Datasets:
| 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 | |
| ## 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) | |