premsa/political-bias-prediction-allsides-BERT

model base: https://huggingface.co/google-bert/bert-base-uncased

dataset: https://github.com/ramybaly/Article-Bias-Prediction

training parameters:

• batch_size: 100
• epochs: 5
• dropout: 0.05
• max_length: 512
• learning_rate: 3e-5
• warmup_steps: 100
• random_state: 239

training methodology:

• sanitize dataset following specific rule-set, utilize random split as provided in the dataset
• train on train split and evaluate on validation split in each epoch
• evaluate test split only on the model that performed best on validation loss

result summary:

• throughout the five training epochs, model of second epoch achieved the lowest validation loss of 0.3314
• on test split second epoch model achieved f1 score of 0.9041

usage:

from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer


def main(repository: str):

    model = AutoModelForSequenceClassification.from_pretrained(repository)

    tokenizer = AutoTokenizer.from_pretrained(repository)

    nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

    print(nlp("the masses are controlled by media."))

if __name__ == "__main__":
    main(repository="premsa/political-bias-prediction-allsides-BERT")