Datasets:
Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,59 @@
|
|
1 |
-
---
|
2 |
-
license: mit
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
task_categories:
|
4 |
+
- text-classification
|
5 |
+
language:
|
6 |
+
- en
|
7 |
+
tags:
|
8 |
+
- fake-news
|
9 |
+
- real-news
|
10 |
+
- binary-classification
|
11 |
+
- transformers
|
12 |
+
pretty_name: Fake New News Detection Dataset
|
13 |
+
size_categories:
|
14 |
+
- 10K<n<100K
|
15 |
+
---
|
16 |
+
license: mit
|
17 |
+
task_categories:
|
18 |
+
- text-classification
|
19 |
+
language:
|
20 |
+
- en
|
21 |
+
tags:
|
22 |
+
- fake-news
|
23 |
+
- real-news
|
24 |
+
- binary-classification
|
25 |
+
- transformers
|
26 |
+
pretty_name: Fake New News Detection Dataset
|
27 |
+
size_categories:
|
28 |
+
- 10K<n<100K
|
29 |
+
---
|
30 |
+
# NewsFineTuning
|
31 |
+
|
32 |
+
A combined dataset of fake and true news articles for binary classification.
|
33 |
+
|
34 |
+
## Files
|
35 |
+
|
36 |
+
- `train.csv` – 70% training data
|
37 |
+
- `val.csv` – 15% validation data
|
38 |
+
- `test.csv` – 15% test data
|
39 |
+
|
40 |
+
Each CSV contains:
|
41 |
+
- `title` — Article title
|
42 |
+
- `text` — Full article content
|
43 |
+
- `subject` — Topic category
|
44 |
+
- `date` — Published date
|
45 |
+
- `label` — `0` = Real, `1` = Fake
|
46 |
+
|
47 |
+
## Task
|
48 |
+
|
49 |
+
This dataset is ideal for:
|
50 |
+
- Fake news detection
|
51 |
+
- Binary classification
|
52 |
+
- Fine-tuning transformers (e.g., DistilBERT)
|
53 |
+
|
54 |
+
## Load with Datasets
|
55 |
+
|
56 |
+
```python
|
57 |
+
from datasets import load_dataset
|
58 |
+
|
59 |
+
dataset = load_dataset("declan101/NewsFineTuning")
|