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mini1013
/
master_item_top_bt3

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +395 -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: klue/roberta-base
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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: 마스크 오브 매그너민티 315g - 파워 마스크/페이스 앤 바디 마스크 팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 >
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+ 입욕제;위메프 > 뷰티 > 스킨케어 > 팩/마스크;위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 워시오프팩 /필오프팩;위메프 > 뷰티 > 클렌징/필링
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+ > 클렌징;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모 > 입욕제;(#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크
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+ > 마스크시트팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 입욕제
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+ - text: '[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용
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+ > 마스크/팩 > 수면팩'
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+ - text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 38개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn
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+ > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩
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+ - text: 메디힐 마스크팩 티트리 베스트 10매 세트 수분 미백 여드름 비타 라이트빔 에센셜[10매] 홈>화장품/미용>마스크/팩>마스크시트;홈>전체상품;(#M)홈>브랜드관>메디힐
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+ Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트
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+ - text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 29개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>시트마스크 Coupang >
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+ 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크
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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: accuracy
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+ value: 0.7775471698113208
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+ name: Accuracy
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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:** 4 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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+ | 3 | <ul><li>'차앤박 CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분) (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'</li><li>'미팩토리 3단 돼지코팩 10개입 × 3개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>코팩 Coupang > 뷰티 > 스킨케어 > 마스크/팩'</li><li>'[차앤박] CNP 안티포어 블랙헤드 버블 코팩 1매 / 넓은 모공 피부 / (#M)화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩'</li></ul> |
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+ | 0 | <ul><li>'메디힐×마리끌레르 기획전 앰플/크림/마스크팩~58% 25_메디힐 티트리 케어솔루션 에센셜마스크 [10매] 쇼킹딜 홈>뷰티>클렌징/팩/마스크>팩/마스크;11st>스킨케어>팩/마스크>마스크시트팩;(#M)11st>뷰티>클렌징/팩/마스크>팩/마스크 11st Hour Event > 패션/뷰티 > 뷰티 > 클렌징/팩/마스크 > 팩/마스크'</li><li>'[의료기기] 듀오덤 스팟패치 72매 [의료기기] 듀오덤 스팟패치 72매 (#M)홈>구강/건강용품>패치/겔>스팟패치 OLIVEYOUNG > 베스트 > 구강/건강용품'</li><li>'이지덤 뷰티 릴리프 스팟패치 57개입 3개 (#M)쿠팡 홈>생활용품>건강/의료용품>의약외품/상비용품>반창고/밴드 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 스팟패치'</li></ul> |
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+ | 2 | <ul><li>'안스킨 클래리파잉 골드 모델링 팩 1000ml 20개 (#M)홈>화장품/미용>마스크/팩>필오프팩 Naverstore > 화장품/미용 > 마스크/팩 > 필오프팩'</li><li>'[러쉬]오티픽스 75g - 프레쉬 페이스 마스크/마스크 팩 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크;ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 입욕제/버블바스;ssg > 뷰티 > 스킨케어 > 마스크/팩;ssg > 뷰티 > 스킨케어 > 클렌징 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크'</li><li>'푸드어홀릭 콜라겐 필오프팩 150ml / 다시마 MinSellAmount (#M)화장품/향수>팩/마스크>필오프팩 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 필오프팩'</li></ul> |
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+ | 1 | <ul><li>'물광 콜라겐 크림 티르티르 80ml 생크림 도자기 피부 물광마스크 이유빈 콜라겐물광마스크40ml (#M)홈>전체상품 Naverstore > 화장품/미용 > 남성화장품 > 크림'</li><li>'립 슬리핑 마스크 EX 20g 4종 베리 자몽 민트초코 애플라임 베리 (#M)홈>화장품/미용>마스크/팩>수면팩 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩'</li><li>'설화수 한방 슬리핑마스크 나이트여운팩 120ml 1개 (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩'</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** | 0.7775 |
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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.
95
+
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+ ```python
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+ from setfit import SetFitModel
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+
99
+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_item_top_bt3")
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+ # Run inference
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+ preds = model("[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩")
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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 | 21.75 | 91 |
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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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+
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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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+ | 0.0032 | 1 | 0.4549 | - |
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+ | 0.1597 | 50 | 0.3933 | - |
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+ | 0.3195 | 100 | 0.3669 | - |
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+ | 0.4792 | 150 | 0.2841 | - |
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+ | 0.6390 | 200 | 0.1163 | - |
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+ | 0.7987 | 250 | 0.0104 | - |
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+ | 0.9585 | 300 | 0.0072 | - |
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+ | 1.1182 | 350 | 0.0065 | - |
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+ | 1.2780 | 400 | 0.0059 | - |
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+ | 1.4377 | 450 | 0.0058 | - |
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+ | 1.5974 | 500 | 0.0035 | - |
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+ | 1.7572 | 550 | 0.0032 | - |
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+ | 1.9169 | 600 | 0.0032 | - |
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+ | 2.0767 | 650 | 0.0025 | - |
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+ | 2.2364 | 700 | 0.0023 | - |
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+ | 2.3962 | 750 | 0.0023 | - |
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+ | 2.5559 | 800 | 0.0025 | - |
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+ | 2.7157 | 850 | 0.0023 | - |
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+ | 2.8754 | 900 | 0.003 | - |
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+ | 3.0351 | 950 | 0.0026 | - |
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+ | 3.1949 | 1000 | 0.0043 | - |
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+ | 3.3546 | 1050 | 0.0022 | - |
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+ | 3.5144 | 1100 | 0.0024 | - |
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+ | 3.6741 | 1150 | 0.0025 | - |
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+ | 3.8339 | 1200 | 0.0025 | - |
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+ | 3.9936 | 1250 | 0.0024 | - |
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+ | 4.1534 | 1300 | 0.0025 | - |
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+ | 4.3131 | 1350 | 0.0025 | - |
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+ | 4.4728 | 1400 | 0.0027 | - |
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+ | 4.6326 | 1450 | 0.0023 | - |
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+ | 4.7923 | 1500 | 0.0022 | - |
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+ | 4.9521 | 1550 | 0.0026 | - |
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+ | 5.1118 | 1600 | 0.0022 | - |
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+ | 5.2716 | 1650 | 0.0027 | - |
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+ | 5.4313 | 1700 | 0.0022 | - |
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+ | 5.5911 | 1750 | 0.0024 | - |
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+ | 5.7508 | 1800 | 0.0029 | - |
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+ | 5.9105 | 1850 | 0.0018 | - |
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+ | 6.0703 | 1900 | 0.0033 | - |
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+ | 6.2300 | 1950 | 0.002 | - |
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+ | 6.3898 | 2000 | 0.0027 | - |
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+ | 6.5495 | 2050 | 0.0021 | - |
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+ | 6.7093 | 2100 | 0.0022 | - |
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+ | 6.8690 | 2150 | 0.0023 | - |
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+ | 7.0288 | 2200 | 0.0026 | - |
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+ | 7.1885 | 2250 | 0.0018 | - |
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+ | 7.3482 | 2300 | 0.0024 | - |
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+ | 7.5080 | 2350 | 0.002 | - |
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+ | 7.6677 | 2400 | 0.0027 | - |
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+ | 7.8275 | 2450 | 0.0022 | - |
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+ | 7.9872 | 2500 | 0.0032 | - |
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+ | 8.1470 | 2550 | 0.0029 | - |
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+ | 8.3067 | 2600 | 0.0025 | - |
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+ | 8.4665 | 2650 | 0.0017 | - |
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+ | 8.6262 | 2700 | 0.0026 | - |
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+ | 8.7859 | 2750 | 0.0023 | - |
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+ | 8.9457 | 2800 | 0.0023 | - |
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+ | 9.1054 | 2850 | 0.0029 | - |
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+ | 9.2652 | 2900 | 0.0028 | - |
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+ | 9.4249 | 2950 | 0.0021 | - |
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+ | 9.5847 | 3000 | 0.0027 | - |
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+ | 9.7444 | 3050 | 0.0019 | - |
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+ | 9.9042 | 3100 | 0.0022 | - |
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+ | 10.0639 | 3150 | 0.003 | - |
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+ | 10.2236 | 3200 | 0.0024 | - |
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+ | 10.3834 | 3250 | 0.0019 | - |
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+ | 10.5431 | 3300 | 0.0023 | - |
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+ | 10.7029 | 3350 | 0.0024 | - |
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+ | 10.8626 | 3400 | 0.0026 | - |
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+ | 11.0224 | 3450 | 0.0025 | - |
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+ | 11.1821 | 3500 | 0.0022 | - |
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+ | 11.3419 | 3550 | 0.0023 | - |
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+ | 11.5016 | 3600 | 0.0027 | - |
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+ | 11.6613 | 3650 | 0.0032 | - |
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+ | 11.8211 | 3700 | 0.0022 | - |
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+ | 11.9808 | 3750 | 0.0019 | - |
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+ | 12.1406 | 3800 | 0.0029 | - |
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+ | 12.3003 | 3850 | 0.0026 | - |
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+ | 12.4601 | 3900 | 0.0027 | - |
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+ | 12.6198 | 3950 | 0.0019 | - |
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+ | 12.7796 | 4000 | 0.0021 | - |
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+ | 12.9393 | 4050 | 0.0023 | - |
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+ | 13.0990 | 4100 | 0.0027 | - |
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+ | 13.2588 | 4150 | 0.0021 | - |
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+ | 13.4185 | 4200 | 0.0022 | - |
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+ | 13.5783 | 4250 | 0.0026 | - |
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+ | 13.7380 | 4300 | 0.0025 | - |
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+ | 13.8978 | 4350 | 0.0025 | - |
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+ | 14.0575 | 4400 | 0.0021 | - |
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+ | 14.2173 | 4450 | 0.0031 | - |
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+ | 14.3770 | 4500 | 0.0022 | - |
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+
354
+ ### Framework Versions
355
+ - Python: 3.10.12
356
+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
358
+ - 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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+
363
+ ## Citation
364
+
365
+ ### BibTeX
366
+ ```bibtex
367
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
368
+ doi = {10.48550/ARXIV.2209.11055},
369
+ url = {https://arxiv.org/abs/2209.11055},
370
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
371
+ 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},
373
+ publisher = {arXiv},
374
+ year = {2022},
375
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
377
+ ```
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+
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+ <!--
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+ ## Glossary
381
+
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+ *Clearly define terms in order to be accessible across audiences.*
383
+ -->
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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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+ -->
390
+
391
+ <!--
392
+ ## 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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