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mini1013
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master_item_top_bt11

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 +392 -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: 듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수
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+ - text: 오랑쥬 상긴느 200ml (증정) 울랑 앙피니 30ml_마젠타 LotteOn > 뷰티 > 베이스메이크업 > 향수/디퓨저 > 공용향수
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+ LotteOn > 뷰티 > 명품화장품 > 향수/디퓨저 > 공용향수
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+ - text: 디올 블루밍 부케 롤러 펄 오 드 뚜왈렛 20ml LotteOn > 뷰티 > 향수 > 여성향수 LotteOn > 뷰티 > 향수 >
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+ 여성향수
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+ - text: 포멜로 파라디 30ml +1.7ml 1종 마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded
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+ > 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트
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+ - text: 베르가모트 솔레이 200ml (증정) 울랑 앙피니 30ml_블랙 LOREAL > DepartmentLotteOn > 아틀리에 코롱 >
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+ Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱
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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: 0.5334819796768769
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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>'마타바 석고방향제 만들기 diy 재료모음 05_석고전용색소_01_석고전용색소50ml_노랑 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li><li>'A 시그니처 디퓨저 1+1 프로모션 네롤리바질_피오니 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제'</li><li>'마타바 석고방향제 만들기 diy 재료모음 01_스위스G향료100ml_멋스럽고세련된향기_69_인투유 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li></ul> |
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+ | 0 | <ul><li>'아틀리에 코롱 - 자스민 안젤리크 코롱 압솔뤼 스프레이 100ml/3.3oz LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀���에 코롱'</li><li>'베티베르 파탈 200ml (증정) 아이리스 리벨 30ml LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 30ml +1.7ml 1종 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트'</li></ul> |
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+ | 2 | <ul><li>'베르가모트 솔레이 200ml (증정) 러브 오스만투스 30ml_오랑쥬 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 100ml 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디'</li><li>'베르가모트 솔레이 200ml (증정) 클레망틴 캘리포니아 30ml_마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li></ul> |
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+ | 1 | <ul><li>'불가리 뿌르 옴므 익스트림 EDT 50ml LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수'</li><li>'조르지오 아르마니 아쿠아 디 지오 옴므 세트 EDT 100ml + 트레블 15ml (#M)위메프 > 뷰티 > 명품화장품 > 메이크업 > 립메이크업 위메프 > 뷰티 > 명품화장품 > 스킨케어'</li><li>'불가리 뿌르옴므 익스트림 100ml 50ml 30ml 백화점정품 50ml 백화점정품 홈>화장품/미용>향수>남성향수;(#M)홈>남자향수>불가리 Naverstore > 화장품/미용 > 향수 > 남성향수'</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** | 0.5335 |
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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_item_top_bt11")
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+ # Run inference
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+ preds = model("듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수")
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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 | 26.41 | 45 |
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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 | 0.395 | - |
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+ | 0.1597 | 50 | 0.3286 | - |
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+ | 0.3195 | 100 | 0.2663 | - |
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+ | 0.4792 | 150 | 0.2215 | - |
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+ | 0.6390 | 200 | 0.1928 | - |
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+ | 0.7987 | 250 | 0.081 | - |
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+ | 0.9585 | 300 | 0.0147 | - |
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+ | 1.1182 | 350 | 0.0027 | - |
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+ | 1.2780 | 400 | 0.0008 | - |
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+ | 1.4377 | 450 | 0.0004 | - |
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+ | 1.5974 | 500 | 0.0006 | - |
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+ | 1.7572 | 550 | 0.0003 | - |
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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 | 0.0001 | - |
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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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+ | 3.8339 | 1200 | 0.0 | - |
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+
351
+ ### Framework Versions
352
+ - Python: 3.10.12
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+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
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+ - 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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+
360
+ ## Citation
361
+
362
+ ### BibTeX
363
+ ```bibtex
364
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
365
+ doi = {10.48550/ARXIV.2209.11055},
366
+ url = {https://arxiv.org/abs/2209.11055},
367
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
368
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
369
+ title = {Efficient Few-Shot Learning Without Prompts},
370
+ publisher = {arXiv},
371
+ year = {2022},
372
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
374
+ ```
375
+
376
+ <!--
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+ ## Glossary
378
+
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+ *Clearly define terms in order to be accessible across audiences.*
380
+ -->
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+
382
+ <!--
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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.*
386
+ -->
387
+
388
+ <!--
389
+ ## 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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