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mini1013/
master_cate_fd7

Name: master_cate_fd7
Rating: 2.5 (7 reviews)

setfit

safetensors

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

mini1013/master_domain

finetune

model-index

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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:

Fine-tuning a Sentence Transformer with contrastive learning.
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	'동원 면발의신 얼큰칼국수268g x 4 코스트코 650449 상품 상세페이지 참조_268g x 4 탑럭셔리3' '[청정원] 두부로만든 콩담백면 택1(옵션선택) 1.비빔 380g(2인) 아센드라도' '라리 펜네 500g (유) 싱싱채소 그린팜'
0.0	'팔도 비빔면딸기 135g (5개입) x 1팩 주식회사 디씽컴퍼니' '오뚜기 열 라면 120g 5개 서신빠마켓' '농심 오징어짬뽕컵 67g x 6개 으쓱몰'

Evaluation

Metrics

Label	Metric
all	0.9464

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_fd7")
# Run inference
preds = model("앙카라 스파게티면 5kg  지윤 주식회사")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	8.94	20

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	0.394	-
3.125	50	0.0229	-
6.25	100	0.0002	-
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

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}
}