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: 4 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	'배럴 맨 에센셜 스탠다드핏 집업 래쉬가드 B4SMWZR101BLK 스포츠/레저>수영>비치웨어>상의' '여성 래쉬가드 집업 비치 웨어 커플 수영복 스포츠/레저>수영>비치웨어>커플비치웨어' '엘르 엘르스포츠 남성 트렁크 비치 NVY E3SMOMJ01 스포츠/레저>수영>비치웨어>팬츠'
2.0	'아레나 오리발 롱핀 WHT265 A3AC1AF01WHT265 스포츠/레저>수영>수영용품>오리발' '패들보드 서핑 공기주입식 웨이크 보드 스탠드 풀세트 스포츠/레저>수영>수영용품>기타수영용품' '아레나 아레나 킥보드 A3AC1AK01YEL 스포츠/레저>수영>수영용품>기타수영용품'
0.0	'스피도 남성 스탠다드 수영복 사각 다리 스플라이스 로고 피코트 스몰 스포츠/레저>수영>남성수영복>반신수영복' '아레나 와트 아동레저 슈트 A3BB1BI23NVY-MN 스포츠/레저>수영>남성수영복>반신수영복' '남자 수영복 전신 슈트 스포츠/레저>수영>남성수영복>전신수영복'
3.0	'아레나 여성 비키니 2PCS 수영복 A0BL1PS09BLK 스포츠/레저>수영>여성수영복>비키니' '실내수영장 체형커버 수영복 풀빌라 온천 빅 사이즈 스포츠/레저>수영>여성수영복>원피스수영복' '빅 사이즈 투피스 비키니 심플 올오버 프린트 수영복 624538 스포츠/레저>수영>여성수영복>비키니'

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_sl16")
# Run inference
preds = model("레노마수영복 여성 파레오랩스커트 WS20307 스포츠/레저>수영>비치웨어>스커트")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	8.4071	21

Label	Training Sample Count
0.0	70
1.0	70
2.0	70
3.0	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
0.0182	1	0.4884	-
0.9091	50	0.4351	-
1.8182	100	0.1675	-
2.7273	150	0.0769	-
3.6364	200	0.0023	-
4.5455	250	0.0001	-
5.4545	300	0.0	-
6.3636	350	0.0	-
7.2727	400	0.0	-
8.1818	450	0.0	-
9.0909	500	0.0	-
10.0	550	0.0	-
10.9091	600	0.0	-
11.8182	650	0.0	-
12.7273	700	0.0	-
13.6364	750	0.0	-
14.5455	800	0.0	-
15.4545	850	0.0	-
16.3636	900	0.0	-
17.2727	950	0.0	-
18.1818	1000	0.0	-
19.0909	1050	0.0	-
20.0	1100	0.0	-
20.9091	1150	0.0	-
21.8182	1200	0.0	-
22.7273	1250	0.0	-
23.6364	1300	0.0	-
24.5455	1350	0.0	-
25.4545	1400	0.0	-
26.3636	1450	0.0	-
27.2727	1500	0.0	-
28.1818	1550	0.0	-
29.0909	1600	0.0	-
30.0	1650	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_sl16