Added dataset description inside readme

README.md

@@ -29,4 +29,42 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+task_categories:
+- text-classification
+language:
+- it
+pretty_name: ff
+size_categories:
+- 1K<n<10K
 ---
+# SardiStance
+**Disclaimer: This dataset is not the official SardiStance repository from EVALITA. For the official dataset and more information, please visit the [EVALITA SardiStance page](http://www.di.unito.it/~tutreeb/sardistance-evalita2020/index.html) and the [SardiStance repository](https://github.com/mirkolai/evalita-sardistance)**
+
+SardiStance is a unique dataset designed for the task of stance detection in Italian tweets. It consists of tweets related to the Sardines movement, providing a valuable resource for researchers and practitioners in the field of NLP. This dataset was curated for the EVALITA 2020 competition, aiming to advance the automatic detection of stance in social media text.
+
+## Dataset Description
+
+The SardiStance dataset contains tweets written in Italian, specifically focusing on the Sardines movement. The primary goal is to classify the stance of each tweet into one of three categories: `AGAINST`, `FAVOR` or `NONE` towards the Sardines movement. The dataset includes a variety of tweets, capturing different opinions and sentiments expressed by users on this political movement.
+
+
+## Data Format and Annotations
+The entire SardiStance dataset consists of 3,242 instances, divided into training and test sets. The training set contains 2,132 tweets, while the test set includes 1,110 tweets. The dataset was collected by retrieving tweets containing keywords related to the Sardines movement and their associated hashtags. Retweets, quotes, replies, and tweets containing URLs or extended entities (videos, photos, GIFs) were excluded to ensure data quality.
+
+The dataset is composed of the following columns:
+- `tweet_id`: the id of the sample
+- `text`: The dataset includes the full text of each tweet.
+- `label`: Each tweet is annotated with one of three stance labels: `AGAINST`, `FAVOR` or `NONE`.
+
+## Citations:
+If you use this dataset, please cite the original authors:
+
+```
+@inproceedings{cignarella2020sardistance,
+  title={Sardistance@ evalita2020: Overview of the task on stance detection in italian tweets},
+  author={Cignarella, Alessandra Teresa and Lai, Mirko and Bosco, Cristina and Patti, Viviana and Rosso, Paolo and others},
+  booktitle={CEUR WORKSHOP PROCEEDINGS},
+  pages={1--10},
+  year={2020},
+  organization={Ceur}
+}
+```