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W tym samouczku pokazano, jak wygenerować osadzenia z modułu TensorFlow Hub (TF-Hub) na podstawie danych wejściowych i zbudować przybliżony indeks najbliższych sąsiadów (ANN) przy użyciu wyodrębnionych osadów. Indeks można następnie wykorzystać do dopasowywania i wyszukiwania podobieństw w czasie rzeczywistym.
W przypadku dużego zbioru danych dokładne dopasowanie poprzez skanowanie całego repozytorium w celu znalezienia w czasie rzeczywistym elementów najbardziej podobnych do danego zapytania nie jest efektywne. Dlatego używamy przybliżonego algorytmu dopasowywania podobieństwa, który pozwala nam poświęcić odrobinę dokładności w znajdowaniu dokładnych dopasowań najbliższego sąsiada na rzecz znacznego zwiększenia szybkości.
W tym samouczku pokażemy przykład wyszukiwania tekstu w czasie rzeczywistym w korpusie nagłówków wiadomości, aby znaleźć nagłówki najbardziej podobne do zapytania. W przeciwieństwie do wyszukiwania słów kluczowych, wychwytuje to podobieństwo semantyczne zakodowane w osadzaniu tekstu.
Kroki tego samouczka są następujące:
- Pobierz przykładowe dane.
- Generuj osadzanie danych za pomocą modułu TF-Hub
- Zbuduj indeks ANN dla osadzania
- Użyj indeksu do dopasowania podobieństwa
Do generowania osadzania z modułu TF-Hub używamy Apache Beam . Korzystamy również z biblioteki ANNOY Spotify, aby zbudować przybliżony indeks najbliższych sąsiadów.
Więcej modeli
W przypadku modeli o tej samej architekturze, ale przeszkolonych w innym języku, zapoznaj się z tą kolekcją. Tutaj możesz znaleźć wszystkie osadzone teksty, które są obecnie hostowane na tfhub.dev .
Organizować coś
Zainstaluj wymagane biblioteki.
pip install -q apache_beampip install -q 'scikit_learn~=0.23.0' # For gaussian_random_matrix.pip install -q annoy
Zaimportuj wymagane biblioteki
import os
import sys
import pickle
from collections import namedtuple
from datetime import datetime
import numpy as np
import apache_beam as beam
from apache_beam.transforms import util
import tensorflow as tf
import tensorflow_hub as hub
import annoy
from sklearn.random_projection import gaussian_random_matrix
print('TF version: {}'.format(tf.__version__))
print('TF-Hub version: {}'.format(hub.__version__))
print('Apache Beam version: {}'.format(beam.__version__))
TF version: 2.4.0 TF-Hub version: 0.11.0 Apache Beam version: 2.26.0
1. Pobierz przykładowe dane
Zbiór danych Million News Headlines zawiera nagłówki wiadomości opublikowane w ciągu 15 lat i pochodzą od renomowanej firmy Australian Broadcasting Corp. (ABC). Ten zbiór wiadomości zawiera podsumowanie historycznych zapisów godnych uwagi wydarzeń na świecie od początku 2003 r. do końca 2017 r., ze szczególnym uwzględnieniem Australii.
Format : Dane w dwóch kolumnach oddzielonych tabulatorami: 1) data publikacji i 2) tekst nagłówka. Nas interesuje tylko tekst nagłówka.
wget 'https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true' -O raw.tsvwc -l raw.tsvhead raw.tsv
--2021-01-07 12:50:08-- https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true Resolving dataverse.harvard.edu (dataverse.harvard.edu)... 206.191.184.198 Connecting to dataverse.harvard.edu (dataverse.harvard.edu)|206.191.184.198|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 57600231 (55M) [text/tab-separated-values] Saving to: ‘raw.tsv’ raw.tsv 100%[===================>] 54.93M 14.7MB/s in 4.4s 2021-01-07 12:50:14 (12.4 MB/s) - ‘raw.tsv’ saved [57600231/57600231] 1103664 raw.tsv publish_date headline_text 20030219 "aba decides against community broadcasting licence" 20030219 "act fire witnesses must be aware of defamation" 20030219 "a g calls for infrastructure protection summit" 20030219 "air nz staff in aust strike for pay rise" 20030219 "air nz strike to affect australian travellers" 20030219 "ambitious olsson wins triple jump" 20030219 "antic delighted with record breaking barca" 20030219 "aussie qualifier stosur wastes four memphis match" 20030219 "aust addresses un security council over iraq"
Dla uproszczenia zachowujemy jedynie tekst nagłówka i usuwamy datę publikacji
!rm -r corpus
!mkdir corpus
with open('corpus/text.txt', 'w') as out_file:
with open('raw.tsv', 'r') as in_file:
for line in in_file:
headline = line.split('\t')[1].strip().strip('"')
out_file.write(headline+"\n")
rm: cannot remove 'corpus': No such file or directory
tail corpus/text.txtsevere storms forecast for nye in south east queensland snake catcher pleads for people not to kill reptiles south australia prepares for party to welcome new year strikers cool off the heat with big win in adelaide stunning images from the sydney to hobart yacht the ashes smiths warners near miss liven up boxing day test timelapse: brisbanes new year fireworks what 2017 meant to the kids of australia what the papodopoulos meeting may mean for ausus who is george papadopoulos the former trump campaign aide
2. Wygeneruj osadzanie danych.
W tym samouczku używamy modelu języka sieci neuronowej (NNLM) do generowania osadzania danych nagłówka. Osadzanie zdań można następnie łatwo wykorzystać do obliczenia podobieństwa znaczeń na poziomie zdań. Proces generowania osadzania prowadzimy przy użyciu Apache Beam.
Metoda ekstrakcji osadzającej
embed_fn = None
def generate_embeddings(text, module_url, random_projection_matrix=None):
# Beam will run this function in different processes that need to
# import hub and load embed_fn (if not previously loaded)
global embed_fn
if embed_fn is None:
embed_fn = hub.load(module_url)
embedding = embed_fn(text).numpy()
if random_projection_matrix is not None:
embedding = embedding.dot(random_projection_matrix)
return text, embedding
Konwertuj na metodę tf.Example
def to_tf_example(entries):
examples = []
text_list, embedding_list = entries
for i in range(len(text_list)):
text = text_list[i]
embedding = embedding_list[i]
features = {
'text': tf.train.Feature(
bytes_list=tf.train.BytesList(value=[text.encode('utf-8')])),
'embedding': tf.train.Feature(
float_list=tf.train.FloatList(value=embedding.tolist()))
}
example = tf.train.Example(
features=tf.train.Features(
feature=features)).SerializeToString(deterministic=True)
examples.append(example)
return examples
Rurociąg belkowy
def run_hub2emb(args):
'''Runs the embedding generation pipeline'''
options = beam.options.pipeline_options.PipelineOptions(**args)
args = namedtuple("options", args.keys())(*args.values())
with beam.Pipeline(args.runner, options=options) as pipeline:
(
pipeline
| 'Read sentences from files' >> beam.io.ReadFromText(
file_pattern=args.data_dir)
| 'Batch elements' >> util.BatchElements(
min_batch_size=args.batch_size, max_batch_size=args.batch_size)
| 'Generate embeddings' >> beam.Map(
generate_embeddings, args.module_url, args.random_projection_matrix)
| 'Encode to tf example' >> beam.FlatMap(to_tf_example)
| 'Write to TFRecords files' >> beam.io.WriteToTFRecord(
file_path_prefix='{}/emb'.format(args.output_dir),
file_name_suffix='.tfrecords')
)
Generowanie losowej macierzy wag projekcyjnych
Rzutowanie losowe to prosta, ale potężna technika stosowana w celu zmniejszenia wymiarowości zbioru punktów leżących w przestrzeni euklidesowej. Tło teoretyczne można znaleźć w lemacie Johnsona-Lindenstraussa .
Zmniejszenie wymiarowości osadzania za pomocą losowej projekcji oznacza mniej czasu potrzebnego na budowanie i sprawdzanie indeksu SSN.
W tym samouczku używamy losowej projekcji Gaussa z biblioteki Scikit-learn .
def generate_random_projection_weights(original_dim, projected_dim):
random_projection_matrix = None
random_projection_matrix = gaussian_random_matrix(
n_components=projected_dim, n_features=original_dim).T
print("A Gaussian random weight matrix was creates with shape of {}".format(random_projection_matrix.shape))
print('Storing random projection matrix to disk...')
with open('random_projection_matrix', 'wb') as handle:
pickle.dump(random_projection_matrix,
handle, protocol=pickle.HIGHEST_PROTOCOL)
return random_projection_matrix
Ustaw parametry
Jeśli chcesz zbudować indeks przy użyciu oryginalnej przestrzeni do osadzania bez losowej projekcji, ustaw parametr projected_dim na None . Należy pamiętać, że spowolni to etap indeksowania w przypadku osadzania wielowymiarowego.
module_url = 'https://tfhub.dev/google/nnlm-en-dim128/2'
projected_dim = 64
Uruchom potok
import tempfile
output_dir = tempfile.mkdtemp()
original_dim = hub.load(module_url)(['']).shape[1]
random_projection_matrix = None
if projected_dim:
random_projection_matrix = generate_random_projection_weights(
original_dim, projected_dim)
args = {
'job_name': 'hub2emb-{}'.format(datetime.utcnow().strftime('%y%m%d-%H%M%S')),
'runner': 'DirectRunner',
'batch_size': 1024,
'data_dir': 'corpus/*.txt',
'output_dir': output_dir,
'module_url': module_url,
'random_projection_matrix': random_projection_matrix,
}
print("Pipeline args are set.")
args
A Gaussian random weight matrix was creates with shape of (128, 64)
Storing random projection matrix to disk...
Pipeline args are set.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function gaussian_random_matrix is deprecated; gaussian_random_matrix is deprecated in 0.22 and will be removed in version 0.24.
warnings.warn(msg, category=FutureWarning)
{'job_name': 'hub2emb-210107-125029',
'runner': 'DirectRunner',
'batch_size': 1024,
'data_dir': 'corpus/*.txt',
'output_dir': '/tmp/tmp0g361gzp',
'module_url': 'https://tfhub.dev/google/nnlm-en-dim128/2',
'random_projection_matrix': array([[-0.1349755 , -0.12082699, 0.07092581, ..., -0.02680793,
-0.0459312 , -0.20462361],
[-0.06197901, 0.01832142, 0.21362496, ..., 0.06641898,
0.14553738, -0.117217 ],
[ 0.03452009, 0.14239163, 0.01371371, ..., 0.10422342,
0.02966668, -0.07094185],
...,
[ 0.03384223, 0.05102025, 0.01941788, ..., -0.07500625,
0.09584965, -0.08593636],
[ 0.11010087, -0.10597793, 0.06668758, ..., -0.0518654 ,
-0.14681441, 0.08449293],
[ 0.26909502, -0.0291555 , 0.04305639, ..., -0.02295843,
0.1164921 , -0.04828371]])}
print("Running pipeline...")
%time run_hub2emb(args)
print("Pipeline is done.")
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. Running pipeline... Warning:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac3599d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac3599d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac475598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac475598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. CPU times: user 9min 4s, sys: 10min 14s, total: 19min 19s Wall time: 2min 30s Pipeline is done.
ls {output_dir}emb-00000-of-00001.tfrecords
Przeczytaj niektóre wygenerowane osadzania...
embed_file = os.path.join(output_dir, 'emb-00000-of-00001.tfrecords')
sample = 5
# Create a description of the features.
feature_description = {
'text': tf.io.FixedLenFeature([], tf.string),
'embedding': tf.io.FixedLenFeature([projected_dim], tf.float32)
}
def _parse_example(example):
# Parse the input `tf.Example` proto using the dictionary above.
return tf.io.parse_single_example(example, feature_description)
dataset = tf.data.TFRecordDataset(embed_file)
for record in dataset.take(sample).map(_parse_example):
print("{}: {}".format(record['text'].numpy().decode('utf-8'), record['embedding'].numpy()[:10]))
headline_text: [ 0.07743962 -0.10065071 -0.03604915 0.03902601 0.02538098 -0.01991337 -0.11972483 0.03102058 0.16498186 -0.04299153] aba decides against community broadcasting licence: [ 0.02420221 -0.07736929 0.05655728 -0.18739551 0.11344934 0.12652674 -0.18189304 0.00422473 0.13149698 0.01910412] act fire witnesses must be aware of defamation: [-0.17413895 -0.05418579 0.07769868 0.05096476 0.08622053 0.33112594 0.04067763 0.00448784 0.15882017 0.33829722] a g calls for infrastructure protection summit: [ 0.16939437 -0.18585566 -0.14201084 -0.21779229 -0.1374832 0.14933842 -0.19583155 0.12921487 0.09811856 0.099967 ] air nz staff in aust strike for pay rise: [ 0.0230642 -0.03269081 0.18271443 0.23761444 -0.01575144 0.06109515 -0.01963143 -0.05211507 0.06050447 -0.20023327]
3. Zbuduj indeks SNN dla osadzania
ANNOY (Approximate Nearest Neighbours Oh Yeah) to biblioteka C++ z powiązaniami z Pythonem, służąca do wyszukiwania punktów w przestrzeni znajdujących się blisko danego punktu zapytania. Tworzy również duże struktury danych oparte na plikach tylko do odczytu, które są mapowane w pamięci. Jest zbudowany i używany przez Spotify do rekomendacji muzycznych. Jeśli jesteś zainteresowany, możesz grać razem z innymi alternatywami dla ANNOY, takimi jak NGT , FAISS itp.
def build_index(embedding_files_pattern, index_filename, vector_length,
metric='angular', num_trees=100):
'''Builds an ANNOY index'''
annoy_index = annoy.AnnoyIndex(vector_length, metric=metric)
# Mapping between the item and its identifier in the index
mapping = {}
embed_files = tf.io.gfile.glob(embedding_files_pattern)
num_files = len(embed_files)
print('Found {} embedding file(s).'.format(num_files))
item_counter = 0
for i, embed_file in enumerate(embed_files):
print('Loading embeddings in file {} of {}...'.format(i+1, num_files))
dataset = tf.data.TFRecordDataset(embed_file)
for record in dataset.map(_parse_example):
text = record['text'].numpy().decode("utf-8")
embedding = record['embedding'].numpy()
mapping[item_counter] = text
annoy_index.add_item(item_counter, embedding)
item_counter += 1
if item_counter % 100000 == 0:
print('{} items loaded to the index'.format(item_counter))
print('A total of {} items added to the index'.format(item_counter))
print('Building the index with {} trees...'.format(num_trees))
annoy_index.build(n_trees=num_trees)
print('Index is successfully built.')
print('Saving index to disk...')
annoy_index.save(index_filename)
print('Index is saved to disk.')
print("Index file size: {} GB".format(
round(os.path.getsize(index_filename) / float(1024 ** 3), 2)))
annoy_index.unload()
print('Saving mapping to disk...')
with open(index_filename + '.mapping', 'wb') as handle:
pickle.dump(mapping, handle, protocol=pickle.HIGHEST_PROTOCOL)
print('Mapping is saved to disk.')
print("Mapping file size: {} MB".format(
round(os.path.getsize(index_filename + '.mapping') / float(1024 ** 2), 2)))
embedding_files = "{}/emb-*.tfrecords".format(output_dir)
embedding_dimension = projected_dim
index_filename = "index"
!rm {index_filename}
!rm {index_filename}.mapping
%time build_index(embedding_files, index_filename, embedding_dimension)
rm: cannot remove 'index': No such file or directory rm: cannot remove 'index.mapping': No such file or directory Found 1 embedding file(s). Loading embeddings in file 1 of 1... 100000 items loaded to the index 200000 items loaded to the index 300000 items loaded to the index 400000 items loaded to the index 500000 items loaded to the index 600000 items loaded to the index 700000 items loaded to the index 800000 items loaded to the index 900000 items loaded to the index 1000000 items loaded to the index 1100000 items loaded to the index A total of 1103664 items added to the index Building the index with 100 trees... Index is successfully built. Saving index to disk... Index is saved to disk. Index file size: 1.61 GB Saving mapping to disk... Mapping is saved to disk. Mapping file size: 50.61 MB CPU times: user 9min 54s, sys: 53.9 s, total: 10min 48s Wall time: 5min 5s
lscorpus random_projection_matrix index raw.tsv index.mapping tf2_semantic_approximate_nearest_neighbors.ipynb
4. Użyj indeksu do dopasowywania podobieństw
Teraz możemy użyć indeksu ANN, aby znaleźć nagłówki wiadomości, które są semantycznie zbliżone do zapytania wejściowego.
Załaduj indeks i pliki mapowania
index = annoy.AnnoyIndex(embedding_dimension)
index.load(index_filename, prefault=True)
print('Annoy index is loaded.')
with open(index_filename + '.mapping', 'rb') as handle:
mapping = pickle.load(handle)
print('Mapping file is loaded.')
Annoy index is loaded. /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: The default argument for metric will be removed in future version of Annoy. Please pass metric='angular' explicitly. """Entry point for launching an IPython kernel. Mapping file is loaded.
Metoda dopasowywania podobieństw
def find_similar_items(embedding, num_matches=5):
'''Finds similar items to a given embedding in the ANN index'''
ids = index.get_nns_by_vector(
embedding, num_matches, search_k=-1, include_distances=False)
items = [mapping[i] for i in ids]
return items
Wyodrębnij osadzanie z danego zapytania
# Load the TF-Hub module
print("Loading the TF-Hub module...")
%time embed_fn = hub.load(module_url)
print("TF-Hub module is loaded.")
random_projection_matrix = None
if os.path.exists('random_projection_matrix'):
print("Loading random projection matrix...")
with open('random_projection_matrix', 'rb') as handle:
random_projection_matrix = pickle.load(handle)
print('random projection matrix is loaded.')
def extract_embeddings(query):
'''Generates the embedding for the query'''
query_embedding = embed_fn([query])[0].numpy()
if random_projection_matrix is not None:
query_embedding = query_embedding.dot(random_projection_matrix)
return query_embedding
Loading the TF-Hub module... CPU times: user 757 ms, sys: 619 ms, total: 1.38 s Wall time: 1.37 s TF-Hub module is loaded. Loading random projection matrix... random projection matrix is loaded.
extract_embeddings("Hello Machine Learning!")[:10]
array([ 0.12164804, 0.0162079 , -0.15466002, -0.14580576, 0.03926325,
-0.10124508, -0.1333948 , 0.0515029 , -0.14688903, -0.09971556])
Wprowadź zapytanie, aby znaleźć najbardziej podobne elementy
query = "confronting global challenges"
print("Generating embedding for the query...")
%time query_embedding = extract_embeddings(query)
print("")
print("Finding relevant items in the index...")
%time items = find_similar_items(query_embedding, 10)
print("")
print("Results:")
print("=========")
for item in items:
print(item)
Generating embedding for the query... CPU times: user 5.18 ms, sys: 596 µs, total: 5.77 ms Wall time: 2.19 ms Finding relevant items in the index... CPU times: user 555 µs, sys: 327 µs, total: 882 µs Wall time: 601 µs Results: ========= confronting global challenges emerging nations to help struggling global economy g7 warns of increasing global economic crisis world struggling to cope with global terrorism companies health to struggle amid global crisis external risks biggest threat to economy asian giants unite to tackle global crisis g7 ministers warn of slowing global growth experts to discuss global warming threat scientists warn of growing natural disasters
Chcesz dowiedzieć się więcej?
Możesz dowiedzieć się więcej o TensorFlow na tensorflow.org i zapoznać się z dokumentacją API TF-Hub na tensorflow.org/hub . Znajdź dostępne moduły TensorFlow Hub na tfhub.dev, w tym więcej modułów do osadzania tekstu i modułów wektorów funkcji obrazu.
Zapoznaj się także z przyspieszonym kursem uczenia maszynowego , który stanowi szybkie, praktyczne wprowadzenie Google do uczenia maszynowego.