bhadreshpsavani/UnderstandingNLP
Natural Language Processing Analysis
This is a collection of notebooks and resources for learning about Natural Language Processing (NLP). It provides practical tips for working with large language models, parallel model training, and techniques like stratified K-fold sampling for multilabel classification. It's intended for data scientists, machine learning engineers, and researchers who want to understand and implement NLP methods.
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Use this if you are a machine learning practitioner looking for practical tips, code examples, and curated learning resources to deepen your understanding and implementation of Natural Language Processing techniques.
Not ideal if you are a non-technical user seeking a ready-to-use tool for immediate NLP tasks without needing to write code or understand underlying algorithms.
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Oct 07, 2023
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