Update README.md

README.md

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   - split: train
     path: data/train-*
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+# Afrikaans-English Translation Dataset
+
+A high-quality, curated parallel corpus for machine translation between English (en) and Afrikaans (af), created through extensive data processing and quality filtering.
+
+## 📊 Dataset Overview
+
+- **Language Pair**: English → Afrikaans (en-af)
+- **Total Sentence Pairs**: 161,644
+- **Quality Threshold**: QE ≥ 0.7
+- **Processing Date**: September 22, 2025
+- **Repository**: [amanuelbyte/af-en-translation-dataset](https://huggingface.co/amanuelbyte/af-en-translation-dataset)
+
+## 🎯 Key Features
+
+- **Quality Assured**: All sentence pairs meet minimum Quality Estimation (QE) score of 0.7
+- **Deduplicated**: Extensive deduplication process removes redundant content
+- **Multi-Domain**: Covers diverse domains including technical, religious, educational, and general content
+- **Clean Data**: Filtered for language identification, semantic similarity, and rule-based quality checks
+
+## 📈 Dataset Statistics
+
+### Data Volume
+- **Before Deduplication**: 192,541 sentence pairs
+- **After Deduplication**: 161,644 sentence pairs
+- **Deduplication Ratio**: 16.1% reduction
+
+### Quality Metrics
+- **Average QE Score**: 0.78
+- **Highest Quality Dataset**: Tatoeba (QE: 0.861)
+- **Lowest Quality Dataset**: KDE4 (QE: 0.723)
+
+## 📚 Data Sources
+
+This dataset combines **20 high-quality sources**:
+
+| Source | Dataset | Pairs | QE Score | Domain |
+|--------|---------|-------|----------|---------|
+| wikimedia | Wikipedia Articles | 56,766 | 0.781 | General |
+| bible-uedin | Bible Translations | 51,118 | 0.748 | Religious |
+| OpenSubtitles | Movie Subtitles | 37,731 | 0.795 | Entertainment |
+| qed | Europarl Questions | 10,412 | 0.773 | Political |
+| GNOME | Software Localization | 6,169 | 0.761 | Technical |
+| spc | Scientific Papers | 6,811 | 0.739 | Academic |
+| kde4 | KDE Software | 5,365 | 0.723 | Technical |
+| Pontoon-Translations | Mozilla Translations | 5,606 | 0.785 | Technical |
+| Weblate-Translations | Weblate Platform | 3,978 | 0.757 | General |
+| TED2020 | TED Talks | 1,882 | 0.783 | Educational |
+| ntrex | News Articles | 1,806 | 0.777 | News |
+| Tatoeba | Community Translations | 2,197 | 0.861 | General |
+| smol | Smol Dataset | 812 | 0.789 | General |
+| lingvanex_test_references | Test References | 748 | 0.840 | General |
+| ELRC-wikipedia_health | Health Wikipedia | 371 | 0.808 | Medical |
+| ELRC_2922 | European Language Resources | 369 | 0.808 | General |
+| ELRC-3039-wikipedia_health | Health Wikipedia | 371 | 0.808 | Medical |
+| ted_talks_iwslt | TED/IWSLT Talks | 27 | 0.770 | Educational |
+| ELRC-2638-monumentos_2007 | Cultural Heritage | 2 | 0.848 | Cultural |
+| lingvanex_test_references | Test References | 748 | 0.840 | General |
+
+## 🛠️ Processing Pipeline
+
+The dataset underwent rigorous processing:
+
+1. **Rule-based Filtering**: Length constraints, character validation
+2. **Semantic Filtering**: Sentence embedding similarity (threshold: 0.7)
+3. **Language Detection**: FastText + AfroLID models for accurate language identification
+4. **Quality Estimation**: Africomet QE model for translation quality scoring
+5. **Sentence-level QE Filtering**: Only pairs with QE ≥ 0.7 retained
+6. **Deduplication**: 4-stage process removing identical and similar content
+
+### Deduplication Process
+1. Remove identical source-target pairs
+2. Remove duplicate (source, target) combinations
+3. Remove duplicate source sentences
+4. Remove duplicate target sentences
+
+## 💾 Dataset Format
+
+The dataset is available in Hugging Face `datasets` format with the following structure:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("amanuelbyte/af-en-translation-dataset")
+print(dataset)
+
+# Dataset structure:
+# DatasetDict({
+#     "train": Dataset({
+#         features: ['en', 'af'],
+#         num_rows: 161644
+#     })
+# })
+```
+
+### Columns
+- `en`: English source text
+- `af`: Afrikaans target text
+
+## 📖 Usage Examples
+
+### Loading the Dataset
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("amanuelbyte/af-en-translation-dataset")
+
+# Access the training split
+train_data = dataset["train"]
+
+# Get a sample
+sample = train_data[0]
+print(f"English: {sample['en']}")
+print(f"Afrikaans: {sample['af']}")
+```
+
+### For Machine Translation Training
+```python
+# Prepare data for training
+source_texts = train_data["en"]
+target_texts = train_data["af"]
+
+# Statistics
+print(f"Dataset size: {len(train_data)}")
+print(f"Average source length: {sum(len(text.split()) for text in source_texts) / len(source_texts)".1f"}")
+print(f"Average target length: {sum(len(text.split()) for text in target_texts) / len(target_texts)".1f"}")
+```
+
+## 🎓 Applications
+
+This dataset is suitable for:
+- **Machine Translation**: Training and fine-tuning MT models
+- **Language Modeling**: Pretraining language models for Afrikaans
+- **Cross-lingual Transfer**: Multilingual NLP tasks
+- **Quality Estimation**: Research on translation quality
+- **Linguistic Studies**: Analysis of Afrikaans-English language pairs
+
+## 🔍 Quality Assessment
+
+The dataset includes comprehensive quality metrics:
+
+- **Individual Dataset QE Scores**: Range from 0.723 to 0.861
+- **Overall Average QE**: 0.78
+- **Quality Distribution**: All datasets exceed 0.7 threshold
+- **Consistency**: Uniform quality across different domains
+
+## 📄 License
+
+This dataset is released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license. The individual source datasets maintain their original licenses.
+
+## 🤝 Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{af-en-translation-dataset-2025,
+  title={Afrikaans-English Translation Dataset},
+  author={Amanuel Tewolde},
+  year={2025},
+  publisher={Hugging Face},
+  journal={Hugging Face Hub},
+  howpublished={\url{https://huggingface.co/amanuelbyte/af-en-translation-dataset}}
+}
+```
+
+## 📞 Contact
+
+For questions or issues regarding this dataset, please contact:
+- **Maintainer**: Amanuel Tewolde
+- **Email**: [email protected]
+- **Organization**: Hugging Face Community
+
+## 🔗 Related Resources
+
+- [AfroLID Model](https://huggingface.co/UBC-NLP/afrolid_1.5) - Language identification
+- [Africomet QE](https://huggingface.co/masakhane/africomet-qe-stl) - Quality estimation
+- [Processing Pipeline](https://github.com/amanuelbyte/mt-data-processing) - Source code
+
+---
+
+**Note**: This dataset was created using automated processing pipelines with quality thresholds. While extensive filtering has been applied, users should perform their own quality assessment based on their specific use cases.