amazon-science/synthesizrr
Synthesizing realistic and diverse text-datasets from augmented LLMs
This project helps machine learning researchers and data scientists create synthetic text datasets that are realistic and diverse. By augmenting large language models with retrieval, it takes existing text data and generates new, varied examples. The output is a high-quality dataset suitable for training or evaluating other natural language processing models.
Use this if you need to expand a limited text dataset for machine learning tasks, improve model robustness, or explore model performance on a wider range of text variations.
Not ideal if you need to generate entirely novel text content without any initial text data or if your primary goal is real-time text generation for user interaction.
Stars
16
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 26, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/amazon-science/synthesizrr"
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