1112 | - |
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+ | 0.6471 | 550 | 0.0979 | - |
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+ | 0.7059 | 600 | 0.0873 | - |
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+ | 0.7647 | 650 | 0.0575 | - |
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+ | 0.8235 | 700 | 0.0482 | - |
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+ | 0.8824 | 750 | 0.0729 | - |
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+ | 0.9412 | 800 | 0.0411 | - |
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+ | 1.0 | 850 | 0.0542 | - |
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+ | 1.0588 | 900 | 0.0626 | - |
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+ | 1.1176 | 950 | 0.0385 | - |
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+ | 1.1765 | 1000 | 0.0373 | - |
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+ | 1.2353 | 1050 | 0.0276 | - |
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+ | 1.2941 | 1100 | 0.0205 | - |
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+ | 1.3529 | 1150 | 0.0275 | - |
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+ | 1.4118 | 1200 | 0.0226 | - |
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+ | 1.4706 | 1250 | 0.0231 | - |
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+ | 1.5294 | 1300 | 0.0273 | - |
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+ | 1.5882 | 1350 | 0.0183 | - |
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+ | 1.6471 | 1400 | 0.0158 | - |
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+ | 1.7059 | 1450 | 0.0112 | - |
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+ | 1.7647 | 1500 | 0.0068 | - |
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+ | 1.8235 | 1550 | 0.0098 | - |
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+ | 1.8824 | 1600 | 0.0047 | - |
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+ | 1.9412 | 1650 | 0.0053 | - |
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+ | 2.0 | 1700 | 0.0027 | - |
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+ | 2.0588 | 1750 | 0.0007 | - |
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+ | 2.1176 | 1800 | 0.0015 | - |
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+ | 2.1765 | 1850 | 0.0042 | - |
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+ | 2.2353 | 1900 | 0.002 | - |
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+ | 2.2941 | 1950 | 0.0018 | - |
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+ | 2.3529 | 2000 | 0.0023 | - |
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+ | 2.4118 | 2050 | 0.0025 | - |
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+ | 2.4706 | 2100 | 0.0014 | - |
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+ | 2.5294 | 2150 | 0.0007 | - |
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+ | 2.5882 | 2200 | 0.0005 | - |
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+ | 2.6471 | 2250 | 0.0042 | - |
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+ | 2.7059 | 2300 | 0.0022 | - |
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+ | 2.7647 | 2350 | 0.0028 | - |
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+ | 2.8235 | 2400 | 0.0004 | - |
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+ | 2.8824 | 2450 | 0.0003 | - |
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+ | 2.9412 | 2500 | 0.0009 | - |
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+ | 3.0 | 2550 | 0.0002 | - |
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+ | 3.0588 | 2600 | 0.0011 | - |
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+ | 3.1176 | 2650 | 0.001 | - |
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+ | 3.1765 | 2700 | 0.0003 | - |
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+ | 3.2353 | 2750 | 0.0006 | - |
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+ | 3.2941 | 2800 | 0.0034 | - |
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+ | 3.3529 | 2850 | 0.0002 | - |
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+ | 3.4118 | 2900 | 0.0012 | - |
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+ | 3.4706 | 2950 | 0.0004 | - |
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+ | 3.5294 | 3000 | 0.0004 | - |
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+ | 3.5882 | 3050 | 0.0002 | - |
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+ | 3.6471 | 3100 | 0.0002 | - |
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+ | 3.7059 | 3150 | 0.0002 | - |
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+ | 3.7647 | 3200 | 0.0001 | - |
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+ | 3.8235 | 3250 | 0.002 | - |
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+ | 3.8824 | 3300 | 0.0026 | - |
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+ | 3.9412 | 3350 | 0.0001 | - |
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+ | 4.0 | 3400 | 0.0001 | - |
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+ | 4.0588 | 3450 | 0.0001 | - |
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+ | 4.1176 | 3500 | 0.0003 | - |
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+ | 4.1765 | 3550 | 0.0001 | - |
247
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+
347
+ ### Framework Versions
348
+ - Python: 3.10.12
349
+ - SetFit: 1.1.0.dev0
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+ - 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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+
356
+ ## Citation
357
+
358
+ ### BibTeX
359
+ ```bibtex
360
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
361
+ doi = {10.48550/ARXIV.2209.11055},
362
+ url = {https://arxiv.org/abs/2209.11055},
363
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
364
+ 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},
366
+ publisher = {arXiv},
367
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
370
+ ```
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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.*
376
+ -->
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+
378
+ <!--
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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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+ -->
383
+
384
+ <!--
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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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