FakeBERTa

FakeBerta: A Fine-Tuned DistilRoBERTa Model for Fake News Detection

You can check the model's fine-tuning code on my GitHub.

Model Overview

FakeBerta is a fine-tuned version of DistilRoBERTa for detecting fake news. The model is trained to classify news articles as real (0) or fake (1) using natural language processing (NLP) techniques. Base Model: DistilRoBERTa Task: Fake news classification

Example of code using AutoModelForSequenceCalssification:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_name = "YerayEsp/FakeBerta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

inputs = tokenizer("Breaking: Scientists discover water on Mars!", return_tensors="pt")
outputs = model(**inputs)

logits = outputs.logits
predicted_class = torch.argmax(logits).item()

print(f"Predicted class: {predicted_class}")  # 0 = Real, 1 = Fake

Library: Transformers (Hugging Face)