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Using & Expanding the NLP Models Hub


The NLP Models Hub which powers the Spark NLP and NLU libraries takes a different approach than the hubs of other libraries like TensorFlow, PyTorch, and Hugging Face. While it also provides an easy-to-use interface to find, understand, and reuse pre-trained models, it focuses on providing production-grade state-of-the-art models for each NLP task instead of a comprehensive archive.

This implies a higher quality bar for accepting community contributions to the NLP Models Hub - in terms of automated testing, level of documentation, and transparency of accuracy metrics and training datasets. This webinar shows how you can make the most of it, whether you're looking to easily reuse models or contribute new ones.


Presented by Dia Trambitas

Dia Trambitas is a computer scientist with a rich background in Natural Language Processing. She has a Ph.D. in Semantic Web from the University of Grenoble, France, where she worked on ways of describing spatial and temporal data using OWL ontologies and reasoning based on semantic annotations. She then changed her interest to text processing and data extraction from unstructured documents, a subject she has been working on for the last 10 years. She has a rich experience working with different annotation tools and leading document classification and NER extraction projects in verticals such as Finance, Investment, Banking, and Healthcare.