mini1013/master_cate_lh24

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: 2 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
1.0	'매직솔트 천목도자기 좌훈기 매직솔트' '냄새제거 해충기피 좌훈 강화약쑥 태우는쑥 2봉 이즈데어' '가정용 원목 좌훈기 족욕기 혈액순환 찜질 좌욕 훈증 70 높이 W포트 찜통 E 아르랩'
0.0	'접이식 가정용 좌욕기 임산부 치질 온욕 폴딩 대야 수동 비데 접이식 가정용좌욕기 그레이 데일리마켓' 'OK 소프트 좌욕대야 좌욕기 임산부 가정용 좌욕 1_핑크 메디칼유' '닥터프리 버블 가정용 좌욕기 쑥 치질 임산부 대야 A.고급 천연 약쑥 30포 주식회사 다니고'

Evaluation

Metrics

Label	Metric
all	0.9881

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_lh24")
# Run inference
preds = model("반신욕 가운 좌훈 사우나 목욕탕 찜질 땀복 좌욕 치마 5. 블루 커버 컬러몰")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	10.8	22

Label	Training Sample Count
0.0	50
1.0	50

Training Hyperparameters

• batch_size: (512, 512)
• num_epochs: (20, 20)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 40
• body_learning_rate: (2e-05, 2e-05)
• head_learning_rate: 2e-05
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0625	1	0.4245	-
3.125	50	0.0003	-
6.25	100	0.0	-
9.375	150	0.0	-
12.5	200	0.0	-
15.625	250	0.0	-
18.75	300	0.0	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.1.0.dev0
• Sentence Transformers: 3.1.1
• Transformers: 4.46.1
• PyTorch: 2.4.0+cu121
• Datasets: 2.20.0
• Tokenizers: 0.20.0

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}
}