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mini1013/
master_cate_ac8

Name: master_cate_ac8
Rating: 2.5 (6 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	'자전거 등산 골프 겨울 발 다리토시 레그워머 브라운 디플코리아 (Digital Plus Korea)' '국산 면 탁텔 겨울 방한 팔 다리 수면 토시 발 임산부 산후용품 수족냉증 겨울 방한 보온 기본 수면토시 그레이 세자매 양말' '세븐다스 여자 레그워머 수면 여성 발토시 겨울 보온 SD001 그레이_FREE 아이보리'
0.0	'[매장발송] 마리떼 11/6 배송 3PACK EMBROIDERY SOCKS multi OS 와이에스마켓' '에브리데이 플러스 쿠션 트레이닝 크루 삭스(3켤레) SX6888-100 024 ' '[롯데백화점]언더아머(백) 유니섹스 UA 코어 쿼터 양말 - 3켤레 1358344-100 1.LG 롯데백화점_'

Evaluation

Metrics

Label	Metric
all	0.7735

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_ac8")
# Run inference
preds = model("도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	10.82	24

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	0.4226	-
3.125	50	0.0022	-
6.25	100	0.0001	-
9.375	150	0.0001	-
12.5	200	0.0001	-
15.625	250	0.0001	-
18.75	300	0.0001	-

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}
}