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: 5 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
4.0	'토게 속성 인형 이누마키 솜인형 솜뭉치 가구/인테리어>솜류>쿠션솜' '모던하우스 호텔 다운필 쿠션솜 50x50 FP4119002 가구/인테리어>솜류>쿠션솜' '텐바이텐 푹신한 국산 쿠션솜 지퍼형 빵빵한 구름솜 50x50 가구/인테리어>솜류>쿠션솜'
2.0	'목화 솜 요 솜이불 겨울 패드 토퍼 이불 바닥 목화솜 가구/인테리어>솜류>요솜/매트솜>목화요솜' '이브자리 뉴 레이언 요솜 S D Q K 가구/인테리어>솜류>요솜/매트솜>견면요솜' '생일 축하 케이크 토퍼 글리터 발레 걸 댄스 발레리나 여아용 파티 장식 댄서 토퍼 골든 132066 가구/인테리어>솜류>요솜/매트솜>견면요솜'
3.0	'폭스베딩 사계절용 모달 헝가리 구스다운 이불 솜털93프로 - 킹600g 가구/인테리어>솜류>이불솜>거위털/오리털이불솜' '슈프렐 95도 사계절 이불솜 가구/인테리어>솜류>이불솜>일반이불솜' '북유럽풍 램스울 양모 겨울이불 순면 이불세트 침구 극세사 두꺼운 가구/인테리어>솜류>이불솜>양모이불솜'
0.0	'베이직 방석솜 가구/인테리어>솜류>방석솜' '코지톡 사용감의 원형 솜방석 4개 가구/인테리어>솜류>방석솜' '포근한 하라홈 국내산 구름 새솜 방석솜 50x50 가구/인테리어>솜류>방석솜'
1.0	'힐튼 호텔 퀼팅베개 계절베개 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜' '바운티풀 호텔베개 폴란드 구스다운 90 수피마면 삼중구조 구스베개 600g 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜' '폭스베딩 프라우덴 헝가리산 구스 베개솜 솜털90 60수 베개커버선물 EH2TXX00106 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'

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_fi4")
# Run inference
preds = model("2장 지퍼형 항균베개솜 4060 애프터식스 가구/인테리어>솜류>베개솜/속통>일반베개솜")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	8.6171	19

Label	Training Sample Count
0.0	70
1.0	70
2.0	70
3.0	70
4.0	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
0.0145	1	0.4828	-
0.7246	50	0.4997	-
1.4493	100	0.2078	-
2.1739	150	0.0067	-
2.8986	200	0.0001	-
3.6232	250	0.0	-
4.3478	300	0.0	-
5.0725	350	0.0	-
5.7971	400	0.0	-
6.5217	450	0.0	-
7.2464	500	0.0	-
7.9710	550	0.0	-
8.6957	600	0.0	-
9.4203	650	0.0	-
10.1449	700	0.0	-
10.8696	750	0.0	-
11.5942	800	0.0	-
12.3188	850	0.0	-
13.0435	900	0.0	-
13.7681	950	0.0	-
14.4928	1000	0.0	-
15.2174	1050	0.0	-
15.9420	1100	0.0	-
16.6667	1150	0.0	-
17.3913	1200	0.0	-
18.1159	1250	0.0	-
18.8406	1300	0.0	-
19.5652	1350	0.0	-
20.2899	1400	0.0	-
21.0145	1450	0.0	-
21.7391	1500	0.0	-
22.4638	1550	0.0	-
23.1884	1600	0.0	-
23.9130	1650	0.0	-
24.6377	1700	0.0	-
25.3623	1750	0.0	-
26.0870	1800	0.0	-
26.8116	1850	0.0	-
27.5362	1900	0.0	-
28.2609	1950	0.0	-
28.9855	2000	0.0	-
29.7101	2050	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_fi4