mini1013/master_cate_bc8

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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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	'플랩캡 모자 자외선차단 수영모자 유아 아동 공용 UV모자 C_핑크_M 출산/육아 > 수영복/용품 > 수경/수모/귀마개' '아기물안경 유아물안경 성인용 CA01 화이트 출산/육아 > 수영복/용품 > 수경/수모/귀마개' 'UV 플랩캡 아기 유아 아동 수영모자 버킷햇 해변 워터파크 자외선차단 UV플랩캡_그레이_S(3세이하) 출산/육아 > 수영복/용품 > 수경/수모/귀마개'
2.0	'어린이 수영가방 유아 비치백 여아 유치원 캐치티니핑 C.수영모자_47_로이도이 소프트_화이트/56 (770297) 출산/육아 > 수영복/용품 > 수영가방/비치백' '어린이수영가방 수영장가방 비치백 유아 아동 유치원 09.엘오엘_LOL 레트로 비치 핸드백(핑크) 출산/육아 > 수영복/용품 > 수영가방/비치백' '물빠지는 방수 메쉬 목욕가방 스파백_33.NCCSB11_블랙 출산/육아 > 수영복/용품 > 수영가방/비치백'
0.0	'레노마 아레나 슬라임 아동 4부 남아동수영복 A3BB1BF02 출산/육아 > 수영복/용품 > 남아수영복' '스플래쉬어바웃 사계절 키즈래쉬가드 쇼티 웨트슈트 남아래쉬가드 남아수영복 키즈수영복 터그보츠_XXL(8-10세) 출산/육아 > 수영복/용품 > 남아수영복' '디스커버리익스페디션키즈 키즈 로고 래쉬가드 L MINT 출산/육아 > 수영복/용품 > 남아수영복'
3.0	'아레나 초등여아 실내수영복 초등학생 키즈 주니어 4부 5부 반신 생존수영A3FG1GL22 핑크_70 출산/육아 > 수영복/용품 > 여아수영복' '뿔공룡 유아 래쉬가드(90-120) 204119 피치90 출산/육아 > 수영복/용품 > 여아수영복' '블루독 하트전판레쉬가드세트 24940 621 52 출산/육아 > 수영복/용품 > 여아수영복'

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_bc8")
# Run inference
preds = model("뷰 아동 수경 일반렌즈 일본 V424J LV 출산/육아 > 수영복/용품 > 수경/수모/귀마개")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	13.5607	24

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	0.4886	-
0.9091	50	0.4981	-
1.8182	100	0.3363	-
2.7273	150	0.0279	-
3.6364	200	0.0001	-
4.5455	250	0.0	-
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}
}