Accelerating the search for antimicrobial peptides with Google Antigravity
deepmind.google·3d ago
AI Engineering
Hugging Face Blog·3d ago·5 min read
Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
TL;DR
Nemotron-pretraining-Indic-v1 introduces a massive dataset of 2.8 trillion tokens across 22 Indian languages, enhancing resources for Indic language model training. This initiative aims to bridge the data gap in Indian language technology.
✦ Why It Matters
Start using the Nemotron-pretraining-Indic-v1 dataset to enhance your Indic language models today.
Key Takeaways
- Explore the 2.8 trillion token dataset for Indic languages.
- Utilize the open data generation pipeline for custom data creation.
- Incorporate both native-script and romanized text in your models.
- Evaluate model performance using the provided Indic benchmarks.
How It Works
The data generation pipeline employs a four-stage process: translating English documents, assessing translation quality, repairing low-quality outputs, and converting accepted translations into romanized text. This ensures high-quality synthetic data while maintaining traceability to original documents.