setfit-banking77

nickprock/setfit-banking77

This is a SetFit model that can be used for text 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.

Train Hyperparameters

• Simulate the few-shot regime by sampling 25 examples per class
• Sentence Transformer checkpoint: "sentence-transformers/paraphrase-distilroberta-base-v2"
• Number of text pairs to generate for contrastive learning: 10
• Epochs: 1
• Batch size: 32

Metrics on Evaluation set

• accuracy score: 0.8529
• f1 score: 0.8527

Usage

To use this model for inference, first install the SetFit library:

python -m pip install setfit

You can then run inference as follows:

from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("nickprock/setfit-banking77")
# Run inference
preds = model(["I can't pay by my credit card"])

BibTeX entry and citation info

@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}
}