Natural Language Processing with Transformers

My notes from the book Natural Language Processing with Transformers: Building Language Applications with Hugging Face by Lewis Tunstall, Leandro von Werra, and Thomas Wolf.
Published

March 30, 2022

Modified

February 18, 2024

Notes on Transformers Book Ch. 1

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Chapter 1 covers essential advancements for transformers, recurrent architectures, the encoder-decoder framework, attention mechanisms, transfer learning in NLP, and the HuggingFace ecosystem.
Mar 30, 2022
19 min

Notes on Transformers Book Ch. 2

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Chapter 2 covers training a model to classify emotions expressed in Twitter messages.
Apr 1, 2022
79 min

Notes on Transformers Book Ch. 3

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Chapter 3 covers the Transformer architecture and different types of transformer models available on the Hugging Face Hub.
Apr 6, 2022
30 min

Notes on Transformers Book Ch. 4

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Chapter 4 covers fine-tuning a multilingual transformer model to perform named entity recognition.
Apr 7, 2022
105 min

Notes on Transformers Book Ch. 5

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Chapter 5 covers different methods for generating text with GPT-2.
Apr 8, 2022
23 min

Notes on Transformers Book Ch. 6

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Chapter 6 covers building an encoder-decoder model to condense dialogues between several people into a crisp summary.
Apr 10, 2022
44 min

Notes on Transformers Book Ch. 7

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Chapter 7 covers building a question-answering model that finds answers to questions in customer reviews.
Apr 12, 2022
72 min

Notes on Transformers Book Ch. 8

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Chapter 8 covers different methods to make transformer models more efficient in production.
Apr 14, 2022
62 min

Notes on Transformers Book Ch. 9

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Chapter 9 covers how to deal with few to no labels by training a model that automatically tags GitHub issues for the Hugging Face Transformers library.
Apr 22, 2022
95 min

Notes on Transformers Book Ch. 10

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Chapter 10 covers how to train a GPT-like model to generate Python source code from scratch.
Apr 25, 2022
71 min

Notes on Transformers Book Ch. 11

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Chapter 11 explores scaling up transformers, methods to make self-attention more efficient, and multimodel transformers.
Apr 26, 2022
30 min
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