spaly99/my-setfit-model-dataset-PG-OCR-3

SetFit

This is a SetFit model that can be used for Text Classification. 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
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 2 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
True	'-. Pepsi-Colacold beats any cola cold! ' "Use “Jemes! et : L lemen peeple wen't Lemon. “i720 ait? " 'Ifit happens once, it could happen again. soptacaceee tates
False	'ps-script' 'Make your bidder browser agnostic to access high-performing cookie alternative supply' 'International Students & Scholars'

Evaluation

Metrics

Label	Accuracy	Precision	Recall	F1
all	0.8732	0.8566	0.8871	0.8716

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("setfit_model_id")
# Run inference
preds = model("Books
")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	8.4845	1060

Label	Training Sample Count
False	7940
True	8060

Framework Versions

• Python: 3.11.0
• SetFit: 1.0.3
• Sentence Transformers: 2.3.0
• Transformers: 4.37.2
• PyTorch: 2.2.1+cu121
• Datasets: 2.16.1
• Tokenizers: 0.15.1

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}
}