premsa/political-bias-prediction-allsides-mDeBERTa

model base: https://huggingface.co/microsoft/mdeberta-v3-base

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

training parameters:

• devices: 2xH100
• 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 0.2573
• on test split second epoch model achieved f1 score of 0.9184 and a test loss of 0.2904

usage:

model = AutoModelForSequenceClassification.from_pretrained("premsa/political-bias-prediction-allsides-mDeBERTa")
tokenizer = AutoTokenizer.from_pretrained("premsa/political-bias-prediction-allsides-mDeBERTa")
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
print(nlp("die massen werden von den medien kontrolliert."))