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mini1013
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master_cate_top_bt6_3_test_flat

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 +377 -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: 필리밀리 데일리 아이래쉬 대용량 (1~2호) 데일리 아이래쉬2호(3set) (#M)홈>미용소품>얼굴소품>속눈썹 OLIVEYOUNG
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+ > 미용소품 > 얼굴소품 > 전체
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+ - text: 더툴랩 더스타일래쉬 4종리얼/내츄럴/볼륨/맥스 중 택1 003 볼륨 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 >
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+ 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
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+ - text: 아리따움 아이돌래쉬 속눈썹 베이직/프리미엄/대용량 베이직 2호 쁘띠 볼륨 (#M)홈>뷰티 Naverstore > 화장품/미용 > 뷰티소품
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+ > 아이소품 > 속눈썹/속눈썹펌제
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+ - text: 더툴랩 속눈썹 해피림 아이래쉬 내추럴 가닥속눈썹 1pack 11.5N (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore
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+ > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제
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+ - text: '[에뛰드] 마이뷰티툴 속눈썹 1ea 4호 홈>화장소품;홈>TOOL;(#M)홈>배송비 절약템 🛒 Naverstore > 화장품/미용
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+ > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'
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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: 0.9726224783861671
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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:** 5 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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+ | 4 | <ul><li>'6줄리뉴얼 엘스몰 대용량 A형속눈썹 인조속눈썹 연장부분아이돌가닥속눈썹 E형_8-8-9-9-10-10mm 홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제;(#M)홈>속눈썹 상품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'[에뛰드] 마이뷰티툴 속눈썹 1ea 5호 홈>화장소품;홈>TOOL;(#M)홈>배송비 절약템 🛒 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'에뛰드하우스 마이뷰티툴 속눈썹 6종/인조속눈썹 6호 볼륨 업 (#M)11st>뷰티소품>메이크업소품>메이크업소품 11st > 뷰티 > 뷰티소품 > 메이크업소품'</li></ul> |
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+ | 1 | <ul><li>'트위저맨 Studio Collection 브로우 쉐이핑 가위 브러쉬 NEW 정품 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트'</li><li>'트위저맨 Tweezerman 스테인리스 브��우 쉐이핑 가위 및 브러시 521626 (#M)홈>화장품/미용>뷰티소품>헤어소품>미용가위 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 미용가위'</li><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li></ul> |
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+ | 0 | <ul><li>'BL 옵티모 글루 속눈썹 연장 글루 접착제 5g (#M)홈>속눈썹펌&연장🎀>속눈썹연장글루&리무버 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 기타아이소품'</li><li>'필리밀리 속눈썹 접착제 (블랙) 필리밀리 속눈썹 접착제 (블랙) 홈>미용소품>얼굴소품>속눈썹;(#M)홈>미용소품>아이>속눈썹/쌍꺼풀 OLIVEYOUNG > 미용소품 > 아이 > 속눈썹/쌍꺼풀'</li><li>'에뛰드하우스 마이뷰티툴 쌍꺼풀 액속눈썹 접착제 MinSellAmount (#M)화장품/향수>이미용소품>쌍꺼풀 Gmarket > 뷰티 > 화장품/향수 > 이미용소품 > 쌍꺼풀'</li></ul> |
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+ | 2 | <ul><li>'Tweezerman 로즈 골드 클래식 속눈썹 뷰러 1035-RGR104536 (#M)홈>화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 눈썹칼'</li><li>'크리스챤 디올 디올 백스테이지 아이래쉬 컬러 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li><li>'[CHANEL] 르 르쿠르브 실 드 샤넬 (속눈썹 뷰러/ 선물포장가능) (#M)홈>화장품/미용>뷰티소품>아이소품>뷰러 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러'</li></ul> |
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+ | 3 | <ul><li>'e.l.f. 듀얼 펜슬 샤프너 3세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품'</li><li>'샤프너 펜슬 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'샤프너 펜슬 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품'</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** | 0.9726 |
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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_bt6_3_test_flat")
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+ # Run inference
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+ preds = model("필리밀리 데일리 아이래쉬 대용량 (1~2호) 데일리 아이래쉬2호(3set) (#M)홈>미용소품>얼굴소품>속눈썹 OLIVEYOUNG > 미용소품 > 얼굴소품 > 전체")
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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 | 13 | 19.3591 | 47 |
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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 | 9 |
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+ | 2 | 50 |
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+ | 3 | 22 |
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+ | 4 | 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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+ | 0.0035 | 1 | 0.4429 | - |
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+ | 0.1767 | 50 | 0.4197 | - |
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+ | 0.3534 | 100 | 0.3579 | - |
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+ | 0.5300 | 150 | 0.2926 | - |
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+ | 0.7067 | 200 | 0.2247 | - |
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+ | 0.8834 | 250 | 0.1077 | - |
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+ | 1.0601 | 300 | 0.0253 | - |
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+ | 1.2367 | 350 | 0.0025 | - |
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+ | 1.4134 | 400 | 0.0016 | - |
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+ | 1.5901 | 450 | 0.0008 | - |
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+ | 1.7668 | 500 | 0.0005 | - |
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+ | 1.9435 | 550 | 0.0002 | - |
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+ | 2.1201 | 600 | 0.0002 | - |
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+ | 2.2968 | 650 | 0.0002 | - |
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+ | 2.4735 | 700 | 0.0003 | - |
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+ | 2.6502 | 750 | 0.0002 | - |
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+ | 2.8269 | 800 | 0.0007 | - |
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+ | 3.0035 | 850 | 0.0164 | - |
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+ | 3.1802 | 900 | 0.0009 | - |
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+ | 3.3569 | 950 | 0.0002 | - |
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+ | 3.5336 | 1000 | 0.0 | - |
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+ | 3.7102 | 1050 | 0.0 | - |
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+ | 3.8869 | 1100 | 0.0 | - |
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+ | 4.0636 | 1150 | 0.0002 | - |
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+ | 4.2403 | 1200 | 0.0 | - |
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+ | 4.4170 | 1250 | 0.0 | - |
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+ | 4.5936 | 1300 | 0.0 | - |
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+ | 4.7703 | 1350 | 0.0 | - |
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+ | 4.9470 | 1400 | 0.0 | - |
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+ | 5.1237 | 1450 | 0.0 | - |
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+ | 5.3004 | 1500 | 0.0 | - |
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+ | 5.4770 | 1550 | 0.0 | - |
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+ | 5.6537 | 1600 | 0.0 | - |
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+ | 5.8304 | 1650 | 0.0 | - |
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+ | 6.0071 | 1700 | 0.0006 | - |
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+ | 6.1837 | 1750 | 0.0001 | - |
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+ | 6.3604 | 1800 | 0.0 | - |
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+ | 6.5371 | 1850 | 0.0 | - |
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+ | 6.7138 | 1900 | 0.0 | - |
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+ | 6.8905 | 1950 | 0.0 | - |
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+ | 7.0671 | 2000 | 0.0 | - |
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+ | 7.2438 | 2050 | 0.0 | - |
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+ | 7.4205 | 2100 | 0.0 | - |
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+ | 7.5972 | 2150 | 0.0 | - |
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+ | 7.7739 | 2200 | 0.0 | - |
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+ | 7.9505 | 2250 | 0.0 | - |
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+ | 8.1272 | 2300 | 0.0 | - |
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+ | 8.3039 | 2350 | 0.0 | - |
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+ | 8.4806 | 2400 | 0.0023 | - |
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+ | 8.6572 | 2450 | 0.0 | - |
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+ | 8.8339 | 2500 | 0.0 | - |
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+ | 9.0106 | 2550 | 0.0 | - |
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+ | 9.1873 | 2600 | 0.0 | - |
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+ | 9.3640 | 2650 | 0.0 | - |
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+ | 9.5406 | 2700 | 0.0 | - |
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+ | 9.7173 | 2750 | 0.0 | - |
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+ | 9.8940 | 2800 | 0.0 | - |
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+ | 10.0707 | 2850 | 0.0 | - |
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+ | 10.2473 | 2900 | 0.0 | - |
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+ | 10.4240 | 2950 | 0.0 | - |
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+ | 10.6007 | 3000 | 0.0 | - |
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+ | 10.7774 | 3050 | 0.0 | - |
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+ | 10.9541 | 3100 | 0.0 | - |
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+ | 11.1307 | 3150 | 0.0 | - |
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+ | 11.3074 | 3200 | 0.0 | - |
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+ | 11.4841 | 3250 | 0.0 | - |
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+ | 11.6608 | 3300 | 0.0 | - |
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+ | 11.8375 | 3350 | 0.0 | - |
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+ | 12.0141 | 3400 | 0.0 | - |
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+ | 12.1908 | 3450 | 0.0 | - |
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+ | 12.3675 | 3500 | 0.0 | - |
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+ | 12.5442 | 3550 | 0.0 | - |
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+ | 12.7208 | 3600 | 0.0 | - |
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+ | 12.8975 | 3650 | 0.0 | - |
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+ | 13.0742 | 3700 | 0.0 | - |
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+ | 13.2509 | 3750 | 0.0 | - |
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+ | 13.4276 | 3800 | 0.0 | - |
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+ | 13.6042 | 3850 | 0.0 | - |
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+ | 13.7809 | 3900 | 0.0 | - |
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+ | 13.9576 | 3950 | 0.0 | - |
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+ | 14.1343 | 4000 | 0.0 | - |
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+ | 14.3110 | 4050 | 0.0 | - |
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+ | 14.4876 | 4100 | 0.0 | - |
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+ | 14.6643 | 4150 | 0.0 | - |
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+ | 14.8410 | 4200 | 0.0 | - |
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+ | 15.0177 | 4250 | 0.0 | - |
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+ | 15.1943 | 4300 | 0.0 | - |
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+ | 15.3710 | 4350 | 0.0 | - |
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+ | 15.5477 | 4400 | 0.0 | - |
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+
336
+ ### Framework Versions
337
+ - 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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+
345
+ ## Citation
346
+
347
+ ### BibTeX
348
+ ```bibtex
349
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
350
+ doi = {10.48550/ARXIV.2209.11055},
351
+ url = {https://arxiv.org/abs/2209.11055},
352
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
353
+ 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},
355
+ publisher = {arXiv},
356
+ year = {2022},
357
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
359
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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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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+ -->
372
+
373
+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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