Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Arabic
Size:
10K - 100K
License:
| license: apache-2.0 | |
| task_categories: | |
| - text-classification | |
| language: | |
| - ar | |
| tags: | |
| - news | |
| - arabic | |
| - classification | |
| pretty_name: news_2026_exercise | |
| size_categories: | |
| - 10K<n<100K | |
| # news_2026_exercise | |
| Arabic news dataset for text classification. | |
| | Column | Description | | |
| |--------|-------------| | |
| | `title` | News title | | |
| | `content` | News body | | |
| | `category` | Label: سياسة, اقتصاد, صحة, رياضة | | |
| **28,000** rows (7,000 per category). | |
| ## Download and load as pandas | |
| ```python | |
| from datasets import load_dataset | |
| import pandas as pd | |
| ds = load_dataset("maher13/news_2026_exercise") | |
| df = ds["train"].to_pandas() | |
| print(df.head()) | |
| ``` | |
| ## Sample N rows from each category | |
| ```python | |
| from datasets import load_dataset | |
| import pandas as pd | |
| N = 100 # change this | |
| ds = load_dataset("maher13/news_2026_exercise") | |
| df = ds["train"].to_pandas() | |
| sample_df = ( | |
| df.groupby("category", group_keys=False) | |
| .apply(lambda x: x.sample(n=min(N, len(x)), random_state=42)) | |
| .reset_index(drop=True) | |
| ) | |
| print(sample_df["category"].value_counts()) | |
| print(sample_df.head()) | |
| ``` | |