readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: US_politicians_covid_skepticism
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  
  
  This model is a fine-tuned version of [vinai/bertweet-covid19-base-uncased](https://huggingface.co/vinai/bertweet-covid19-base-uncased) on a dataset of 10k tweets about COVID-19 policies from US legislators in the House and Senate. 
  
  The model is intended to identify skepticism of COVID-19 policies (i.e. masks, social distancing, lockdowns, vaccines etc.). The model classifies as 1 (expressing skepticism/opposition to a COVID-19 policy or 0 (no opposition)
  
  It achieves the following results on the evaluation set:
  - Train Loss: 0.1007
  - Train Sparse Categorical Accuracy: 0.9591
  - Validation Loss: 0.0913
  - Validation Sparse Categorical Accuracy: 0.9627
  - Epoch: 3
  
  
  The following hyperparameters were used during training:
  - optimizer: {'name': 'Adam', 'learning_rate': 5e-07, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  - training_precision: float32
  
  ### Training results
  
  | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
  | 0.1822     | 0.9345                            | 0.1021          | 0.9584                                 | 0     |
  | 0.1007     | 0.9591                            | 0.0913          | 0.9627                                 | 1     |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - TensorFlow 2.8.2
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  