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Barhomy/
distilbert-fake-news-model

Name: distilbert-fake-news-model
Availability: InStock

safetensors

distilbert

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📰 DistilBERT Fake News Classifier

This is a fine-tuned DistilBERT model for binary fake news classification (TRUE vs FAKE).

🔍 Model Details

Base model: distilbert-base-uncased
Format: safetensors (268MB)
Classification labels:
- 0: TRUE (real news)
- 1: FAKE (misleading content)

💻 Usage in Python

python

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from huggingface_hub import snapshot_download
import torch

model_path = snapshot_download("Barhomy/distilbert-fake-news-model")
tokenizer = DistilBertTokenizer.from_pretrained(model_path)
model = DistilBertForSequenceClassification.from_pretrained(model_path)

inputs = tokenizer("Aliens have landed in Paris!", return_tensors="pt")
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits).item()
print("FAKE" if prediction == 1 else "TRUE")