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	'고리 집게 가방 여행용 멜빵 클립 다용도 삼각버클 후크 옐로우몰' '패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 흰색 폭 2.5cm 120cm 맴매2' '패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 파란색 흰색 빨간색 줄무늬 폭2.5 120cm 맴매2'
2.0	'Basic Leather Belt 네이비_100cm 만달문화여행사' '다이에나롤랑 러블리 여자벨트 146276 은장 브라운 FCB0012CM_L 105 네잎클로버마켓' '[갤러리아] 헤지스핸드백HJBE2F406W2브라운 스티치장식 소가죽 여성 벨트(타임월드) 한화갤러리아(주)'
0.0	'(아크테릭스)(공식판매처)(23SS) 컨베이어 벨트 32mm (AENSUX5577) BLACK_SM ' '[갤러리아] 헤지스핸드백 HJBE2F775BK_ 블랙 빅로고 버클 가죽 자동벨트(타임월드) 한화갤러리아(주)' '닥스_핸드백 (선물포장/쇼핑백동봉) 블랙 체크배색 가죽 자동벨트 DBBE3E990BK 롯데백화점2관'

Evaluation

Metrics

Label	Metric
all	0.9650

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_ac3")
# Run inference
preds = model("[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.6133	17

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	0.394	-
2.0833	50	0.0731	-
4.1667	100	0.0	-
6.25	150	0.0	-
8.3333	200	0.0	-
10.4167	250	0.0	-
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

@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_ac3