Add Arabic Poetry Periods Classification dataset (train/validation split 8:2)

README.md

@@ -1,3 +1,82 @@
 ---
-license: cc-by-4.0
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: Title
+      dtype: string
+    - name: Author
+      dtype: string
+    - name: Category
+      dtype: string
+    - name: Poem
+      dtype: string
+  splits:
+    - name: train
+      num_bytes: 16542253
+      num_examples: 20000
+    - name: validation
+      num_bytes: 4189717
+      num_examples: 5000
+  download_size: 38105941
+  dataset_size: 20731970
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train-*.parquet
+      - split: validation
+        path: validation-*.parquet
 ---
+
+# Arabic Poetry Periods Classification
+
+This dataset contains Arabic poetry with period classification. Each poem is classified into a specific historical period (Category).
+
+## Dataset Information
+
+The dataset is split into train (80%) and validation (20%) sets with stratified sampling based on the Category (period) to ensure balanced distribution.
+
+- **Train set**: 20000 examples
+- **Validation set**: 5000 examples
+- **Total**: 25000 examples
+
+## Data Format
+
+The dataset is stored in Parquet format with the following fields:
+
+- `id`: Unique identifier
+- `Title`: Poem title
+- `Author`: Author name
+- `Category`: Period category (historical period)
+- `Poem`: Poem text
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PoetryMTEB/ArabicPoetryPeriodsClassification")
+
+# Access train and validation splits
+train_data = dataset['train']
+validation_data = dataset['validation']
+```
+
+## Citation
+
+If you use this dataset, please cite the following paper:
+
+```
+@INPROCEEDINGS{10638967,
+  author={Ba Alawi, Abdulfattah E. and Bozkurt, Ferhat and Yağanoğlu, Mete},
+  booktitle={2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)}, 
+  title={BERT-AraPeotry: BERT-based Arabic Poems Classification Model}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1-5},
+  keywords={Training;Text analysis;Computational modeling;Bidirectional control;Writing;Transformers;Natural language processing;Arabic Text;Poem;Sentiment Analysis;Natural Language Processing},
+  doi={10.1109/eSmarTA62850.2024.10638967}
+}
+```

train-00000-of-00001.parquet

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+size 16542253

validation-00000-of-00001.parquet

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+oid sha256:9a39ad3b5830f0b2aa2369097ad6739a1a6c4c9cc0d0c8da9602ca0b7b7379a9
+size 4189717