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Datasets:
themohal
/
saraiki-llm-dataset

Tasks:
Text Generation
Mask Generation
Modalities:
Text
Formats:
parquet
Languages:
Saraiki
Size:
100K - 1M
Tags:
saraiki-continual-pre-training
saraiki
DOI:
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
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Intended Uses

This dataset can support:

  • Saraiki LLM pre-training and continued pre-training
  • Supervised fine-tuning (SFT)
  • Instruction tuning
  • Saraiki text generation
  • Language understanding
  • NLP research
  • Low-resource language modeling
  • Multilingual AI research
  • LLM evaluation
  • Academic and educational projects
  • Development of Saraiki language applications

Why This Dataset Matters

High-quality language data is an important foundation for building capable language models. For low-resource languages such as Saraiki, curated datasets can help researchers investigate how modern LLM techniques can be adapted to languages with comparatively limited digital resources.

This dataset is intended to contribute to the broader development of Saraiki NLP and language technology, while encouraging further research into Saraiki language modeling and culturally relevant AI systems.

Access Requirements

Access to this dataset is gated. Users are required to provide information about their intended use and agree to the applicable usage conditions before accessing the dataset.

By requesting access, users agree not to use the dataset to conduct experiments that cause harm to human subjects and agree to use the dataset for non-commercial purposes only.

Citation

If you use this dataset in research, publications, models, or publicly released projects, please cite and acknowledge the dataset:

@misc{raza2026saraikillm,
  title        = {Saraiki LLM Dataset},
  author       = {Raza, Muhammad Farjad Ali},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/themohal/saraiki-llm-dataset}}
}

Disclaimer

This dataset is provided for research and educational purposes. Users are responsible for ensuring that their use of the dataset complies with applicable laws, ethical requirements, and the dataset's license and access conditions.
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