Evrensel Cümle Kodlayıcı-Lite demosu

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın GitHub'da görüntüle Not defterini indir TF Hub modeline bakın

Bu İşbirliği, cümle benzerliği görevi için Evrensel Cümle Kodlayıcı-Lite'ın nasıl kullanılacağını gösterir. Bu modül çok benzer Evrensel Cümle Encoder çalıştırmak için gereken tek farkla SentencePiece girişinizi cümlelerin üzerine işlenmesini.

Evrensel Cümle Kodlayıcı, cümle düzeyinde gömmeler almayı, tek tek kelimeler için gömmelere bakmak için geçmişte olduğu kadar kolay hale getirir. Cümle yerleştirmeleri, daha sonra, daha az denetimli eğitim verisi kullanarak aşağı akış sınıflandırma görevlerinde daha iyi performans sağlamak için cümle düzeyinde anlam benzerliğini hesaplamak için önemsiz bir şekilde kullanılabilir.

Başlarken

Kurmak

# 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/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:111: 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

Modülü TF-Hub'dan yükleyin

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-Hub Modülünden SentencePiece modelini yükleyin

SentencePiece modeli, modülün varlıkları içinde uygun şekilde saklanır. İşlemciyi başlatmak için yüklenmesi gerekir.

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

sp = spm.SentencePieceProcessor()
with tf.io.gfile.GFile(spm_path, mode="rb") as f:
  sp.LoadFromSerializedProto(f.read())
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)

Modülü birkaç örnekle test edin

# 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, -0.052356015890836716, ...]

Message: I am a sentence for which I would like to get its embedding.
Embedding size: 512
Embedding: [0.03533298149704933, -0.04714975506067276, 0.012305550277233124, ...]

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: [-0.004081667400896549, -0.08954868465662003, 0.03737196698784828, ...]

Anlamsal Metin Benzerliği (STS) görev örneği

Evrensel Cümle Kodlayıcı tarafından üretilen yerleştirmeler yaklaşık olarak normalleştirilir. İki cümlenin anlamsal benzerliği, kodlamaların iç çarpımı olarak önemsiz bir şekilde hesaplanabilir.

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)

benzerlik görselleştirildi

Burada benzerliği bir ısı haritasında gösteriyoruz. Nihai grafik her giriş, bir 9x9 matristir [i, j] cümle için kodlama iç ürüne göre renkli i ve 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?",
]


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

png

Değerlendirme: STS (Semantik Metinsel Benzerlik) Benchmark

STS Deney benzerlik skorları insan yargılarla cümle kalıplamaların hizalama kullanılarak hesaplanmaktadır ettiği bir derece bir intristic değerlendirilmesini sağlar. Karşılaştırma, sistemlerin çeşitli cümle çiftleri seçimi için benzerlik puanları döndürmesini gerektirir. Pearson korelasyon daha sonra insan yargılar karşı makine benzerlik puanları kalitesini değerlendirmek için kullanılır.

Verileri indir

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()

Downloading data from http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz
417792/409630 [==============================] - 2s 5us/step
425984/409630 [===============================] - 2s 5us/step

Değerlendirme grafiği oluştur

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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Cümle yerleştirmelerini değerlendirin

Karşılaştırma için veri kümesi seçin

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 = 0.7856484874001958
p-value = 1.065794746e-314