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mini1013
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master_cate_bt_top11_test

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 +390 -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: mini1013/master_domain
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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: '[VDAY] 포멜로 파라디 30ml 발렌타인 세트 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded
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+ > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'
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+ - text: 톰포드 비터 피치 오 드 퍼퓸 50ml LotteOn > 뷰티 > 향수 > 여성향수 LotteOn > 뷰티 > 향수 > 여성향수
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+ - text: 샹스 오 드 빠르펭 35ml ssg > 뷰티 > 향수 > 여성향수 ssg > 뷰티 > 향수 > 여성향수
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+ - text: 미라클 EDP 100ml LotteOn > 뷰티 > 향수 > 여성향수 LotteOn > 뷰티 > 향수 > 여성향수
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+ - text: 퍼플라벤다 플라워디퓨저 150ml_P050374133 투명병/아카시아 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품 ssg
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+ > 뷰티 > 미용기기/소품 > 거울/용기/기타소품
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+ inference: true
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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: 0.5350966429298067
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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:** 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>'로즈마리 아로마 에센셜 오일 12ml 천연 디퓨저 마사지 족욕 피로 페퍼민트 본상품선택 × 1 (#M)쿠팡 홈>생활용품>헤어/바디/세안>바디로션/크림>아로마오일 Coupang > 뷰티 > 바디 > 바디로션/크림 > 아로마오일'</li><li>'루브르 알라바스트 디퓨저 스톤&리필 5ml 세트 (알라바스트 케이스+스톤+리필액 구성) 사모트라케의 승리의 여신 니케(멜 마르스) ssg > 뷰티 > 향수 > 여성향수;ssg > 뷰티 > 향수 > 캔들/디퓨저/아로마 ssg > 뷰티 > 향수 > 캔들/디퓨저/아로마'</li><li>'아이캔디 윈드스핀 매직 볼륨 헤어컬 디퓨저 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 드라이샴푸;ssg > 뷰티 > 헤어/바디 > 헤어스타일링 ssg > 뷰티 > 헤어/바디 > 헤어케어 > 드라이샴푸'</li></ul> |
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+ | 0 | <ul><li>'코롱 50ml 택 1 (+스페셜 GIFT ) 5) 라임 바질 앤 만다린 코롱 50ml (#M)신세계백화점/향수/여성향수 LOREAL > DepartmentSsg > 아틀리에 코롱 > Generic > 핸드크림'</li><li>'오 드 쏠레이 블랑 100ML SSG.COM>신세계백화점TOM FORD>FRAGRANCES>SIGNATURE FRAGRANCES;(#M)SSG.COM>신세계백화점TOM FORD>FRAGRANCE>SIGNATURE ssg > 뷰티 > 향수 > 여성향수'</li><li>'[메종마르지엘라](신세계타임스퀘어점패션관)바이 더 파이어플레이스 EDT 30ML(공식수입정품) 바이 더 파이어플레이스 30ML+선물박스포장 (#M)홈>화장품/미용>향수>남녀공용향수 Naverstore > 화장품/미용 > 향수 > 남녀공용향수'</li></ul> |
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+ | 2 | <ul><li>'재고정리 오늘주문 내일도착 샤넬 코코 마드모아젤 우먼 오드퍼퓸 100ml 쇼핑백X (#M)11st>향수>여성향수>여성향수 11st > 뷰티 > 향수 > 여성향수'</li><li>'헉슬리 퍼퓸 블루 메디나 탠저린 15mL 홈>Fragrance;(#M)홈>프레그런스>향수 Naverstore > 화장품/미용 > 향수 > 남녀공용향수'</li><li>'조말론 오스만투스 블러썸 1온스 여성용 쾰른 스프레이 원래 언박스 단일상품 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디미스트/샤워코롱 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디미스트/샤워코롱'</li></ul> |
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+ | 1 | <ul><li>'히어로 EDT 50ml DepartmentLotteOn > 뷰티 > 향수 > 남성용 > 31ml~50ml DepartmentLotteOn > 뷰티 > 향수 > 남성용 > 51ml~'</li><li>'에르메스 떼르 데르메스 오 뜨레 프라쉐 EDT 125ml/관 MinSellAmount (#M)바디/헤어>바디케어>바디로션 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디로션'</li><li>'[AK백화점][한정판] 랄프로렌 폴로 블루 베어 에디션 EDT 40ML(+단독 폴로블루 파우치) 단일상품 `1095946223` LotteOn > 뷰티 > 향수 > 향수세트 LotteOn > 뷰티 > 향수 > 향수세트'</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** | 0.5351 |
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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_bt_top11_test")
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+ # Run inference
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+ preds = model("샹스 오 드 빠르펭 35ml ssg > 뷰티 > 향수 > 여성향수 ssg > 뷰티 > 향수 > 여성향수")
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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 | 12 | 21.735 | 48 |
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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 | 0.4649 | - |
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+ | 0.1597 | 50 | 0.4669 | - |
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+ | 0.3195 | 100 | 0.4469 | - |
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+ | 0.4792 | 150 | 0.4259 | - |
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+ | 0.6390 | 200 | 0.3685 | - |
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+ | 0.7987 | 250 | 0.2893 | - |
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+ | 0.9585 | 300 | 0.2223 | - |
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+ | 1.1182 | 350 | 0.1858 | - |
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+ | 1.2780 | 400 | 0.1766 | - |
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+ | 1.4377 | 450 | 0.1629 | - |
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+ | 1.5974 | 500 | 0.1571 | - |
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+ | 1.7572 | 550 | 0.1362 | - |
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+ | 1.9169 | 600 | 0.1208 | - |
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+ | 2.0767 | 650 | 0.1 | - |
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+ | 2.2364 | 700 | 0.0746 | - |
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+ | 2.3962 | 750 | 0.0699 | - |
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+ | 2.5559 | 800 | 0.0635 | - |
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+ | 2.7157 | 850 | 0.0464 | - |
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+ | 2.8754 | 900 | 0.0246 | - |
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+ | 3.0351 | 950 | 0.0195 | - |
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+ | 3.1949 | 1000 | 0.0063 | - |
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+ | 3.3546 | 1050 | 0.0004 | - |
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+ | 3.5144 | 1100 | 0.0003 | - |
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+ | 3.6741 | 1150 | 0.0001 | - |
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+ | 3.8339 | 1200 | 0.0002 | - |
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+ | 3.9936 | 1250 | 0.0007 | - |
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+ | 4.1534 | 1300 | 0.0006 | - |
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+ | 4.3131 | 1350 | 0.0001 | - |
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+ | 4.4728 | 1400 | 0.0002 | - |
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+ | 4.6326 | 1450 | 0.0001 | - |
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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 | 0.0001 | - |
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+ | 5.4313 | 1700 | 0.0 | - |
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+ | 5.5911 | 1750 | 0.0001 | - |
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+ | 5.7508 | 1800 | 0.0002 | - |
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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 | 0.0 | - |
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+ | 7.1885 | 2250 | 0.0002 | - |
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+ | 7.3482 | 2300 | 0.0 | - |
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+ | 7.5080 | 2350 | 0.0005 | - |
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+ | 7.6677 | 2400 | 0.0003 | - |
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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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+ | 8.6262 | 2700 | 0.0001 | - |
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+ | 8.7859 | 2750 | 0.0 | - |
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+ | 8.9457 | 2800 | 0.0 | - |
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+ | 9.1054 | 2850 | 0.0005 | - |
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+ | 9.2652 | 2900 | 0.0 | - |
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+ | 9.4249 | 2950 | 0.0 | - |
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+ | 9.5847 | 3000 | 0.0 | - |
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+ | 9.7444 | 3050 | 0.0002 | - |
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+ | 9.9042 | 3100 | 0.0047 | - |
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+ | 10.0639 | 3150 | 0.0088 | - |
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+ | 10.2236 | 3200 | 0.0031 | - |
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+ | 10.3834 | 3250 | 0.0001 | - |
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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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+ | 11.3419 | 3550 | 0.0 | - |
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+ | 11.5016 | 3600 | 0.0 | - |
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+ | 11.6613 | 3650 | 0.0001 | - |
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+ | 11.8211 | 3700 | 0.0002 | - |
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+ | 11.9808 | 3750 | 0.0025 | - |
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+ | 12.1406 | 3800 | 0.0074 | - |
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+ | 12.3003 | 3850 | 0.006 | - |
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+ | 12.4601 | 3900 | 0.005 | - |
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+ | 12.6198 | 3950 | 0.0006 | - |
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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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+ | 13.2588 | 4150 | 0.0 | - |
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+ | 13.4185 | 4200 | 0.0004 | - |
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+ | 13.5783 | 4250 | 0.0 | - |
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+ | 13.7380 | 4300 | 0.0 | - |
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+ | 13.8978 | 4350 | 0.0 | - |
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+ | 14.0575 | 4400 | 0.0 | - |
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+ | 14.2173 | 4450 | 0.0 | - |
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+ | 14.3770 | 4500 | 0.0 | - |
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+ | 14.5367 | 4550 | 0.0 | - |
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+ | 14.6965 | 4600 | 0.0 | - |
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+ | 14.8562 | 4650 | 0.0 | - |
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+ | 15.0160 | 4700 | 0.0 | - |
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+ | 15.1757 | 4750 | 0.0 | - |
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+ | 15.3355 | 4800 | 0.0 | - |
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+ | 15.4952 | 4850 | 0.0 | - |
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+ | 15.6550 | 4900 | 0.0 | - |
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+ | 15.8147 | 4950 | 0.0 | - |
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+ | 15.9744 | 5000 | 0.0 | - |
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+ | 16.1342 | 5050 | 0.0 | - |
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+ | 16.6134 | 5200 | 0.0 | - |
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+ | 16.7732 | 5250 | 0.0 | - |
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+ | 17.7316 | 5550 | 0.0 | - |
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+ | 17.8914 | 5600 | 0.0002 | - |
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+ | 18.0511 | 5650 | 0.0 | - |
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+ | 18.2109 | 5700 | 0.0 | - |
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+ | 18.3706 | 5750 | 0.0 | - |
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+ | 18.5304 | 5800 | 0.0 | - |
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+ | 18.6901 | 5850 | 0.0 | - |
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+ | 18.8498 | 5900 | 0.0 | - |
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+ | 19.0096 | 5950 | 0.0 | - |
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+ | 19.1693 | 6000 | 0.0 | - |
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+ | 19.3291 | 6050 | 0.0 | - |
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+ | 19.4888 | 6100 | 0.0 | - |
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+ | 19.6486 | 6150 | 0.0 | - |
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+ | 19.8083 | 6200 | 0.0 | - |
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+ | 19.9681 | 6250 | 0.0 | - |
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+ | 20.1278 | 6300 | 0.0 | - |
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+ | 20.2875 | 6350 | 0.0 | - |
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+ | 20.4473 | 6400 | 0.0 | - |
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+ | 20.6070 | 6450 | 0.0 | - |
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+ | 20.7668 | 6500 | 0.0 | - |
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+
349
+ ### Framework Versions
350
+ - Python: 3.10.12
351
+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
353
+ - 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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+
358
+ ## Citation
359
+
360
+ ### BibTeX
361
+ ```bibtex
362
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
363
+ doi = {10.48550/ARXIV.2209.11055},
364
+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
366
+ 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},
368
+ publisher = {arXiv},
369
+ year = {2022},
370
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
372
+ ```
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+
374
+ <!--
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+ ## Glossary
376
+
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+ *Clearly define terms in order to be accessible across audiences.*
378
+ -->
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+
380
+ <!--
381
+ ## 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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+ -->
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+
386
+ <!--
387
+ ## 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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