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mini1013/
master_cate_lh7

Name: master_cate_lh7
Availability: InStock

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: 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
1.0	'프리스타일 리브레 무채혈 연속혈당측정기(24년1월)얼라이브패치1매 거래명세서 광명헬스케어' 'SD 코드프리 혈당측정기(측정기+채혈기+침10매+파우치)P 스토어알파' '올메디쿠스 글루코닥터 탑 혈당계 AGM-4100+파우치+채혈기+채혈침 10개 엠에스메디칼'
2.0	'에스디 SD 코드프리 측정지
0.0	'비디 울트라파인 인슐린 주사기 1박스 100입 324901 [31G 6mm 0.5ml] BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G 8mm 3/10ml(0.5단위) 1박스(320440) 더메디칼샵' 'BD 비디 울트라파인 인슐린 주사기 시린지 31G 6mm 1ml 324903 100입 주식회사 더에스지엠' '정림 멸균 일회용 주사기 3cc 23g 25mm 100개입 멸균주사기 10cc 18G 38mm(100ea/pck) (주)케이디상사'

Evaluation

Metrics

Label	Metric
all	0.9787

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_lh7")
# Run inference
preds = model("녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	9.62	21

Label	Training Sample Count
0.0	50
1.0	50
2.0	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
0.0417	1	0.4565	-
2.0833	50	0.1836	-
4.1667	100	0.1645	-
6.25	150	0.0004	-
8.3333	200	0.0001	-
10.4167	250	0.0001	-
12.5	300	0.0	-
14.5833	350	0.0	-
16.6667	400	0.0	-
18.75	450	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}
}