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master_cate_sl14

Text Classification
setfit
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sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
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master_cate_sl14 / README.md
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: 피라미드 Pyramid Path 디럭스 더블 롤러와 오버사이즈 액세서리 포켓 볼링 백 로열 스포츠/레저>볼링>볼링가방
  - text: 볼링 파우치 싱글볼용 백 공 휴대용 스포츠/레저>볼링>볼링가방
  - text: 900글로벌 T N T 볼링공 12-16파운드 스포츠/레저>볼링>볼링공
  - text: KR 스트라이크포스 스타 청록 오른손 여성 볼링화 스포츠/레저>볼링>볼링화
  - text: 해머 공인구 햄머 바이브 볼링공 15파운드 소프트볼 시소백 스포츠/레저>볼링>볼링공
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
model-index:
  - name: SetFit with mini1013/master_domain
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
2.0
  • '매드볼링 72매 볼링공클리너티슈 스포츠/레저>볼링>볼링용품'
  • 'BEL 볼링 기름제거 걸레 천 볼링공 광 마찰 스포츠/레저>볼링>볼링용품'
  • '면 로프 크로켓 위켓 5pcs 소프트 게임 스틱 7 08x5 9 플레이어 세트 가족 게임용 스포츠/레저>볼링>볼링용품'
0.0
  • '와이드앵글 CO 미니 캐주얼 볼링백 WWU23B09K7 LE1214748392 스포츠/레저>볼링>볼링가방'
  • 'FOTTSFOTTS 볼링백 미니 - BOWLING BAG MINI 219966 스포츠/레저>볼링>볼링가방'
  • '대륙 스파이크 풀셋 가방 스포츠/레저>볼링>볼링가방'
5.0
  • '볼링 아대 핸드 손목 가드 스포츠/레저>볼링>아대'
  • '선브릿지 메카텍터 MECHATECTER 볼링 아대 왼손 MD-4DX 스포츠/레저>볼링>아대'
  • '1쌍 프로볼링장갑 통기성장갑 스포츠장갑 스포츠/레저>볼링>아대'
4.0
  • '해머 디젤 왼손 볼링화 남성용 - 9 5 스포츠/레저>볼링>볼링화'
  • 'Dexter 볼링 슈즈 스포츠/레저>볼링>볼링화'
  • 'ACCOREN 볼링화 커버 1피스 - 볼링화용 조절 가능한 볼링화 슬라이더 - 프리미엄 볼링 액세서리 - 일관된 스포츠/레저>볼링>볼링화'
3.0
  • '어썸 향균쿨론 탄탄스판티 단체 볼링 티셔츠 5장이상 2L AS20200402 스포츠/레저>볼링>볼링의류'
  • 'SAVALINO 남성용 볼링 폴로 셔츠 소재 땀 흡수 빠른 건조 사이즈 5X-Large 스포츠/레저>볼링>볼링의류'
  • '오프화이트 홀리데이 볼링 패턴 반팔셔츠 OMGG004C99FAB001 1000 스포츠/레저>볼링>볼링의류'
1.0
  • '해머 Hammer Widow Legend Bowling Ball 13lbs 155828 스포츠/레저>볼링>볼링공'
  • '로우 해머 볼링 공 블루실버화이트 12 스포츠/레저>볼링>볼링공'
  • '단체활동 10000 플렛볼 파워 플레시 스포츠/레저>볼링>볼링공'

Evaluation

Metrics

Label Accuracy
all 1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_sl14")
# Run inference
preds = model("볼링 파우치 싱글볼용 백 공 휴대용 스포츠/레저>볼링>볼링가방")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 8.8452 20
Label Training Sample Count
0.0 70
1.0 70
2.0 70
3.0 70
4.0 70
5.0 70

Training Hyperparameters

  • batch_size: (256, 256)
  • num_epochs: (30, 30)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 50
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0120 1 0.4925 -
0.6024 50 0.4964 -
1.2048 100 0.3374 -
1.8072 150 0.0388 -
2.4096 200 0.0003 -
3.0120 250 0.0001 -
3.6145 300 0.0001 -
4.2169 350 0.0001 -
4.8193 400 0.0 -
5.4217 450 0.0 -
6.0241 500 0.0001 -
6.6265 550 0.0001 -
7.2289 600 0.0 -
7.8313 650 0.0 -
8.4337 700 0.0 -
9.0361 750 0.0 -
9.6386 800 0.0 -
10.2410 850 0.0 -
10.8434 900 0.0 -
11.4458 950 0.0 -
12.0482 1000 0.0 -
12.6506 1050 0.0 -
13.2530 1100 0.0 -
13.8554 1150 0.0 -
14.4578 1200 0.0 -
15.0602 1250 0.0 -
15.6627 1300 0.0 -
16.2651 1350 0.0 -
16.8675 1400 0.0 -
17.4699 1450 0.0 -
18.0723 1500 0.0 -
18.6747 1550 0.0 -
19.2771 1600 0.0 -
19.8795 1650 0.0 -
20.4819 1700 0.0 -
21.0843 1750 0.0 -
21.6867 1800 0.0 -
22.2892 1850 0.0 -
22.8916 1900 0.0 -
23.4940 1950 0.0 -
24.0964 2000 0.0 -
24.6988 2050 0.0 -
25.3012 2100 0.0 -
25.9036 2150 0.0 -
26.5060 2200 0.0 -
27.1084 2250 0.0 -
27.7108 2300 0.0 -
28.3133 2350 0.0 -
28.9157 2400 0.0 -
29.5181 2450 0.0 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.3.1
  • Transformers: 4.44.2
  • PyTorch: 2.2.0a0+81ea7a4
  • Datasets: 3.2.0
  • Tokenizers: 0.19.1

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}