BERT-FakeNews-Classification

Model Description

This model is a multiclass classification model trained on the Liar Dataset using the BERT (bert-base-uncased) architecture. The primary task is to classify news articles into different categories, making it suitable for fake news detection.
BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model known for its effectiveness in natural language processing tasks.

The model classifies the input text into one of 6 target classes.

Bias: The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions.

Code Implementation

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model directly
tokenizer = AutoTokenizer.from_pretrained(
    "Arjun24420/BERT-FakeNews-Classification")
model = AutoModelForSequenceClassification.from_pretrained(
    "Arjun24420/BERT-FakeNews-Classification")

# Define class labels mapping
class_mapping = {
    0: 'half-true',
    1: 'mostly-true',
    2: 'false',
    3: 'true',
    4: 'barely-true',
    5: 'pants-fire'
}


def predict(text):
    # Tokenize the input text and move tensors to the GPU if available
    inputs = tokenizer(text, padding=True, truncation=True,
                       max_length=512, return_tensors="pt")

    # Get model output (logits)
    outputs = model(**inputs)

    probs = outputs.logits.softmax(1)
    # Get the probabilities for each class
    class_probabilities = {class_mapping[i]: probs[0, i].item()
                           for i in range(probs.shape[1])}

    return class_probabilities