How to do it...

Let's code up the approach we defined previously, as follows (the code file is available as Intent_and_entity_extraction.ipynb in GitHub):

  1. Import the datasets, as shown in the following code:

Load the training dataset:

import numpy as np import pandas as pdimport pickleDATA_DIR="/content"def load_ds(fname='atis.train.pkl'):     with open(fname, 'rb') as stream:     ds,dicts = pickle.load(stream)     print('Done loading: ', fname)     print(' samples: {:4d}'.format(len(ds['query'])))     print(' vocab_size: {:4d}'.format(len(dicts['token_ids'])))     print(' slot count: {:4d}'.format(len(dicts['slot_ids'])))     print(' intent count: {:4d}'.format(len(dicts['intent_ids']))) return ...

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