mlf1_run3.py

training_args = {
    'train_batch_size':8, 
    'gradient_accumulation_steps':16, 
    'learning_rate': 2e-5, 
    'num_train_epochs': 5, 
    'max_seq_length': 60               # based on the histogram of number of words present in the headline
}

# model_type and model_name have been changed to now fine-tune RoBERTa instead of BERT
model_params = {
    'model_type': 'roberta',
    'model_name': 'roberta-base'
}

mlf_run_3 = mlf_api.create_run(project_name='financial-sentiment-analysis', run_name='roberta')
model3, result3, model_outputs3, wrong_predictions3 = trainModel(model_params, training_args, mlf_run_3, 'roberta')