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
- Text Classification
- Topic Classification
- Title Generation
- Greek
- Greek NLP
pretty_name: Greek Reddit
size_categories:
- 1K<n<10K
GreekReddit
A Greek topic classification dataset collected from Greek subreddits, which contains 6,534 posts, their titles and topic labels. This dataset has been used to train our best-performing model as part of our upcoming research article: Mastrokostas, C., Giarelis, N., & Karacapilidis, N. (2024). Social Media Topic Classification on Greek Reddit For information about dataset creation, limitations etc. see the original article.
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: (class label): The class label of the post.
Data Splits
| Split | No of Documents |
|---|---|
| Train | 5,530 |
| Validation | 504 |
| Test | 500 |
Example code
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 model please e-mail one of the following authors:
giarelis@ceid.upatras.gr
cmastrokostas@ac.upatras.gr
karacap@upatras.gr
Citation
TBA