April 2022
Beginner to intermediate
200 pages
4h 17m
English
Content preview from Natural Language Processing with Flair
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Chapter 7: Train Your Own Embeddings
The reason Flair sequence taggers yield such outstanding performance can mainly be attributed to its secret sauce – Flair embeddings. Their contextual design, the fact that they are character-based, and the way they can be used in the backward-forward configuration make them a perfect fit for use in sequence labeling tasks. But, up until this point in this book, we haven't focused much on how these embeddings are actually trained.
In the previous chapter, where we covered model training and learned about word embeddings, we simply used the pre-trained embeddings that were available as part of the Flair Python package. But there are many Natural Language Processing (NLP) problems we may stumble upon where ...
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