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---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- fake-news
- real-news
- binary-classification
- transformers
pretty_name: Fake New News Detection Dataset
size_categories:
- 10K<n<100K
---
# NewsFineTuning
A combined dataset of fake and true news articles for binary classification.
## Files
- `train.csv` – 70% training data
- `val.csv` – 15% validation data
- `test.csv` – 15% test data
Each CSV contains:
- `title` — Article title
- `text` — Full article content
- `subject` — Topic category
- `date` — Published date
- `label` — `0` = Real, `1` = Fake
## Task
This dataset is ideal for:
- Fake news detection
- Binary classification
- Fine-tuning transformers (e.g., DistilBERT)
## Load with Datasets
```python
from datasets import load_dataset
dataset = load_dataset("declan101/NewsFineTuning") |