omanyasa/zim-langid-v2

Dataset Card for Zimbabwe Multilingual Language Identification Dataset

Dataset Description

This dataset contains text samples from 5 Zimbabwean languages for language identification tasks. It represents a comprehensive collection of Zimbabwean linguistic data designed to support natural language processing and machine learning research for African languages.

Languages

• English (en): International language with widespread usage
• Shona (sn): Major Zimbabwean language with ~7 million speakers
• Chewa (ny): Bantu language spoken in Zimbabwe and neighboring countries (ISO 639-1: ny)
• Ndebele (nd): Major Zimbabwean language with ~2 million speakers (ISO 639-1: nd)
• Tonga (to): Bantu language spoken in Zimbabwe and Zambia (ISO 639-1: to)

Dataset Statistics

• Total Samples: 164,595
• Training Split: 131,676 samples
• Validation Split: 16,459 samples
• Test Split: 16,460 samples
• Average Length: Varies by language and text source
• Format: FastText format with standardized labels

Class Distribution

Language	Code	Training	Validation	Test	Total
English	en	26,357	3,222	3,340	32,919
Shona	sn	26,421	3,248	3,250	32,919
Chewa	ny	26,365	3,285	3,269	32,919
Ndebele	nd	26,326	3,344	3,249	32,919
Tonga	to	26,207	3,360	3,352	32,919
Total		131,676	16,459	16,460	164,595

Data Format

FastText Format

__label__en This is English text
__label__sn Zvimhu zita rake shona
__label__ny Muli bwanji chichewa
__label__nd Salingelele isindebele
__label__to Mwapona wa ci Tonga

Hugging Face Format

{"text": "This is English text", "label": "en"}
{"text": "Zvimhu zita rake shona", "label": "sn"}
{"text": "Muli bwanji chichewa", "label": "ny"}
{"text": "Salingelele isindebele", "label": "nd"}
{"text": "Mwapona wa ci Tonga", "label": "to"}

Data Sources

• Public domain religious texts: Open-access religious and educational materials
• Educational materials: School textbooks and learning resources
• Open-access textual corpora: Public domain literary and news content
• Community-contributed samples: Native speaker contributions and verified content

Data Quality

• Label Accuracy: Manually verified by native speakers
• Text Cleaning: Standardized preprocessing applied
• Dialect Balance: Representative sampling across major dialects
• Quality Control: Automated validation and manual review

Intended Uses

• Language Identification: Training classification models
• Linguistic Research: Studying language patterns and features
• Educational: Teaching NLP concepts with African languages
• Cross-lingual Applications: Building multilingual systems

Limitations

• Domain Specific: Primarily formal text, limited informal language
• Dialect Coverage: May not cover all regional variations
• Imbalance: Some languages may have more samples than others
• Code-switching: Limited examples of mixed-language text

Ethical Considerations

• Representation: Efforts made to balance language representation
• Cultural Sensitivity: Text reviewed for cultural appropriateness
• Data Privacy: Personal information removed from text samples
• Community Involvement: Native speakers consulted in validation

Usage Example

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("omanyasa/zim-langid-v2")

# Access training data
train_data = dataset["train"]
print(f"Training samples: {len(train_data)}")

# View a sample
sample = train_data[0]
print(f"Text: {sample['text']}")
print(f"Language: {sample['label']}")

# Get label distribution
from collections import Counter
label_dist = Counter(train_data["label"])
print("Label distribution:", dict(label_dist))

Research Impact

This dataset contributes to low-resource African NLP by:

• Supporting language identification for underrepresented languages
• Enabling multilingual model development for Zimbabwean languages
• Improving inclusivity in NLP systems for African languages
• Providing benchmark data for cross-lingual transfer learning
• Facilitating research on low-resource language processing

Future Work

• Expansion: Scale to 16 Zimbabwean languages
• Code-switching: Include mixed-language text samples
• Multimodal: Add speech + text alignment data
• LLM Integration: Fine-tune large language models for Zimbabwean languages
• Dialect Coverage: Include regional variations and dialects

Maintenance

• Updates: Planned quarterly with new text samples
• Version Control: Semantic versioning for dataset updates
• Community Feedback: Open to contributions and corrections
• Quality Assurance: Regular validation and cleaning processes

Citation

If you use this dataset, please cite:

@dataset{zim_langid_v2,
  title={Zimbabwe Multilingual Language Identification Dataset},
  author={MSU National Language Institute (MSUNLI)},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/omanyasa/zim-langid-v2}
}

License

This dataset is licensed under the MIT License, allowing for commercial and non-commercial use with proper attribution.

Contact

For questions, contributions, or feedback regarding this dataset:

• Repository: https://huggingface.co/datasets/omanyasa/zim-langid-v2
• Organization: MSU National Language Institute (MSUNLI)
• Issues: Use GitHub issues or Hugging Face discussions