This is a sequence classification model designed to detect fake news articles. It was built by fine-tuning the 110-million parameter bert-base-uncased model on a balanced dataset of real and fake news.

The model outputs binary predictions:

0: Fake News
1: Real News

Training Procedure

The model was fine-tuned using the Hugging Face Trainer API with the following hyperparameters:

Learning Rate: 2e-5
Train/Eval Batch Size: 8
Epochs: 3
Weight Decay: 0.01
Max Sequence Length: 512 tokens (strict truncation)

Evaluation Metrics

During training, the model was evaluated using a custom compute metric function. It tracks:

Accuracy
F1-Score

How to Use

You can easily load and test this model using the transformers library:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_id = "ahmednawaz8813/BERT_Fine_Tuned"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# Test with an article snippet
article_text = "Scientists have discovered a new species of deep-sea jellyfish."
inputs = tokenizer(article_text, return_tensors="pt", truncation=True, max_length=512, padding="max_length")

# Get prediction
with torch.no_grad():
    logits = model(**inputs).logits
    predicted_class_id = logits.argmax().item()

label_map = {0: "Fake News", 1: "Real News"}
print(f"Prediction: {label_map[predicted_class_id]}")