Datasets:
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---
license: mit
language:
- en
tags:
- nlp
- ml
- dataset
- fake-news
- classification
pretty_name: x_g85_fn_dataset
configs:
- config_name: processed
data_files:
- split: train
path: fn_train.csv
- split: test
path: fn_test.csv
- split: valid
path: fn_valid.csv
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int32
---
# X_G85 Fake News Dataset
It is a preprocessed dataset that is used to build X_G85 ML Models. The collection of fake news that are collect from the following datasets
```
Labels
0: Fake News
1: Real News
```
## How to stream dataset & use as pandas dataframe
By streaming the dataset, it won't download on your host computer. Read more here [hugging face streaming dataset](https://huggingface.co/docs/datasets/stream).
```py
import pandas as pd
from datasets import load_dataset
```
> Note: The following operation may take some time depending on the size of the dataset.
```py
dataset = load_dataset("x-g85/x_g85_fn_dataset", streaming=True)
train = pd.DataFrame(dataset["train"])
valid = pd.DataFrame(dataset["valid"])
test = pd.DataFrame(dataset["test"])
```
```py
X_train = train["text"]
y_train = train["label"]
X_valid = valid["text"]
y_vaild = valid["label"]
X_test = test["text"]
y_test = test["label"]
```
## Credit
We have used the following datasets to create our own datasets and train models.
- [Kaggle: Fake news detection dataset english](https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english)
- [Kaggle: Liar Preprocessed](https://www.kaggle.com/datasets/khandalaryan/liar-preprocessed-dataset)
- [Kaggle: Stocknews](https://www.kaggle.com/datasets/aaron7sun/stocknews)
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