readme: ---
  license: mit
  ---
  
  ## long-covid-classification
  We fine-tuned bert-base-cased using a [manually curated dataset](https://huggingface.co/llangnickel/long-covid-classification-data) to train a Sequence Classification model able to distinguish between long COVID and non-long COVID-related documents. 
  
  ## Used hyper parameters
  |Parameter|Value|
  |---|---|  
  |Learning rate|3e-5|
  |Batch size|16|
  |Number of epochs|4|
  |Sequence Length|512|
  
  ## Metrics
  |Precision [%]|Recall [%]|F1-score [%]|
  |---|---|---|  
  |91.18|91.18|91.18|
  
  ## How to load the model
  ```
  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))
  ```
  
  ## 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},  
  }