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	'셀렉스 매일 마시는 프로틴 12l 160ml × 48개 출산/육아 > 분유 > 특수분유' '일동후디스 초유밀플러스2단계 1캔(1gx90포)) 출산/육아 > 분유 > 특수분유' 'gvp 스마트폰 카드포켓 스마트링블랙 출산/육아 > 분유 > 특수분유'
0.0	'매일유업 앱솔루트 명작 2FL 액상 2단계 240ml 24개 x2개 출산/육아 > 분유 > 국내분유' '매일유업 앱솔루트 센서티브 1단계 900g x 1개 [라면] 봉지라면_얼큰한 너구리 120g 20개 출산/육아 > 분유 > 국내분유' '매일유업 앱솔루트 센서티브 1단계 900g x 1개 [음료] 우유두유_삼육검은콩앤칼슘파우치190ml40팩 출산/육아 > 분유 > 국내분유'
1.0	'힙 압타밀 HA 뢰벤짠 밀라산 홀레 퇴퍼 베바 세레락 프레 2단계 콤비오틱 무전분 산양 [퇴퍼] Töpfer_퇴퍼 락타나 600g (최대8통)_[1통] xPRE Topfer 출산/육아 > 분유 > 수입분유' '뉴트리시아 압타밀 프로누트라 어드밴스 2단계 800g [음료] 탄산음료_데미소다피치250ml30캔 출산/육아 > 분유 > 수입분유' '퇴퍼 홀레 뢰벤짠 힙 노발락 압타밀 무전분 AR 킨더밀쉬 압타밀 오가닉(New)_오가닉 2 800g 1통_◆dm4056631003169_1◆ 출산/육아 > 분유 > 수입분유'

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_bc6")
# Run inference
preds = model("남양유업 아이엠마더 액상 3단계 240ml x96개  출산/육아 > 분유 > 국내분유")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	14.9429	30

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	0.4943	-
1.1905	50	0.4806	-
2.3810	100	0.1671	-
3.5714	150	0.0003	-
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
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master_cate_bc6