Smoltalk
Large textual synthetic dataset designed for the supervised fine-tuning of language models (LLM). It brings together several subsets covering various tasks such as rewriting, summarizing, summarizing, reasoning, generation constraints, as well as coding and mathematics, in order to improve the quality of instructive models.
Approximately 1 million textual examples, divided into several specialized subsets (editing, constraints, constraints, rewriting, summarizing, mathematics, code...), in JSON format usable with the Hugging Face Datasets library.
Apache 2.0
Description
The dataset Smoltalk is a synthetic collection of over a million textual examples created for the supervised fine-tuning of language models. It includes several subsets specialized in various tasks such as rewriting text, summarizing emails and articles, meeting specific constraints in generation, as well as data on mathematics and coding. This data is formatted for easy use with the Hugging Face Datasets library and is intended to improve the instruction and reasoning skills of LLMs.
What is this dataset for?
- Train Language Models to Better Follow Varied and Complex Instructions
- Improve performance in rewriting, summarizing, math, and programming
- Test and strengthen the understanding of the long context and the specific constraints in text generation
Can it be enriched or improved?
Yes, this dataset can be supplemented with specific annotations or adapted to other languages. Examples can be added that target specific areas or types of instructions that are not covered. Additionally, users can customize subsets by filtering or combining data as needed for specific tasks.
🔎 In summary
🧠 Recommended for
- NLP researchers
- LLM Developers
- Tuning instruction projects
🔧 Compatible tools
- Hugging Face Datasets
- Transformers
- PyTorch
- TensorFlow
💡 Tip
Use targeted subsets to optimize training according to the specific task in question.
Frequently Asked Questions
Is this dataset suitable for training models to understand complex text?
Yes, thanks to its varied subsets including summarization, rewriting and reasoning, it improves contextual understanding and controlled generation.
Can I use this dataset to train a model commercially?
Yes, the Apache 2.0 license allows commercial use without major restrictions.
Does the dataset contain examples in multiple languages?
Mostly in English, it does not include important multilingual data, but it can be extended or supplemented as needed.
