Long Covid Classification

🚀 Long COVID Classification

We fine-tuned bert-base-cased using a manually curated dataset to train a Sequence Classification model that can distinguish between long COVID and non-long COVID-related documents.

🚀 Quick Start

We fine-tuned bert-base-cased using a manually curated dataset to train a Sequence Classification model. This model is capable of distinguishing between long COVID and non-long COVID-related documents.

✨ Features

• Utilize a manually curated dataset for fine - tuning.
• Train a Sequence Classification model to classify long COVID and non - long COVID documents.

📦 Installation

No specific installation steps provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("llangnickel/long-covid-classification", use_auth_token=True)
label_dict = {0: "nonLongCOVID", 1: "longCOVID"}
model = AutoModelForSequenceClassification.from_pretrained("llangnickel/long-covid-classification", use_auth_token=True, num_labels=len(label_dict))

📚 Documentation

Used Hyperparameters

Metrics

📄 License

This project is licensed under the MIT license.

📚 Citation

@article{10.1093/database/baac048,
    author = {Langnickel, Lisa and Darms, Johannes and Heldt, Katharina and Ducks, Denise and Fluck, Juliane},
    title = "{Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID}",
    journal = {Database},
    volume = {2022},
    year = {2022},
    month = {07},
    issn = {1758-0463},
    doi = {10.1093/database/baac048},
    url = {https://doi.org/10.1093/database/baac048},
    note = {baac048},
    eprint = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baac048/44371817/baac048.pdf},
}