--- license: apache-2.0 language: - el pipeline_tag: text-classification task_categories: - text-classification - text-generation - zero-shot-classification task_ids: - multi-class-classification - topic-classification tags: - Social Media - Reddit - Greek NLP - Text Classification - Topic Classification - Title Generation pretty_name: Greek Reddit size_categories: - 1K ### Supported Tasks and Leaderboards This dataset supports: **Multi-class Text Classification:** Given the text of a post, a model learns to predict the associated topic label. **Title Generation:** Given the text of a post, a text generation model learns to generate a post title. ### Languages All posts are written in Greek. ## Dataset Structure ### Data Instances The dataset is structured as a `.csv` file, while three dataset splits are provided (train, validation and test). ### Data Fields The following data fields are provided for each split: `id`: (**str**) A unique post id. `title`: (**str**) A short post title. `text`: (**str**) The full text of the post. `url`: (**str**) The URL which links to the original unprocessed post. `category`: (**str**): The class label of the post. ### Data Splits |Split|No of Documents| |-------------------|------------------------------------| |Train|5,530| |Validation|504| |Test|500| ### Example code ```python from datasets import load_dataset # Load the training, validation and test dataset splits. train_split = load_dataset('IMISLab/GreekReddit', split = 'train') validation_split = load_dataset('IMISLab/GreekReddit', split = 'validation') test_split = load_dataset('IMISLab/GreekReddit', split = 'test') print(test_split[0]) ``` ## Contact If you have any questions/feedback about the dataset please e-mail one of the following authors: ``` giarelis@ceid.upatras.gr cmastrokostas@ac.upatras.gr karacap@upatras.gr ``` ## Citation ``` @article{mastrokostas2024social, title={Social Media Topic Classification on Greek Reddit}, author={Mastrokostas, Charalampos and Giarelis, Nikolaos and Karacapilidis, Nikos}, journal={Information}, volume={15}, number={9}, pages={521}, year={2024}, publisher={Multidisciplinary Digital Publishing Institute} } ```