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  ---
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- license: apache-2.0
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  task_categories:
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  - text-generation
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  language:
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  - mg
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  size_categories:
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  - 10K<n<100K
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: mit
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  task_categories:
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  - text-generation
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  language:
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  - mg
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  size_categories:
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  - 10K<n<100K
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+ ---
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+ ## Dataset Description
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+
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+ This dataset contains news articles, stories, and cultural reports scraped from [Global Voices Malagasy](https://mg.globalvoices.org/). It is intended to support Natural Language Processing (NLP) tasks for the Malagasy language, such as language modeling, text generation, and translation research.
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+ The data is automatically scraped and updated to ensure freshness.
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+
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+ ## Dataset Structure
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+
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+ The dataset is formatted in JSONL (JSON Lines). Each entry represents a single article.
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+
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+ ### Data Fields
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+ - `url` (string): The original URL of the article.
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+ - `title` (string): The title of the article.
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+ - `date` (string): The publication date (ISO format or similar).
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+ - `author` (string): The name of the author or translator.
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+ - `content` (string): The main body text of the article (cleaned).
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+
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+ ### Example
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+
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+ ```json
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+ {
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+ "url": "https://mg.globalvoices.org/2025/12/15/example-story",
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+ "title": "Lohateny momba ny fiarahamonina",
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+ "date": "2025-12-15T10:00:00+03:00",
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+ "author": "Rakoto",
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+ "content": "Ity dia ohatra iray amin'ny lahatsoratra hita ao amin'ny Global Voices..."
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+ }
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+ ```
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+ ## Use Cases
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+
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+ This dataset is ideal for training models on **formal and standard Malagasy**. Because news articles follow specific grammatical rules and journalistic standards, they provide a clean baseline for NLP.
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+ * **Language Modeling (LLM Pre-training):** To teach models the core grammar, syntax, and vocabulary of the Malagasy language in a formal context.
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+ * **Named Entity Recognition (NER):** The dataset contains numerous mentions of people, locations, organizations, and dates relevant to Madagascar and the world, useful for training entity extractors.
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+
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+ ### How to use
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("Lo-Renz-O/GBV-Malagasy")
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+
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+ # Print the first example
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+ print(dataset['train'][0])
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+ ```
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+