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mini1013
/
master_cate_top_bt13_3

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 +393 -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: '[시세이도프로페셔널/손상모발용] 서브리믹 아쿠아 인텐시브 샴푸 (리필) 450ml DepartmentSsg > 명품화장품 > 향수/바디/헤어
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+ > 헤어케어 > 샴푸/트리트먼트 DepartmentSsg > 명품화장품 > 향수/바디/헤어 > 헤어케어 > 샴푸/트리트먼트'
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+ - text: 제이숲 컬러제이 오로라 보색 샴푸 핑크 380ml C02 오로라 보색 샴푸 (핑크) (#M)화장품/미용>헤어케어>샴푸 AD > traverse
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+ > Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 보색샴푸
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+ - text: '정샘물 살롱집 단백질 리차징 샴푸 1,000ml [ : 트리트먼트] 리차징 샴푸 1000ml+트리트먼트 (#M)화장품/미용>헤어케어>샴푸
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+ AD > Naverstore > 화장품/미용 > 시트러스 > 샴푸'
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+ - text: '[유니크앤몰] 미쟝센 에이징 케어 파워베리 샴푸 1000ml 1000ml × 3개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸
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+ Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸'
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+ - text: 히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 >
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+ 기능성
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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: 0.8699763593380615
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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>'라보에이치 탈모증상완화 두피강화 샴푸 페어앤프리지아 400ml × 2개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>기능성샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 기능성샴푸'</li><li>'[라보에이치] 탈모증상완화 샴푸 댄드러프클리닉 일반건성비듬 400ml (#M)11st>헤어케어>탈모/두피관리제>헤어토닉 11st > 뷰티 > 헤어케어 > 탈모/두피관리제 > 헤어토닉'</li><li>'탈모완화 기능성 헤모나후코이단샴푸 460ml 300ml 헤모나샴푸 460ml (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성'</li></ul> |
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+ | 0 | <ul><li>'코랩 드라이 샴푸 200ml2개 50ml2개 물없이감는 사춘기 초등학생 청소년 올리브영 유니콘 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'1+1 코랩 올리브영 드라이샴푸 프레쉬 200ml 오리지널_유니콘 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 드라이샴푸'</li><li>'바티스트 드라이샴푸 11종 중 택1 04_스위티 200ml 11st>헤어케어>샴푸>일반;11st > 뷰티 > 헤어케어 > 샴푸 11st > 뷰티 > 헤어케어 > 샴푸 > 일반'</li></ul> |
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+ | 2 | <ul><li>'엘라스틴 실크리페어 퍼펙트 샤이닝 샴푸 1200ml x2개 ssg > 뷰티 > 미용기기/소품 > 바디관리기기;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸;ssg > 뷰티 > 헤어/바디 > 헤어케어 ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 바디워시'</li><li>'오가니스트 티트리 비듬 세정 샴푸 500ml x3개 MinSellAmount (#M)바디/헤어>헤어케어>샴푸/린스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 샴푸/린스'</li><li>'오가니스트 체리블라썸 샴푸 500ml x 3개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 샴푸 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸'</li></ul> |
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+ | 1 | <ul><li>'자연이랑 약산성 샴푸바2+린스바1 특별가/제로웨이스트 올인원 고체샴푸/고체린스 청대오일샴푸바2+린스바1_선택안함 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</li><li>'케세리 샴푸바 2개입 100g+100g 비건 퍼퓸 약산성 올인원 비누 [모발영양]딥 너리싱 샴푸바 200g (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 시트러스 > 샴푸'</li><li>'어성초자소엽녹차 샴푸바 로즈마리 자연 샴푸바 1개 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</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** | 0.8700 |
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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_top_bt13_3")
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+ # Run inference
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+ preds = model("히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성")
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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 | 24.02 | 122 |
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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 | 0.4979 | - |
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+ | 0.1597 | 50 | 0.4236 | - |
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+ | 0.3195 | 100 | 0.3685 | - |
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+ | 0.4792 | 150 | 0.2863 | - |
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+ | 0.6390 | 200 | 0.1407 | - |
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+ | 0.7987 | 250 | 0.0265 | - |
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+ | 0.9585 | 300 | 0.0059 | - |
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+ | 1.1182 | 350 | 0.0046 | - |
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+ | 1.2780 | 400 | 0.0003 | - |
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+ | 1.4377 | 450 | 0.0001 | - |
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+ | 1.5974 | 500 | 0.0002 | - |
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+ | 1.7572 | 550 | 0.0001 | - |
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+ | 1.9169 | 600 | 0.0001 | - |
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+ | 2.0767 | 650 | 0.0001 | - |
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+ | 2.2364 | 700 | 0.0001 | - |
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+ | 2.3962 | 750 | 0.0 | - |
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+ | 2.5559 | 800 | 0.0 | - |
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+ | 2.7157 | 850 | 0.0 | - |
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+ | 2.8754 | 900 | 0.0 | - |
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+ | 3.0351 | 950 | 0.0 | - |
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+ | 3.1949 | 1000 | 0.0 | - |
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+ | 3.3546 | 1050 | 0.0 | - |
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+ | 3.5144 | 1100 | 0.0 | - |
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+ | 3.6741 | 1150 | 0.0 | - |
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+ | 3.8339 | 1200 | 0.0 | - |
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+ | 3.9936 | 1250 | 0.0 | - |
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+ | 4.1534 | 1300 | 0.0 | - |
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+ | 4.3131 | 1350 | 0.0 | - |
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+ | 4.4728 | 1400 | 0.0 | - |
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+ | 4.6326 | 1450 | 0.0 | - |
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+ | 4.7923 | 1500 | 0.0 | - |
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+ | 4.9521 | 1550 | 0.0 | - |
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+ | 5.1118 | 1600 | 0.0 | - |
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+ | 5.2716 | 1650 | 0.0 | - |
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+ | 5.4313 | 1700 | 0.0 | - |
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+ | 5.5911 | 1750 | 0.0 | - |
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+ | 5.7508 | 1800 | 0.0 | - |
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+ | 5.9105 | 1850 | 0.0 | - |
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+ | 6.0703 | 1900 | 0.0 | - |
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+ | 6.2300 | 1950 | 0.0 | - |
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+ | 6.3898 | 2000 | 0.0 | - |
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+ | 6.5495 | 2050 | 0.0 | - |
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+ | 6.7093 | 2100 | 0.0 | - |
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+ | 6.8690 | 2150 | 0.0 | - |
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+ | 7.0288 | 2200 | 0.0007 | - |
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+ | 7.1885 | 2250 | 0.0027 | - |
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+ | 7.3482 | 2300 | 0.0008 | - |
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+ | 7.5080 | 2350 | 0.0 | - |
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+ | 7.6677 | 2400 | 0.0 | - |
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+ | 7.8275 | 2450 | 0.0 | - |
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+ | 7.9872 | 2500 | 0.0 | - |
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+ | 8.1470 | 2550 | 0.0 | - |
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+ | 8.3067 | 2600 | 0.0 | - |
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+ | 8.4665 | 2650 | 0.0 | - |
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+ | 8.9457 | 2800 | 0.0 | - |
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+ | 9.2652 | 2900 | 0.0 | - |
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+ | 9.5847 | 3000 | 0.0 | - |
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+ | 10.0639 | 3150 | 0.0 | - |
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+ | 10.3834 | 3250 | 0.0 | - |
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+ | 10.5431 | 3300 | 0.0 | - |
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+ | 10.7029 | 3350 | 0.0 | - |
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+ | 10.8626 | 3400 | 0.0 | - |
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+ | 11.0224 | 3450 | 0.0 | - |
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+ | 11.1821 | 3500 | 0.0 | - |
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+ | 11.5016 | 3600 | 0.0 | - |
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+ | 11.6613 | 3650 | 0.0 | - |
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+ | 11.8211 | 3700 | 0.0 | - |
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+ | 11.9808 | 3750 | 0.0 | - |
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+ | 12.1406 | 3800 | 0.0 | - |
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+ | 12.3003 | 3850 | 0.0 | - |
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+ | 12.4601 | 3900 | 0.0 | - |
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+ | 12.7796 | 4000 | 0.0 | - |
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+ | 12.9393 | 4050 | 0.0 | - |
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+ | 13.0990 | 4100 | 0.0 | - |
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+ | 13.4185 | 4200 | 0.0 | - |
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+ | 15.6550 | 4900 | 0.0 | - |
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+ | 16.1342 | 5050 | 0.0 | - |
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+ | 17.8914 | 5600 | 0.0 | - |
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+ | 18.0511 | 5650 | 0.0 | - |
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+
352
+ ### Framework Versions
353
+ - Python: 3.10.12
354
+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
356
+ - 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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+
361
+ ## Citation
362
+
363
+ ### BibTeX
364
+ ```bibtex
365
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
366
+ doi = {10.48550/ARXIV.2209.11055},
367
+ url = {https://arxiv.org/abs/2209.11055},
368
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
369
+ 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},
371
+ publisher = {arXiv},
372
+ year = {2022},
373
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
375
+ ```
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+
377
+ <!--
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+ ## Glossary
379
+
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+ *Clearly define terms in order to be accessible across audiences.*
381
+ -->
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+
383
+ <!--
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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.*
387
+ -->
388
+
389
+ <!--
390
+ ## 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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