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: 6 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
5.0	'[아이스박스무료] 선인 DB 휘핑크림 1L 무가당 혼합 생크림 재이F&B' '이탈리아 홉라 식물성 생크림 500ml 6개 무가당 홉라 홈라 크림 알이즈웰' '카파 포모나 휘핑 스프레이 500g 와플 크림 딸기향/휘핑 스프레이 500g 디씨즈(This is)'
1.0	'오뚜기 버터후레시 48개(아이스박스)/일회용버터 오뚜기 딸기잼 디스펜팩 40개+메이플시럽 40 박길용' '버터린 롯데 450g 마늘향오일 갈릭버터오일 (주)인벨' '[본사직송] 라꽁비에뜨 가염 무염 꽃소금 버터 450g (15g x 30개) 11/15(수)배송예정_라꽁비에뜨-가염 450g (30개입) 인에이블 코리아(주)'
3.0	'인도네시아 인도 밀크 스위트드컨덴센 팩 545g 연유 동원무역(이마트24 감천네거리점)' '매일연유 5kg 대용량 x 2개 연유 카페스토리(CAFE STORY)' '누티 크리머 스위텐드 연유 시럽 385g x 8개 클루'
0.0	'오뚜기 파운드 마아가린(벌크) 9kg 피치피치몰' '오뚜기 쿠키 옥수수마가린 200Gx2 1세트 제과 제빵 토스트 방글방글마켓' 'Whirl Admiration Pro Fry 액체 쇼트닝 튀김용 3.6kg 8파운드 포커스라이프'
2.0	'밀락골드 1L 제품수량선택 에스제이푸드(SJ FOOD)' '[구매전 긴급공지 필독]1217. 뉴골드라벨 - 한박스(1030g x 12개) 베이킹도전' '밀락골드 1L 아이스박스필수구매 에스제이푸드(SJ FOOD)'
4.0	'상하 샐러드용 슈레드치즈 210g X 1개 종이박스포장 오하' '끼리 크림치즈 스프레드 플레인 x 4개 베이글 발라 먹는 치즈 토스트 끼리 크림치즈 스프레드 플레인 4개 더팜' '데르뜨 롤케이크 선물세트 소잘우유 초코크림 380g 1개 우유크림 360g 냉동 오씨홀딩스'

Evaluation

Metrics

Label	Metric
all	0.9221

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_fd13")
# Run inference
preds = model("홉라 무가당 휘핑크림 1L 2개세트  마켓이")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.5067	18

Label	Training Sample Count
0.0	50
1.0	50
2.0	50
3.0	50
4.0	50
5.0	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
0.0213	1	0.4249	-
1.0638	50	0.2783	-
2.1277	100	0.0747	-
3.1915	150	0.0734	-
4.2553	200	0.0368	-
5.3191	250	0.0373	-
6.3830	300	0.0003	-
7.4468	350	0.0001	-
8.5106	400	0.0001	-
9.5745	450	0.0001	-
10.6383	500	0.0	-
11.7021	550	0.0	-
12.7660	600	0.0	-
13.8298	650	0.0	-
14.8936	700	0.0	-
15.9574	750	0.0	-
17.0213	800	0.0	-
18.0851	850	0.0	-
19.1489	900	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_fd13