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Universal SentenceEncoder-Liteデモ

TensorFlow.orgで表示 GoogleColabで実行 GitHubで表示ノートブックをダウンロードTFハブモデルを参照してください

このColabは、Universal SentenceEncoder-Liteを文の類似性タスクに使用する方法を示しています。このモジュールはUniversalSentence Encoderと非常に似ていますが、入力文に対してSentencePiece処理を実行する必要がある点が異なります。

Universal Sentence Encoderを使用すると、これまで個々の単語の埋め込みを検索するのと同じくらい簡単に、文レベルの埋め込みを取得できます。次に、文の埋め込みを簡単に使用して、文レベルの意味の類似性を計算したり、監視の少ないトレーニングデータを使用してダウンストリームの分類タスクのパフォーマンスを向上させたりできます。

入門

セットアップ

# Install seaborn for pretty visualizations
pip3 install --quiet seaborn
# Install SentencePiece package
# SentencePiece package is needed for Universal Sentence Encoder Lite. We'll
# use it for all the text processing and sentence feature ID lookup.
pip3 install --quiet sentencepiece
from absl import logging

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

import tensorflow_hub as hub
import sentencepiece as spm
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term

TFハブからモジュールをロードします

module = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-lite/2")
input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
encodings = module(
    inputs=dict(
        values=input_placeholder.values,
        indices=input_placeholder.indices,
        dense_shape=input_placeholder.dense_shape))
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TFハブモジュールからSentencePieceモデルをロードします

SentencePieceモデルは、モジュールのアセット内に便利に保存されます。プロセッサを初期化するためにロードする必要があります。

with tf.Session() as sess:
  spm_path = sess.run(module(signature="spm_path"))

sp = spm.SentencePieceProcessor()
sp.Load(spm_path)
print("SentencePiece model loaded at {}.".format(spm_path))
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SentencePiece model loaded at b'/tmp/tfhub_modules/539544f0a997d91c327c23285ea00c37588d92cc/assets/universal_encoder_8k_spm.model'.

def process_to_IDs_in_sparse_format(sp, sentences):
  # An utility method that processes sentences with the sentence piece processor
  # 'sp' and returns the results in tf.SparseTensor-similar format:
  # (values, indices, dense_shape)
  ids = [sp.EncodeAsIds(x) for x in sentences]
  max_len = max(len(x) for x in ids)
  dense_shape=(len(ids), max_len)
  values=[item for sublist in ids for item in sublist]
  indices=[[row,col] for row in range(len(ids)) for col in range(len(ids[row]))]
  return (values, indices, dense_shape)

いくつかの例でモジュールをテストします

# 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]

values, indices, dense_shape = process_to_IDs_in_sparse_format(sp, messages)

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

with tf.Session() as session:
  session.run([tf.global_variables_initializer(), tf.tables_initializer()])
  message_embeddings = session.run(
      encodings,
      feed_dict={input_placeholder.values: values,
                input_placeholder.indices: indices,
                input_placeholder.dense_shape: dense_shape})

  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))
Message: Elephant
Embedding size: 512
Embedding: [0.053387489169836044, 0.053194381296634674, -0.05235602334141731, ...]

Message: I am a sentence for which I would like to get its embedding.
Embedding size: 512
Embedding: [0.03533290699124336, -0.04714979603886604, 0.01230555959045887, ...]

Message: 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.
Embedding size: 512
Embedding: [-0.0040816813707351685, -0.08954869210720062, 0.03737198933959007, ...]


セマンティックテキスト類似性(STS)タスクの例

Universal Sentence Encoderによって生成された埋め込みは、ほぼ正規化されています。 2つの文の意味上の類似性は、エンコーディングの内部積として簡単に計算できます。

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(session, input_placeholder, messages):
  values, indices, dense_shape = process_to_IDs_in_sparse_format(sp,messages)

  message_embeddings = session.run(
      encodings,
      feed_dict={input_placeholder.values: values,
                input_placeholder.indices: indices,
                input_placeholder.dense_shape: dense_shape})
  
  plot_similarity(messages, message_embeddings, 90)

視覚化された類似性

ここでは、ヒートマップで類似性を示します。最終的なグラフは9x9のマトリックスで、各エントリ[i, j]は、文ijエンコーディングの内部積に基づいて色付けされています。

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?",
]


with tf.Session() as session:
  session.run(tf.global_variables_initializer())
  session.run(tf.tables_initializer())
  run_and_plot(session, input_placeholder, messages)

png

評価:STS(セマンティックテキストの類似性)ベンチマーク

STSベンチマークは、文の埋め込みを使用して計算された類似性スコアが人間の判断と一致する程度の直感的な評価を提供します。ベンチマークでは、システムがさまざまな文のペアの選択に対して類似性スコアを返す必要があります。次に、ピアソン相関を使用して、人間の判断に対するマシン類似性スコアの品質を評価します。

データのダウンロード

import pandas
import scipy
import math


def load_sts_dataset(filename):
  # Loads a subset of the STS dataset into a DataFrame. In particular both
  # sentences and their human rated similarity score.
  sent_pairs = []
  with tf.gfile.GFile(filename, "r") as f:
    for line in f:
      ts = line.strip().split("\t")
      # (sent_1, sent_2, similarity_score)
      sent_pairs.append((ts[5], ts[6], float(ts[4])))
  return pandas.DataFrame(sent_pairs, columns=["sent_1", "sent_2", "sim"])


def download_and_load_sts_data():
  sts_dataset = tf.keras.utils.get_file(
      fname="Stsbenchmark.tar.gz",
      origin="http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz",
      extract=True)

  sts_dev = load_sts_dataset(
      os.path.join(os.path.dirname(sts_dataset), "stsbenchmark", "sts-dev.csv"))
  sts_test = load_sts_dataset(
      os.path.join(
          os.path.dirname(sts_dataset), "stsbenchmark", "sts-test.csv"))

  return sts_dev, sts_test


sts_dev, sts_test = download_and_load_sts_data()

評価グラフを作成する

sts_input1 = tf.sparse_placeholder(tf.int64, shape=(None, None))
sts_input2 = tf.sparse_placeholder(tf.int64, shape=(None, None))

# For evaluation we use exactly normalized rather than
# approximately normalized.
sts_encode1 = tf.nn.l2_normalize(
    module(
        inputs=dict(values=sts_input1.values,
                    indices=sts_input1.indices,
                    dense_shape=sts_input1.dense_shape)),
    axis=1)
sts_encode2 = tf.nn.l2_normalize(
    module(
        inputs=dict(values=sts_input2.values,
                    indices=sts_input2.indices,
                    dense_shape=sts_input2.dense_shape)),
    axis=1)

sim_scores = -tf.acos(tf.reduce_sum(tf.multiply(sts_encode1, sts_encode2), axis=1))

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文の埋め込みを評価する


dataset = sts_dev 

values1, indices1, dense_shape1 = process_to_IDs_in_sparse_format(sp, dataset['sent_1'].tolist())
values2, indices2, dense_shape2 = process_to_IDs_in_sparse_format(sp, dataset['sent_2'].tolist())
similarity_scores = dataset['sim'].tolist()
def run_sts_benchmark(session):
  """Returns the similarity scores"""
  scores = session.run(
      sim_scores,
      feed_dict={
          sts_input1.values: values1,
          sts_input1.indices:  indices1,
          sts_input1.dense_shape:  dense_shape1,
          sts_input2.values:  values2,
          sts_input2.indices:  indices2,
          sts_input2.dense_shape:  dense_shape2,
      })
  return scores


with tf.Session() as session:
  session.run(tf.global_variables_initializer())
  session.run(tf.tables_initializer())
  scores = run_sts_benchmark(session)

pearson_correlation = scipy.stats.pearsonr(scores, similarity_scores)
print('Pearson correlation coefficient = {0}\np-value = {1}'.format(
    pearson_correlation[0], pearson_correlation[1]))
Pearson correlation coefficient = 0.7856484814669641
p-value = 1.0658141986e-314