README.md

metadata

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: 군용 단검 스포츠/레저>무술용품>목검/가검
  - text: 에버라스트 초보연습용 스폰지쌍절곤 스포츠/레저 > 무술용품 > 봉/곤/창
  - text: 봉술 나무봉 수련용 창술 막대 훈련 등봉 연습용 장봉 블랙 스포츠/레저 > 무술용품 > 봉/곤/창
  - text: 홈목검C-27 스포츠/레저>무술용품>목검/가검
  - text: 태극권 수련 훈련 무술 장비 나무 봉 창 닌자풍실막대 이소룡 단봉 흑단목 필리핀 스포츠/레저 > 무술용품 > 봉/곤/창
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain

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 Type: SetFit
• Sentence Transformer body: mini1013/master_domain
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 3 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
2.0	'봉술 나무봉 수련용 창술 막대 훈련 등봉 연습용 장봉 블랙 스포츠/레저 > 무술용품 > 봉/곤/창' '에버라스트 초보연습용 스폰지쌍절곤 스포츠/레저 > 무술용품 > 봉/곤/창' '프로칸 25cm 스폰지 쌍절곤 쌍절봉 안전 연습용 수련용 호신용 가벼운 초급자용 스포츠/레저 > 무술용품 > 봉/곤/창'
1.0	'켄신 중도 스포츠/레저>무술용품>목검/가검' '초장도 스포츠/레저>무술용품>목검/가검' '대조영도검 스포츠/레저>무술용품>목검/가검'
0.0	'무술 창 연극 소품 기타무술용품 스포츠/레저 > 무술용품 > 기타무술용품' '무술 창 연극 소품 기타무술용품 P 1개 스포츠/레저 > 무술용품 > 기타무술용품' '죽선 D-1 대나무 부채 부채술 무술용품 스포츠/레저 > 무술용품 > 기타무술용품'

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_sl10")
# Run inference
preds = model("군용 단검 스포츠/레저>무술용품>목검/가검")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	8.9677	18

Label	Training Sample Count
0.0	10
1.0	9
2.0	12

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
0.1429	1	0.4962	-
7.1429	50	0.1601	-
14.2857	100	0.0001	-
21.4286	150	0.0	-
28.5714	200	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}
}