| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - feature-extraction |
| language: |
| - mg |
| --- |
| |
| ## Overview |
| This dataset consists of clean, structured **sentences** extracted via Optical Character Recognition (OCR) from approximately **1GB of Malagasy thesis documents**. These documents were collected based on educational, cultural, and linguistic themes. |
| The dataset is saved in **CSV format**, and is particularly useful for NLP tasks involving **sentence-level modeling** in Malagasy — a low-resource language. |
|
|
| ## Dataset Details |
| - **Language**: Malagasy |
| - **Source**: OCR'd academic thesis documents in PDF form |
| - **Download URL**: [Université d’Antananarivo Thesis Library](http://www.biblio.univ-antananarivo.mg/theses2/) |
| - **Collection Keywords**: `sekoly`, `boky`, `fampianarana`, `fiangonana`, `fanabeazana`, `tontolo`, `gazety`, `asa`, `tononkalo`, `faritra`, `teny`, `fiteny`, `soratra`, `poeta`, `tantara`, `literatiora`, `fomba` |
| - **Format**: CSV |
| - **Column(s)**: `text` |
| - **Granularity**: Each row contains a **single sentence**. |
|
|
| ## Preprocessing Pipeline |
| The following steps were used to clean and normalize the raw OCR text: |
|
|
| 1. **Unicode normalization** using NFKC to standardize characters. |
| 2. **URL removal** to eliminate web links from scanned content. |
| 3. **Quote standardization**, converting straight quotes to typographic quotes. |
| 4. **Non-alphanumeric character removal**, excluding allowed punctuation. |
| 5. **Punctuation spacing**, ensuring correct spacing after commas, periods, etc. |
| 6. **Removal of structured markers** such as: |
| - Numbered headings (`1.`, `1.1.1`, etc.) |
| - Lettered sections (`a.`, `b-1`, etc.) |
| - Roman numeral references (`IV-2`, etc.) |
| 7. **Consecutive punctuation cleanup** to reduce noise from OCR errors. |
| 8. **Paragraph structure fixes**: |
| - Merging broken paragraphs that were split across lines or pages. |
| - Removing paragraphs shorter than 10 characters. |
| 9. **Sentence segmentation** to split structured paragraphs into **individual sentences**. |
| 10. **Whitespace normalization** to remove extra spaces and line breaks. |
| 11. **Deduplicated and Shuffled** |
|
|
| These steps were applied **iteratively** for high-quality, standardized sentence-level data. |
|
|
| ## Potential Applications |
| This dataset is well-suited for: |
| - **Sentence-level language modeling** and generation in Malagasy |
| - **Fine-tuning multilingual NLP models** on Malagasy |
|
|
| ## Limitations |
| - Some sentences may contain **French words or phrases**, as they are sometimes used in citations or quoted material within the thesis documents. |
| - OCR errors may still be present in some complex layouts or highly degraded scans. |
|
|
| ## Usage |
| To load this dataset using the Hugging Face `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset('Lo-Renz-O/malagasy-sentence') |
| print(dataset['train'][0]) |
| ``` |
| ## Contribution |
| We welcome contributions to improve this dataset! If you have suggestions or additional Malagasy text sources, feel free to open a discussion or submit data on Hugging Face. |
