In the previous recipe, we did not use any embeddings, such as Global Vectors for Word Representation (GloVe) or Word2vec; we will now use pre-trained word embeddings from Keras. Let's reuse the documents and labels from the preceding recipe. The code is as follows:
# define documents documents = ['Well done!', 'Good work', 'Great effort', 'nice work', 'Excellent!', 'Weak', 'Poor effort!', 'not good', 'poor work', 'Could have done better.'] # define class labels labels = array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
Keras provides tokenizer APIs for preparing text that can be fit and reused to prepare multiple text documents. A tokenizer is constructed and then fit onto text documents or integer encoded text documents. Here, words ...
