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
2.0	'보약게임 이게 왜 오리너구리, 1개 TS 687769 포레스트 출산/육아 > 교구 > 학습보드게임' '모닝글로리 15000 장기 자석타입 장기알 장기판 폴더형 접이형 보드게임 77 체스 출산/육아 > 교구 > 학습보드게임' '고피쉬 한글3 쉬운 받침 글자 출산/육아 > 교구 > 학습보드게임'
0.0	'립프로그 선택 구매 (풀세트 구매시 립프로그 알파벳 카드 27종 ) 2집 (DVD7+CD7+대본6권) 출산/육아 > 교구 > 비디오/DVD' '고교토론,판 출산/육아 > 교구 > 비디오/DVD' '시간의숲 출산/육아 > 교구 > 비디오/DVD'
1.0	'초등 비즈 보석십자수 아크릴 키링 가방고리 만들기 10인 지능발달 손작업 협업 어린이집 상품 선택_선인장 출산/육아 > 교구 > 학습교구 > 미술교구' '디즈니 음악이론 1-12권 유아 어린이 피아노 음악 교재 책 디즈니 음악 이론 6 출산/육아 > 교구 > 학습교구 > 기타교구' '무지개롤 / 펠트교구 검정 출산/육아 > 교구 > 학습교구 > 영어교구'

Evaluation

Metrics

Label	Accuracy
all	1.0

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_bc0")
# Run inference
preds = model("온가족 보드게임 영어 워드온더스트리트 유아교육기관  출산/육아 > 교구 > 학습보드게임")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	14.4143	34

Label	Training Sample Count
0.0	70
1.0	70
2.0	70

Training Hyperparameters

batch_size: (256, 256)
num_epochs: (30, 30)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 50
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0238	1	0.4941	-
1.1905	50	0.465	-
2.3810	100	0.0367	-
3.5714	150	0.0	-
4.7619	200	0.0	-
5.9524	250	0.0	-
7.1429	300	0.0	-
8.3333	350	0.0	-
9.5238	400	0.0	-
10.7143	450	0.0	-
11.9048	500	0.0	-
13.0952	550	0.0	-
14.2857	600	0.0	-
15.4762	650	0.0	-
16.6667	700	0.0	-
17.8571	750	0.0	-
19.0476	800	0.0	-
20.2381	850	0.0	-
21.4286	900	0.0	-
22.6190	950	0.0	-
23.8095	1000	0.0	-
25.0	1050	0.0	-
26.1905	1100	0.0	-
27.3810	1150	0.0	-
28.5714	1200	0.0	-
29.7619	1250	0.0	-

Framework Versions

Python: 3.10.12
SetFit: 1.1.0
Sentence Transformers: 3.3.1
Transformers: 4.44.2
PyTorch: 2.2.0a0+81ea7a4
Datasets: 3.2.0
Tokenizers: 0.19.1

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_bc0