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  - en
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  size_categories:
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  - 10K<n<100K
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - en
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  size_categories:
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  - 10K<n<100K
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+ ---
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+ ## LIAR2
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+ The [LIAR](https://doi.org/10.18653/v1/P17-2067) dataset has been widely followed by fake news detection researchers since its release, and along with a great deal of research, the community has provided a variety of feedback on the dataset to improve it. We adopted these feedbacks and released the LIAR2 dataset, a new benchmark dataset of ~23k manually labeled by professional fact-checkers for fake news detection tasks. We have used a split ratio of 8:1:1 to distinguish between the training set, the test set, and the validation set, details of which are provided in the paper of "[An Enhanced Fake News Detection System With Fuzzy Deep Learning](https://doi.org/10.1109/ACCESS.2024.3418340)". The LIAR2 dataset can be accessed at [Huggingface](https://huggingface.co/datasets/chengxuphd/liar2) and [Github](https://github.com/chengxuphd/LIAR2),
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+ ## Example Usage
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+ You can load each of the subset as follows:
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+ ```python
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+ import datasets
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+ dataset = "chengxuphd/liar2"
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+ dataset = datasets.load_dataset(dataset)
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+
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+ statement_train, y_train = dataset["train"]["statement"], dataset["train"]["label"]
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+ statement_val, y_train = dataset["val"]["statement"], dataset["val"]["label"]
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+ statement_test, y_test = dataset["test"]["statement"], dataset["test"]["label"]
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+ ```