IBM Research introduces Unitxt, a collaborative platform for processing unified textual data, offering a Python module with configurable pipelines for handling textual data in multiple languages. This facilitates collaboration, transparency, and reproducibility. Unitxt allows for over 100,000 recipe configurations, facilitates integration of datasets, and serves as a crucial data backbone for large language models.
IBM AI Research Introduces Unitxt: An Innovative Library For Customizable Textual Data Preparation And Evaluation Tailored To Generative Language Models
IBM Research has developed Unitxt, a collaborative platform that simplifies the processing of unified textual data. With its new Python module, Unitxt offers practical solutions for handling textual data in multiple languages using configurable pipelines called recipes. These recipes allow users to load, preprocess, and evaluate model predictions, promoting reuse and collaboration.
Key Features of Unitxt:
- Modular and reusable recipes for handling textual data in various languages
- Over 100,000 recipe configurations to experiment with different datasets and formatting options
- Compatibility with existing code to eliminate the need for additional installations
- Seamless integration with HuggingFace datasets and other software sections
Value of Unitxt:
Unitxt simplifies the evaluation of language models across different languages, tasks, and prompt structures. It also facilitates the integration of diverse datasets, making it easier to train and evaluate large language models. By providing a shared foundation for data wrangling, Unitxt enables researchers to focus on developing secure, robust, and performant language models for various natural language processing activities.
Unitxt has already been used to train and evaluate big language models at IBM, and the team aims to see its adoption grow within the open-source community to accelerate progress in language model development.
For more information, you can access the Paper and check out the Github.
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