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	'팔도 뽀로로 홍삼쏙쏙 오렌지 100ml 20포 출산/육아 > 아기간식 > 유아음료' '팔도 뽀로로 음료 어린이 키즈 주스 식혜 홍삼쏙쏙 워터젤리 모음 2.페트음료_사과x12개+블루베리12개 출산/육아 > 아기간식 > 유아음료' '[1+1] 학교로 간 어린이주스 아기음료 유아주스 11종 사과즙 배도라지즙 [일반캡] 샤인머스캣 20팩_★안전캡★ 배 20팩_학교로간 10팩(맛&캡타입 랜덤) 출산/육아 > 아기간식 > 유아음료'
0.0	'[산골이유식] 산골간식 쌀참 떡뻥 과일참 알밤 꿀밤 배도라지즙 퓨레 푸딩 요거트 비타민젤리 어린이김 쌈장 사과퓨레1팩 출산/육아 > 아기간식 > 유아과자' '내아이애 아기과자 유기농 떡뻥 백미 08_유기농 떡뻥 양파 출산/육아 > 아기간식 > 유아과자' '숲바른 유기농 맑음과자 국내산 아기 과자 유아 간식 떡뻥 스틱 [스틱]단호박 출산/육아 > 아기간식 > 유아과자'
1.0	'파스퇴르 위드맘 산양 제왕 100일 유아이유식분유 750g × 3개 출산/육아 > 아기간식 > 유아유제품' '앱솔루트 킨더밀쉬 200ml 출산/육아 > 아기간식 > 유아유제품' '매일유업 상하치즈 유기농 어린이치즈 3단계 60매 아기 간식 출산/육아 > 아기간식 > 유아유제품'

Evaluation

Metrics

Label	Accuracy
all	0.9978

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_bc12")
# Run inference
preds = model("팔도 뽀로로 밀크맛 235ml 1개입 대페트_칠성 제로 사이다 1.5L 12개입 출산/육아 > 아기간식 > 유아음료")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	15.3381	37

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	0.4944	-
1.1905	50	0.4153	-
2.3810	100	0.1469	-
3.5714	150	0.0014	-
4.7619	200	0.0001	-
5.9524	250	0.0001	-
7.1429	300	0.0001	-
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_bc12