iTzMiNOS/mobilebert-uncased-fake-news-detector

Model Description

• Model Type: MobileBERT (fine-tuned on mobilebert-uncased)
• Task: Binary classification (Fake vs. Real news)
• Preprocessing: Text normalization (lowercasing, stopword removal, tokenization)
• Dataset: Custom fake news dataset (not publicly available, please use your own dataset)
• Evaluation Metrics: Accuracy, Classification Report (Precision, Recall, F1-Score)

Model Card

Intended Use

Limitations

• The model is trained on a specific dataset, so it might not generalize well to other domains or contexts of news.
• The performance could vary if the dataset contains other languages or heavily domain-specific terminology not seen during training.

Training Information

• Base Model: mobilebert-uncased
• Epochs: 2 epochs
• Batch Size: 32
• Learning Rate: 2e-5
• Training Libraries: transformers, torch, datasets
• Dataset : 20800 Entries of custom dataset (50% real news, 50% fake news)
• Preprocessing: The text is preprocessed using NLTK for stopword removal and tokenization.

Installation

Model Usage

Load the Model

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the fine-tuned model
model_name = "iTzMiNOS/mobilebert-uncased-fake-news-detector"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Example input text
text = "Breaking: The government announces new economic measures."

# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Inference
with torch.no_grad():
    logits = model(**inputs).logits

# Get predicted class (Fake = 1, Real = 0)
predicted_class = torch.argmax(logits, dim=-1).item()
print("Predicted Class:", "Fake" if predicted_class == 1 else "Real")

Inference Output

Real: The model predicts the article is reliable.

Fake: The model predicts the article is unreliable.

Conclusion