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Ten Colab ilustruje, jak używać Universal Sentence Encoder-Lite do zadania podobieństwa zdań. Moduł ten jest bardzo podobny do uniwersalnego Zdanie Encoder z tą tylko różnicą, że trzeba uruchomić SentencePiece przetwarzanie Twoich zdaniach wejściowych.
Universal Sentence Encoder sprawia, że uzyskiwanie osadzeń na poziomie zdań jest tak łatwe, jak dawniej wyszukiwanie osadzeń dla poszczególnych słów. Osadzania zdań można następnie w trywialny sposób wykorzystać do obliczenia podobieństwa na poziomie zdania, a także w celu umożliwienia lepszej wydajności w dalszych zadaniach klasyfikacyjnych przy użyciu mniej nadzorowanych danych treningowych.
Rozpoczęcie pracy
Ustawiać
# Install seaborn for pretty visualizationspip3 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
Załaduj moduł z TF-Hub
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))
INFO:tensorflow:Saver not created because there are no variables in the graph to restore INFO:tensorflow:Saver not created because there are no variables in the graph to restore
Załaduj model SentencePiece z modułu TF-Hub
Model SentencePiece jest wygodnie przechowywany w zasobach modułu. Musi zostać załadowany w celu zainicjowania procesora.
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))
INFO:tensorflow:Saver not created because there are no variables in the graph to restore INFO:tensorflow:Saver not created because there are no variables in the graph to restore 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)
Przetestuj moduł na kilku przykładach
# 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, ...]
Przykład zadania semantycznego podobieństwa tekstu (STS)
Osadzenia tworzone przez Universal Sentence Encoder są w przybliżeniu znormalizowane. Podobieństwo semantyczne dwóch zdań można w trywialny sposób obliczyć jako produkt wewnętrzny kodowań.
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)
Wizualizacja podobieństwa
Tutaj pokazujemy podobieństwo na mapie cieplnej. Końcowa wykres jest macierz 9x9, gdzie każdy wpis [i, j] jest zabarwiona na podstawie wewnętrznego produktu z kodowaniem dla zdaniu i i 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)
Ocena: test porównawczy STS (Semantyczne podobieństwo tekstu)
Benchmark STS dostarcza intristic ocenę, w jakim stopniu wyniki obliczone z wykorzystaniem podobieństwa zdanie zanurzeń align z orzeczeń człowieka. Benchmark wymaga, aby systemy zwracały wyniki podobieństwa dla zróżnicowanego doboru par zdań. Pearson korelacji są następnie wykorzystywane do oceny jakości wyników maszyna podobieństwa wobec orzeczeń człowieka.
Pobierz dane
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
Zbuduj wykres oceny
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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Oceń osadzania zdań
Wybierz zbiór danych do testu porównawczego
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 = 0.7856484874001958 p-value = 1.065794746e-314