Natural Language Processing With Transformers In Python

Description :

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Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again. In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI’s BERT, or Facebook AI’s DPR. We cover several key NLP frameworks including: HuggingFace’s Transformers TensorFlow 2 PyTorch spaCy NLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases: Language classification/sentiment analysis Named entity recognition (NER) Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application. All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as: History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!