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mini1013
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master_cate_el2

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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  1. 1_Pooling/config.json +10 -0
  2. README.md +235 -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_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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+ - metric
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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: IPTIME UC 305HDMI C타입 USB 멀티포트 노트북 확장 PD (주)스마트포유
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+ - text: 로지텍 파워플레이 Logitech Powerplay 시스템 충전패드 병행수입 Power Play 주식회사 데나
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+ - text: PBT키캡 푸딩 이중사출 영문 정각 108 풀배열 키보드 화이트 몬스타 주식회사
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+ - text: 펠로우즈 i-spire rocking 미니손목받침대 그레이 93933 그레이 아이룸코리아
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+ - text: AMH 클리어 투웨이 4포트 USB3.0 허브 민트 주식회사보성닷컴
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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: metric
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+ value: 0.9550144449030128
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+ name: Metric
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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:** 9 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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+ | 2 | <ul><li>'몬스타기어 달토끼 PBT 체리 프로파일 키캡 주식회사 노벨뷰사이언스'</li><li>'[COX] 영문 키캡, CX158 158키 이색사출 PBT 키캡, OSA 프로파일 [오셀라리스] (주)컴퓨존'</li><li>'벤큐 조위 CAMADE2 e-Sports 게이밍 마우스 번지대/마우스번지/카마데2 하이스트네트웍스 주식회사'</li></ul> |
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+ | 5 | <ul><li>'지클릭커 클라우드 코튼 팜레스트 키보드 쿠션 손목 받침대 눈설탕 눈설탕 (주)수빈인포텍'</li><li>'ABKO ARC1 TKL 아크릴 팜레스트 키보드 손목 받침대 텐키리스용 아이스 아크릴 조은 정보'</li><li>'펠로우즈 크리스탈젤 미니손목받침대 CRC91477 / 보라 에이티쓰리'</li></ul> |
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+ | 8 | <ul><li>'로지텍 K380 키스킨 주식회사 제이앤디코퍼레이션'</li><li>'로지텍 K260 K270 K275 K295 MK275 MK295 키스킨 키보드커버 덮개 로지텍 K295 키스킨 현민트레이딩 주식회사'</li><li>'로지텍 K270 MK270R MK260R 키보드보호 키스킨 유비스마트'</li></ul> |
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+ | 4 | <ul><li>'지클릭커 모니터 필름 PET 부착식 정보 보안 노트북 화면 보호기 블루라이트 차단 12.5인치 현시스템'</li><li>'앱코 블루라이트 차단 양면 부착형 모니터 정보보안필름 와이드(16:9) IP-24W 주식회사 케이에스샵'</li><li>'펠로우즈 프라이버시 정보보안 필터 14.1인치 와이드 16:10 정보보호 필름 48006 와이티코리아 주식회사'</li></ul> |
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+ | 3 | <ul><li>'앱코 Pastel Desk Long Pad 마우스패드 파스텔 베이지 주식회사 승호'</li><li>'스틸시리즈 Qck Edge XL 게이밍 마우스패드 주식회사 엠앤���스'</li><li>'파스텔 방수 가죽 마우스 장패드 네이비 본조르노온라인 주식회사'</li></ul> |
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+ | 7 | <ul><li>'동성 만능크리너 60매 본품 (주)바오밥컴퍼니'</li><li>'동성크리너 동성 만능크리너 150매 (원통형) 주식회사 해인디지탈'</li><li>'일신 ECC-90 전기접점부활제 250g 리모콘 플스 닌텐도 스위치 조이콘 조이스틱 쏠림 접점세척제 벡스 BW-100 전기접점부활제 225g 모멘트리 (MOMENTREE)'</li></ul> |
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+ | 6 | <ul><li>'전오 케이블타이 450mm 대용량 흰색 J-450 100개 국산 손소프트'</li><li>'베이스어스 마그네틱 케이블클립,선정리,케이블홀더 블랙(ACWDJ-01) 주식회사엠피맨코리아'</li><li>'전오 케이블타이 140MM 국산제품 전선정리 포장끈 작업현장 건설 농장 전자 공장 백색(1000개) 보람 LED'</li></ul> |
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+ | 1 | <ul><li>'ipTIME UH505 (기본구성) USB3.0 5포트 USB허브 5V3A 어댑터 (주)즐찾'</li><li>'EFM네트웍스 아이피타임 UH505 다사다 유한책임회사'</li><li>'벨킨 11in1 USB C타입 멀티 허브 독 100W 충전 HDMI VGA 이더넷 노트북 거치대형 INC004bt 아이폰15 갤럭시 S24 그램 맥북 노트북 호환 실버그레이(INC004btSGY) (주) 디지월드'</li></ul> |
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+ | 0 | <ul><li>'Coms DJ729 데스크탑 PC 이동형 스탠드 컴퓨터 본체 거치대 바퀴 이동식 블랙 루미너스'</li><li>'컴퓨터 본체 받침대 DJ729 주식회사보성닷컴'</li><li>'데스크탑 PC 본체 이동형 스탠드 DJ729 주식회사 지디스엠알오'</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 | Metric |
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+ |:--------|:-------|
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+ | **all** | 0.9550 |
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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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+
88
+ ```bash
89
+ pip install setfit
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+ ```
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+
92
+ 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_el2")
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+ # Run inference
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+ preds = model("AMH 클리어 투웨이 4포트 USB3.0 허브 민트 주식회사보성닷컴")
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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 | 4 | 10.1397 | 25 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 8 |
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+ | 1 | 50 |
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+ | 2 | 50 |
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+ | 3 | 50 |
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+ | 4 | 50 |
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+ | 5 | 50 |
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+ | 6 | 50 |
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+ | 7 | 50 |
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+ | 8 | 50 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (512, 512)
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+ - num_epochs: (20, 20)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 40
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ - 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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+ | 0.0156 | 1 | 0.4963 | - |
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+ | 0.7812 | 50 | 0.1854 | - |
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+ | 1.5625 | 100 | 0.046 | - |
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+ | 2.3438 | 150 | 0.0048 | - |
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+ | 3.125 | 200 | 0.0168 | - |
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+ | 3.9062 | 250 | 0.0002 | - |
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+ | 4.6875 | 300 | 0.0001 | - |
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+ | 5.4688 | 350 | 0.0001 | - |
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+ | 6.25 | 400 | 0.0001 | - |
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+ | 7.0312 | 450 | 0.0001 | - |
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+ | 7.8125 | 500 | 0.0001 | - |
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+ | 8.5938 | 550 | 0.0001 | - |
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+ | 9.375 | 600 | 0.0001 | - |
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+ | 10.1562 | 650 | 0.0001 | - |
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+ | 10.9375 | 700 | 0.0 | - |
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+ | 11.7188 | 750 | 0.0001 | - |
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+ | 12.5 | 800 | 0.0 | - |
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+ | 13.2812 | 850 | 0.0 | - |
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+ | 14.0625 | 900 | 0.0 | - |
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+ | 14.8438 | 950 | 0.0 | - |
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+ | 15.625 | 1000 | 0.0 | - |
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+ | 16.4062 | 1050 | 0.0001 | - |
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+ | 17.1875 | 1100 | 0.0 | - |
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+ | 17.9688 | 1150 | 0.0 | - |
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+ | 18.75 | 1200 | 0.0 | - |
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+ | 19.5312 | 1250 | 0.0 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.1.0.dev0
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+ - Sentence Transformers: 3.1.1
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+ - Transformers: 4.46.1
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+ - PyTorch: 2.4.0+cu121
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+ - Datasets: 2.20.0
201
+ - Tokenizers: 0.20.0
202
+
203
+ ## Citation
204
+
205
+ ### BibTeX
206
+ ```bibtex
207
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
208
+ doi = {10.48550/ARXIV.2209.11055},
209
+ 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},
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+ 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},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
216
+ }
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+ ```
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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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+ -->
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+
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+ <!--
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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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+ "single_word": false
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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ "sep_token": {
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+ "content": "[SEP]",
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+ "normalized": false,
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+ "single_word": false
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+ "unk_token": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[CLS]",
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+ "special": true
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+ "1": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "[SEP]",
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+ "normalized": false,
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+ "special": true
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+ }
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+ },
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+ "bos_token": "[CLS]",
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": false,
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+ "eos_token": "[SEP]",
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
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+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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