mini1013/master_cate_sl14

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: 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
2.0	'매드볼링 72매 볼링공클리너티슈 스포츠/레저>볼링>볼링용품' 'BEL 볼링 기름제거 걸레 천 볼링공 광 마찰 스포츠/레저>볼링>볼링용품' '면 로프 크로켓 위켓 5pcs 소프트 게임 스틱 7 08x5 9 플레이어 세트 가족 게임용 스포츠/레저>볼링>볼링용품'
0.0	'와이드앵글 CO 미니 캐주얼 볼링백 WWU23B09K7 LE1214748392 스포츠/레저>볼링>볼링가방' 'FOTTSFOTTS 볼링백 미니 - BOWLING BAG MINI 219966 스포츠/레저>볼링>볼링가방' '대륙 스파이크 풀셋 가방 스포츠/레저>볼링>볼링가방'
5.0	'볼링 아대 핸드 손목 가드 스포츠/레저>볼링>아대' '선브릿지 메카텍터 MECHATECTER 볼링 아대 왼손 MD-4DX 스포츠/레저>볼링>아대' '1쌍 프로볼링장갑 통기성장갑 스포츠장갑 스포츠/레저>볼링>아대'
4.0	'해머 디젤 왼손 볼링화 남성용 - 9 5 스포츠/레저>볼링>볼링화' 'Dexter 볼링 슈즈 스포츠/레저>볼링>볼링화' 'ACCOREN 볼링화 커버 1피스 - 볼링화용 조절 가능한 볼링화 슬라이더 - 프리미엄 볼링 액세서리 - 일관된 스포츠/레저>볼링>볼링화'
3.0	'어썸 향균쿨론 탄탄스판티 단체 볼링 티셔츠 5장이상 2L AS20200402 스포츠/레저>볼링>볼링의류' 'SAVALINO 남성용 볼링 폴로 셔츠 소재 땀 흡수 빠른 건조 사이즈 5X-Large 스포츠/레저>볼링>볼링의류' '오프화이트 홀리데이 볼링 패턴 반팔셔츠 OMGG004C99FAB001 1000 스포츠/레저>볼링>볼링의류'
1.0	'해머 Hammer Widow Legend Bowling Ball 13lbs 155828 스포츠/레저>볼링>볼링공' '로우 해머 볼링 공 블루실버화이트 12 스포츠/레저>볼링>볼링공' '단체활동 10000 플렛볼 파워 플레시 스포츠/레저>볼링>볼링공'

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_sl14")
# Run inference
preds = model("볼링 파우치 싱글볼용 백 공 휴대용 스포츠/레저>볼링>볼링가방")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	8.8452	20

Label	Training Sample Count
0.0	70
1.0	70
2.0	70
3.0	70
4.0	70
5.0	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
0.0120	1	0.4925	-
0.6024	50	0.4964	-
1.2048	100	0.3374	-
1.8072	150	0.0388	-
2.4096	200	0.0003	-
3.0120	250	0.0001	-
3.6145	300	0.0001	-
4.2169	350	0.0001	-
4.8193	400	0.0	-
5.4217	450	0.0	-
6.0241	500	0.0001	-
6.6265	550	0.0001	-
7.2289	600	0.0	-
7.8313	650	0.0	-
8.4337	700	0.0	-
9.0361	750	0.0	-
9.6386	800	0.0	-
10.2410	850	0.0	-
10.8434	900	0.0	-
11.4458	950	0.0	-
12.0482	1000	0.0	-
12.6506	1050	0.0	-
13.2530	1100	0.0	-
13.8554	1150	0.0	-
14.4578	1200	0.0	-
15.0602	1250	0.0	-
15.6627	1300	0.0	-
16.2651	1350	0.0	-
16.8675	1400	0.0	-
17.4699	1450	0.0	-
18.0723	1500	0.0	-
18.6747	1550	0.0	-
19.2771	1600	0.0	-
19.8795	1650	0.0	-
20.4819	1700	0.0	-
21.0843	1750	0.0	-
21.6867	1800	0.0	-
22.2892	1850	0.0	-
22.8916	1900	0.0	-
23.4940	1950	0.0	-
24.0964	2000	0.0	-
24.6988	2050	0.0	-
25.3012	2100	0.0	-
25.9036	2150	0.0	-
26.5060	2200	0.0	-
27.1084	2250	0.0	-
27.7108	2300	0.0	-
28.3133	2350	0.0	-
28.9157	2400	0.0	-
29.5181	2450	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}
}