April 2020
Beginner to intermediate
216 pages
5h 23m
English
Content preview from Natural Language Processing with Python and spaCy
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10TRAINING MODELS
As you learned in Chapter 1, spaCy contains statistical neural network models trained to perform named entity recognition, part-of-speech tagging, syntactic dependency parsing, and semantic similarity prediction. But you’re not limited to using only pretrained, ready-to-use models. You can also train a model with your own training examples, tuning its pipeline components for your application’s requirements.
This chapter covers how to train spaCy’s named entity recognizer and dependency parser, the pipeline components that you most often need to customize to make the model you’re using specific to a particular use case. The reason ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.