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README.md

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----
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 58475891
-    num_examples: 163949
-  download_size: 34690122
-  dataset_size: 58475891
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+# malagasy-sentence
+
+## 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 using the following keywords:
+
+**"sekoly, boky, fampianarana, fiangonana, fanabeazana, tontolo, gazety, asa, tononkalo, faritra, teny, fiteny, soratra, poeta, tantara, literatiora, fomba"**
+
+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)**: `sentence`
+- **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.
+