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mini1013/
master_cate_lh23

Name: master_cate_lh23
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	'태국침향 티베트 인센스 디퓨저 천연 향초 향로 24개 트러스트(trust)쇼핑몰' '엘캔들x보리심양초 밀대 원백 돈타래 쌍대 1박스 돈타래 1박스 40개입 엘캔들' '수인당천무 소원부적 스티커 13종 관재구설부 도깨비몰'
2.0	'가톨릭 성화 성인 원형 성수병 30ml 주문제작 메리블라썸' '티베트 야크 본 비즈 108 말라 묵주 기도문 목걸이 10mm white 뮤니샵' '과달루페 성모상 팔찌 목걸이 마리아 가톨릭 실버 스털링 엠에스(MS)쇼핑'
0.0	'주문제작- 어린이 전도지 (명함9x5초청장)-500장 1번_1000 자라나는 씨' '입교증서 우단증서 A5 KJ 상장케이스 상장용지 교회용품 부광' '주문제작- 어린이 전도지 (명함9x5초청장)-500장 3번-하늘색_500 자라나는 씨'

Evaluation

Metrics

Label	Metric
all	0.8397

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_lh23")
# Run inference
preds = model("손가락염주 벽조목 미니염주 건강 불교용품 경면주사 V 이커머스히어로")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.3867	22

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	0.4271	-
2.0833	50	0.029	-
4.1667	100	0.0007	-
6.25	150	0.0002	-
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}
}