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
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