Forked from georgwiese/universal_sentence_encoder_featurizer.py
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October 12, 2019 22:29
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Universal Sentence Encoder Featurizer
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from rasa_nlu.featurizers import Featurizer | |
import tensorflow_hub as hub | |
import tensorflow as tf | |
class UniversalSentenceEncoderFeaturizer(Featurizer): | |
"""Appends a universal sentence encoding to the message's text_features.""" | |
# URL of the TensorFlow Hub Module | |
TFHUB_URL = "https://tfhub.dev/google/universal-sentence-encoder/2" | |
name = "universal_sentence_encoder_featurizer" | |
# We don't require any previous pipline step and return text_features | |
requires = [] | |
provides = ["text_features"] | |
def __init__(self, component_config): | |
super(UniversalSentenceEncoderFeaturizer, self).__init__(component_config) | |
# Load the TensorFlow Hub Module with pre-trained weights | |
sentence_encoder = hub.Module(self.TFHUB_URL) | |
# Create a TensorFlow placeholder for the input string | |
self.input_string = tf.placeholder(tf.string, shape=[None]) | |
# Invoke `sentence_encoder` in order to create the encoding tensor | |
self.encoding = sentence_encoder(self.input_string) | |
# Create a TensorFlow Session and run initializers | |
self.session = tf.Session() | |
self.session.run([tf.global_variables_initializer(), | |
tf.tables_initializer()]) | |
def train(self, training_data, config, **kwargs): | |
# Nothing to train, just process all training examples so that the | |
# feature is set for future pipeline steps | |
for example in training_data.training_examples: | |
self.process(example) | |
def process(self, message, **kwargs): | |
# Get the sentence encoding by feeding the message text and computing | |
# the encoding tensor. | |
feature_vector = self.session.run(self.encoding, | |
{self.input_string: [message.text]})[0] | |
# Concatenate the feature vector with any existing text features | |
features = self._combine_with_existing_text_features(message, feature_vector) | |
# Set the feature, overwriting any existing `text_features` | |
message.set("text_features", features) |
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