seujung/gluonnlp_tutorial

GluonNLP tutorial for Pycon2019

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This tutorial helps data scientists and machine learning engineers learn to apply deep learning techniques to natural language processing (NLP) tasks. It provides practical examples using the GluonNLP library, showing how to build models for tasks like classifying text intent and recognizing entities within text. You'll go from basic deep learning concepts to implementing advanced models like BERT.

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Use this if you are a data scientist or machine learning engineer looking for hands-on experience and code examples to understand and implement deep learning solutions for NLP problems using the GluonNLP framework.

Not ideal if you are looking for a high-level, no-code solution for NLP, or if you are not comfortable with programming and machine learning concepts.

natural-language-processing deep-learning text-classification entity-recognition machine-learning-engineering
No License Stale 6m No Package No Dependents
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Adoption 5 / 25
Maturity 8 / 25
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Jupyter Notebook

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Last pushed

Aug 16, 2019

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