Update README.md

README.md

@@ -11,20 +11,60 @@ size_categories:
 pretty_name: 'BAREC 2025: Readability Assessment Shared Task'
 ---
 
-# Dataset Card for BAREC Shared Task 2025
+# BAREC Shared Task 2025
 
 ## Dataset Summary
 
-The BAREC Shared Task 2025 focuses on fine-grained readability classification across 19 levels using the Balanced Arabic Readability Evaluation Corpus (BAREC), a dataset of over 1 million words.
+**BAREC** (the Balanced Arabic Readability Evaluation Corpus) is a large-scale dataset developed for the **BAREC Shared Task 2025**, focused on **fine-grained Arabic readability assessment**. The dataset includes over **1M words**, annotated across **19 readability levels**, with additional mappings to coarser 7, 5, and 3 level schemes.
 
-### Supported Tasks and Leaderboards
+The dataset is **annotated at the sentence level**. Document-level readability scores are derived by assigning each document the readability level of its **most difficult sentence**, based on the 19-level scheme. This provides both **sentence-level** and **document-level** readability information.
 
-The dataset can be used to train a model for readability assessment in 19, 7, 5, or 3 levels, which is a **multi-class text classification** task.
-Please checkout the [Shared Task website] for details on the sub-tasks and leaderboards.
+---
+
+## Supported Tasks and Leaderboards
+
+The dataset supports **multi-class readability classification** in the following formats:
+
+- **19 levels** (default)
+- **7 levels**
+- **5 levels**
+- **3 levels**
+
+For details on the shared task, evaluation setup, and leaderboards, visit the [Shared Task Website](https://barec.camel-lab.com/sharedtask2025).
+
+---
 
 ### Languages
 
-The BAREC dataset contains texts in Arabic language only.
+- **Arabic** (Modern Standard Arabic)
+
+---
+
+## How to Use
+
+You can load the dataset using Hugging Face Datasets by specifying the appropriate `data_files`.
+
+### Sentence-level dataset
+
+```python
+data_files={"train": "Sent_Train.csv", "dev": "Sent_Dev.csv", "test": "Sent_Test.csv"}
+barec = load_dataset("CAMeL-Lab/BAREC-Shared-Task-2025", data_files=data_files, token=token)
+barec_train = barec["train"]
+barec_dev = barec["dev"]
+barec_test = barec["test"]
+```
+
+### Document-level dataset
+
+```python
+data_files={"train": "Doc_Train.csv", "dev": "Doc_Dev.csv", "test": "Doc_Test.csv"}
+barec = load_dataset("CAMeL-Lab/BAREC-Shared-Task-2025", data_files=data_files, token=token)
+barec_train = barec["train"]
+barec_dev = barec["dev"]
+barec_test = barec["test"]
+```
+
+---
 
 ## Dataset Structure (Sentence-level)
 
@@ -34,21 +74,23 @@ The BAREC dataset contains texts in Arabic language only.
 
 ### Data Fields
 
-- **ID**: Sentence ID.
-- **Sentence**: Sentence text.
+- **ID**: Unique sentence identifier.
+- **Sentence**: The sentence text.
 - **Word_Count**: Number of words in the sentence.
 - **Readability_Level**: The readability level in `19-levels` scheme, ranging from `1-alif` to `19-qaf`.
 - **Readability_Level_19**: The readability level in `19-levels` scheme, ranging from `1` to `19`.
 - **Readability_Level_7**: The readability level in `7-levels` scheme, ranging from `1` to `7`.
 - **Readability_Level_5**: The readability level in `5-levels` scheme, ranging from `1` to `5`.
 - **Readability_Level_3**: The readability level in `3-levels` scheme, ranging from `1` to `3`.
-- **Annotator**: The annotator (it could be one of the five annotators `A1-A5` or Inter-Annotator Agreement `IAA`).
-- **Document**: The document where the sentence came from.
-- **Source**: The resource where the document came from.
-- **Book**: The book where the document came from.
-- **Author**: The Author of the document.
-- **Domain**: The domain of the document (it could be `Arts & Humanities`, `STEM` or `Social Sciences`).
-- **Text_Class**: The readership group (it could be `Foundational`, `Advanced` or `Specialized`).
+- **Annotator**: The annotator ID (`A1-A5` or `IAA`).
+- **Document**: Source document file name.
+- **Source**: Document source.
+- **Book**: Book name.
+- **Author**: Author name.
+- **Domain**: Domain (`Arts & Humanities`, `STEM` or `Social Sciences`).
+- **Text_Class**: Readership group (`Foundational`, `Advanced` or `Specialized`).
+
+---
 
 ## Dataset Structure (Document-level)
 
@@ -58,30 +100,50 @@ The BAREC dataset contains texts in Arabic language only.
 
 ### Data Fields
 
-- **ID**: Document ID.
-- **Document**: The document.
-- **Sentences**: All sentences in the document.
-- **Sentence_Count**: Number of sentences in the document.
-- **Word_Count**: Number of words in the document.
+- **ID**: Unique document identifier.
+- **Document**: Document file name.
+- **Sentences**: Full text of the document.
+- **Sentence_Count**: Number of sentences.
+- **Word_Count**: Total word count.
 - **Readability_Level**: The readability level in `19-levels` scheme, ranging from `1-alif` to `19-qaf`.
 - **Readability_Level_19**: The readability level in `19-levels` scheme, ranging from `1` to `19`.
 - **Readability_Level_7**: The readability level in `7-levels` scheme, ranging from `1` to `7`.
 - **Readability_Level_5**: The readability level in `5-levels` scheme, ranging from `1` to `5`.
 - **Readability_Level_3**: The readability level in `3-levels` scheme, ranging from `1` to `3`.
-- **Source**: The resource where the document came from.
-- **Book**: The book where the document came from.
-- **Author**: The Author of the document.
-- **Domain**: The domain of the document (it could be `Arts & Humanities`, `STEM` or `Social Sciences`).
-- **Text_Class**: The readership group (it could be `Foundational`, `Advanced` or `Specialized`).
+- **Source**: Document source.
+- **Book**: Book name.
+- **Author**: Author name.
+- **Domain**: Domain (`Arts & Humanities`, `STEM` or `Social Sciences`).
+- **Text_Class**: Readership group (`Foundational`, `Advanced` or `Specialized`).
+
+---
 
 ## Data Splits
 
-The BAREC dataset has three splits: *Train* (80%), *Dev* (10%), and *Test* (10%).
-The splits are in the document level.
-The splits are balanced accross *Readability Levels*, *Domains*, and *Text Classes*.
+- The BAREC dataset has three splits: *Train* (80%), *Dev* (10%), and *Test* (10%).
+- The splits are in the document level.
+- The splits are balanced accross *Readability Levels*, *Domains*, and *Text Classes*.
+
+---
+
+## Evaluation
+
+We define the Readability Assessment task as an ordinal classification task. The following metrics are used for evaluation:
+
+- **Accuracy (Acc<sup>19</sup>):** The percentage of cases where reference and prediction classes match in the 19-level scheme.
+- **Accuracy (Acc<sup>7</sup>, Acc<sup>5</sup>, Acc<sup>3</sup>):** The percentage of cases where reference and prediction classes match after collapsing the 19 levels into 7, 5, or 3 levels, respectively.
+- **Adjacent Accuracy (±1 Acc<sup>19</sup>):** Also known as off-by-1 accuracy. The proportion of predictions that are either exactly correct or off by at most one level in the 19-level scheme.
+- **Average Distance (Dist):** Also known as Mean Absolute Error (MAE). Measures the average absolute difference between predicted and true labels.
+- **Quadratic Weighted Kappa (QWK):** An extension of Cohen’s Kappa that measures the agreement between predicted and true labels, applying a quadratic penalty to larger misclassifications (i.e., predictions farther from the true label are penalized more heavily).
+
+We provide evaluation scripts [here](https://github.com/CAMeL-Lab/barec-shared-task-2025).
+
+---
 
 ## Citation
 
+If you use BAREC in your work, please cite the following papers:
+
 ```
 @inproceedings{elmadani-etal-2025-readability,
     title = "A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment",
@@ -93,4 +155,17 @@ The splits are balanced accross *Readability Levels*, *Domains*, and *Text Class
     address = "Vienna, Austria",
     publisher = "Association for Computational Linguistics"
 }
+
+@inproceedings{habash-etal-2025-guidelines,
+    title = "Guidelines for Fine-grained Sentence-level Arabic Readability Annotation",
+    author = "Habash, Nizar  and
+      Taha-Thomure, Hanada  and
+      Elmadani, Khalid N.  and
+      Zeino, Zeina  and
+      Abushmaes, Abdallah",
+    booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX)",
+    year = "2025",
+    address = "Vienna, Austria",
+    publisher = "Association for Computational Linguistics"
+}
 ```