Datasets:
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# Product Reviews
We post-process and split Product Reviews dataset to ensure uniformity with Political Statements 2.0 and Twitter Rumours as they all go into form GDDS-2.0
## Cleaning
Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
## Preprocessing
Whitespace, quotes, bulletpoints, unicode is normalized.
## Data
The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
There are 20971 samples in the dataset, contained in `product_reviews.jsonl`. For reproduceability, the data is also split into training, test, and validation sets in 80/10/10 ratio. They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified. The training set contains 16776 samples, the validation and the test sets have 2097 and 2098 samles, respectively.
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