UniversalSentenceEncoder

The Universal Sentence Encoder makes getting sentence level embeddings as easy as it has historically been to lookup the embeddings for individual words.

Setup

This section sets up the environment for access to the Universal Sentence Encoder on TF Hub and provides examples of applying the encoder to words, sentences, and paragraphs.

%%capture
!pip3 install seaborn

More detailed information about installing Tensorflow can be found at https://www.tensorflow.org/install/.

from absl import logging

import tensorflow as tf

import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns

module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print ("module %s loaded" % module_url)
def embed(input):
  return model(input)

Compute a representation for each message, showing various lengths supported:

word = "Elephant"
sentence = "I am a sentence for which I would like to get its embedding."
paragraph = (
    "Universal Sentence Encoder embeddings also support short paragraphs. "
    "There is no hard limit on how long the paragraph is. Roughly, the longer "
    "the more 'diluted' the embedding will be.")
messages = [word, sentence, paragraph]

# Reduce logging output.
logging.set_verbosity(logging.ERROR)

message_embeddings = embed(messages)

for i, message_embedding in enumerate(np.array(message_embeddings).tolist()):
  print("Message: {}".format(messages[i]))
  print("Embedding size: {}".format(len(message_embedding)))
  message_embedding_snippet = ", ".join(
      (str(x) for x in message_embedding[:3]))
  print("Embedding: [{}, ...]\n".format(message_embedding_snippet))

Semantic Textual Similarity Task Example:

The embeddings produced by the Universal Sentence Encoder are approximately normalized. The semantic similarity of two sentences can be trivially computed as the inner product of the encodings.

def plot_similarity(labels, features, rotation):
  corr = np.inner(features, features)
  sns.set(font_scale=1.2)
  g = sns.heatmap(
      corr,
      xticklabels=labels,
      yticklabels=labels,
      vmin=0,
      vmax=1,
      cmap="YlOrRd")
  g.set_xticklabels(labels, rotation=rotation)
  g.set_title("Semantic Textual Similarity")

def run_and_plot(messages_):
  message_embeddings_ = embed(messages_)
  plot_similarity(messages_, message_embeddings_, 90)

Similarity Visualized

Here we show the similarity in a heat map. The final graph is a 9x9 matrix where each entry [i, j] is colored based on the inner product of the encodings for sentence i and j.

messages = [
    # Smartphones
    "I like my phone",
    "My phone is not good.",
    "Your cellphone looks great.",

    # Weather
    "Will it snow tomorrow?",
    "Recently a lot of hurricanes have hit the US",
    "Global warming is real",

    # Food and health
    "An apple a day, keeps the doctors away",
    "Eating strawberries is healthy",
    "Is paleo better than keto?",

    # Asking about age
    "How old are you?",
    "what is your age?",
]

run_and_plot(messages)