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: 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	'몬스타기어 7500F 4070 SUPER 32G 500GB 조립PC AMD 7500F 4070SUPER 32G 500GB 몬스타 주식회사' '사무용 주식 인텔 i3 12100F/GT710/SSD 250G/8G 조립컴퓨터 컴퓨터본체 데스크탑 컴퓨터 조립PC 기본사양(추가구성에서 사양변경 가능) (주)아싸컴' '장우컴 가정용 PC (13100F/8G/GT1030/256G) i40207 (주)장우컴퍼니'
0	'T) DELL 옵티플렉스 7010SFF-UB02KR (NVMe 512G 교체 장착) 윈11프로 DSP설치 으뜸' '이그닉 비와이 프로 27Y 4535 OS 미포함 NVMe 512G + 16GB RAM (5년 A／S) 빌리어네어에프' '10만원 쿠폰💖 삼성 DM500TFA-A78A 데스크탑 인텔 13세대 i7 [기본제품] (주)컴퓨존'
1	'레노버 씽크스테이션 P620 라이젠 스레드리퍼 프로 5945WX RAM16GB SSD256GB NVMe HDD1TB NOVGA Win11 Pro (주)디지탈노뜨' '[Dell] Precision 3460 SFF i7-13700 8GB 1TB [추가구성 필요] (주)다인엔시스' 'HP DL20 GEN10 E-2224 / 32G / HDD 1T x2 RAID1 / 서버2019 / AS3년 상품권 주식회사 제로원씨앤씨'

Evaluation

Metrics

Label	Metric
all	0.8841

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_el0")
# Run inference
preds = model("LG전자 24V50N-GR35K  정윤아")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	11.6691	21

Label	Training Sample Count
0	50
1	36
2	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
0.0455	1	0.4961	-
2.2727	50	0.005	-
4.5455	100	0.0001	-
6.8182	150	0.0001	-
9.0909	200	0.0	-
11.3636	250	0.0	-
13.6364	300	0.0	-
15.9091	350	0.0	-
18.1818	400	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}
}

mini1013
/

master_cate_el0