readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: monkeypox-misinformation
    results: []
  ---
  
  ## Model description
  
  This model is a version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) fine-tuned on a monkeypox misinformation dataset available [here](https://www.kaggle.com/datasets/stephencrone/monkeypox).
  
  ## Intended uses & limitations
  
  The model is intended to be used for the binary classification of monkeypox related tweets into one of two classes: 'misinformation' or 'not misinformation'. 
  
  ## Training and evaluation data
  
  The training dataset consists of 5,787 tweets with an imbalanced distribution of the two classes (in favour of the 'not misinformation' class). Training and evaluation data can be accessed [here](https://github.com/stephen-crone/monkeypox).
  
  ## Training procedure
  
  The model was trained for 20 epochs using an early-stopping callback and restoration of weights for the epoch with the lowest validation loss. The following hyperparameters were used during training:
  
  - Initial learning rate: 5e-06
  - Learning rate strategy: reduce on plateau (patience = 2)
  - Batch size: 4
  - Optimiser: Adam
  
  ### Framework versions
  
  - Transformers 4.21.1
  - TensorFlow 2.8.2
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  