10 Natural language processing with TensorFlow: Language modeling
This chapter covers
- Implementing an NLP data pipeline with TensorFlow
- Implementing a GRU-based language model
- Using a perplexity metric for evaluating language models
- Defining an inference model to generate new text from the trained model
- Implementing beam search to uplift the quality of generated text
In the last chapter, we discussed an important NLP task called sentiment analysis. In that chapter, you used a data set of video game reviews and trained a model to predict whether a review carried a negative or positive sentiment by analyzing the text. You learned about various preprocessing steps that you can perform to improve the quality of the text, such as removing stop words ...
Get TensorFlow in Action now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
