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mini1013/
master_cate_sl10

Name: master_cate_sl10
Rating: 2.5 (6 reviews)

setfit

safetensors

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

mini1013/master_domain

finetune

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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	'봉술 나무봉 수련용 창술 막대 훈련 등봉 연습용 장봉 블랙 스포츠/레저 > 무술용품 > 봉/곤/창' '에버라스트 초보연습용 스폰지쌍절곤 스포츠/레저 > 무술용품 > 봉/곤/창' '프로칸 25cm 스폰지 쌍절곤 쌍절봉 안전 연습용 수련용 호신용 가벼운 초급자용 스포츠/레저 > 무술용품 > 봉/곤/창'
1.0	'켄신 중도 스포츠/레저>무술용품>목검/가검' '초장도 스포츠/레저>무술용품>목검/가검' '대조영도검 스포츠/레저>무술용품>목검/가검'
0.0	'무술 창 연극 소품 기타무술용품 스포츠/레저 > 무술용품 > 기타무술용품' '무술 창 연극 소품 기타무술용품 P 1개 스포츠/레저 > 무술용품 > 기타무술용품' '죽선 D-1 대나무 부채 부채술 무술용품 스포츠/레저 > 무술용품 > 기타무술용품'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_sl10")
# Run inference
preds = model("군용 단검 스포츠/레저>무술용품>목검/가검")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	8.9677	18

Label	Training Sample Count
0.0	10
1.0	9
2.0	12

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
0.1429	1	0.4962	-
7.1429	50	0.1601	-
14.2857	100	0.0001	-
21.4286	150	0.0	-
28.5714	200	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

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}
}