AndreasBlombach/setfit_schwurpert_train_desc

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.

The base model is sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It has been finetuned on a small dataset of manually annotated German Telegram posts containing different conspiracy narratives and misinformation. See our GitHub repository for more information.

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("AndreasBlombach/setfit_schwurpert_train_desc")
# Run inference
preds = model(["Die Corona-Zahlen sind erstunken und erlogen.", "Ach wenn alles an Kriminellen eingesammelt ist brauchen wir auch\"Corona\" nicht mehr...aber😎  Vertraut dem Plan und bleibt ohne Angst..."])

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}
}