readme: ---
  language: en
  thumbnail: https://coronacentral.ai/logo-with-name.png?1
  tags:
  - coronavirus
  - covid
  - bionlp
  datasets:
  - cord19
  - pubmed
  license: mit
  widget:
  - text: "Pre-existing T-cell immunity to SARS-CoV-2 in unexposed healthy controls in Ecuador, as detected with a COVID-19 Interferon-Gamma Release Assay."
  - text: "Lifestyle and mental health disruptions during COVID-19."
  - text: "More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis"
  ---
  # CoronaCentral BERT Model for Topic / Article Type Classification
  
  This is the topic / article type multi-label classification for the [CoronaCentral website](https://coronacentral.ai). This forms part of the pipeline for downloading and processing coronavirus literature described in the [corona-ml repo](https://github.com/jakelever/corona-ml) with available [step-by-step descriptions](https://github.com/jakelever/corona-ml/blob/master/stepByStep.md). The method is described in the [preprint](https://doi.org/10.1101/2020.12.21.423860) and detailed performance results can be found in the [machine learning details](https://github.com/jakelever/corona-ml/blob/master/machineLearningDetails.md) document.
  
  This model was derived by fine-tuning the [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) model on this coronavirus sequence (document) classification task.
  
  ## Usage
  
  Below are two Google Colab notebooks with example usage of this sequence classification model using HuggingFace transformers and KTrain.
  
  - [HuggingFace example on Google Colab](https://colab.research.google.com/drive/1cBNgKd4o6FNWwjKXXQQsC_SaX1kOXDa4?usp=sharing)
  - [KTrain example on Google Colab](https://colab.research.google.com/drive/1h7oJa2NDjnBEoox0D5vwXrxiCHj3B1kU?usp=sharing)
  
  ## Training Data
  
  The model is trained on ~3200 manually-curated articles sampled at various stages during the coronavirus pandemic. The code for training is available in the [category\_prediction](https://github.com/jakelever/corona-ml/tree/master/category_prediction) directory of the main Github Repo. The data is available in the [annotated_documents.json.gz](https://github.com/jakelever/corona-ml/blob/master/category_prediction/annotated_documents.json.gz) file.
  
  ## Inputs and Outputs
  
  The model takes in a tokenized title and abstract (combined into a single string and separated by a new line). The outputs are topics and article types, broadly called categories in the pipeline code. The types are listed below. Some others are managed by hand-coded rules described in the [step-by-step descriptions](https://github.com/jakelever/corona-ml/blob/master/stepByStep.md).
  
  ### List of Article Types 
  
  - Comment/Editorial
  - Meta-analysis
  - News
  - Review
  
  ### List of Topics
  
  - Clinical Reports
  - Communication
  - Contact Tracing
  - Diagnostics
  - Drug Targets
  - Education
  - Effect on Medical Specialties
  - Forecasting & Modelling
  - Health Policy
  - Healthcare Workers
  - Imaging
  - Immunology
  - Inequality
  - Infection Reports
  - Long Haul
  - Medical Devices
  - Misinformation
  - Model Systems & Tools
  - Molecular Biology
  - Non-human
  - Non-medical
  - Pediatrics
  - Prevalence
  - Prevention
  - Psychology
  - Recommendations
  - Risk Factors
  - Surveillance
  - Therapeutics
  - Transmission
  - Vaccines
  