# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Utilities for generating the C4 dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import gzip import hashlib import io import re import threading from absl import logging import tensorflow.compat.v2 as tf import tensorflow_datasets.public_api as tfds # WET file constants _PAGE_DELIMITER = "WARC/1.0" _URL_KEY = "WARC-Target-URI:" _URL_DATE = "WARC-Date:" _CONTENT_TYPE = "Content-Type:" _CONTENT_LEN = "Content-Length:" _METADATA_PREFIXES = ("WARC", "CONTENT-", "Content-") # Filters _MIN_WORDS_PER_LINE = 5 _MIN_NUM_SENTENCES = 3 _MAX_WORD_LENGTH = 1000 _END_MARKS = (".", "?", "!", "\"") _ELLIPSIS = "..." _POLICY_SUBSTRINGS = [ "terms of use", "privacy policy", "cookie policy", "uses cookies", "use of cookies", "use cookies"] # Memoized sentence tokenizer. _SENTENCE_TOKENIZER = None def get_counter_inc_fn(namespace): def counter_inc_fn(counter, amt=1): tfds.core.lazy_imports.apache_beam.metrics.Metrics.counter( namespace, counter).inc(amt) return counter_inc_fn def get_hashed_url_filter_fn(predicate_fn): def filter_fn(el): url, _ = el val = int( hashlib.md5(tf.compat.as_text(url).encode("utf-8")).hexdigest(), 16) return predicate_fn(val) return filter_fn def _load_sentence_tokenizer(): """Returns a sentence tokenization function.""" nltk = tfds.core.lazy_imports.nltk # Lock to avoid a race-condition in the creation of the download directory. with threading.Lock(): nltk.download("punkt") return nltk.data.load("nltk:tokenizers/punkt/english.pickle") def _get_sentences(text): global _SENTENCE_TOKENIZER if not _SENTENCE_TOKENIZER: _SENTENCE_TOKENIZER = _load_sentence_tokenizer() return list(_SENTENCE_TOKENIZER.tokenize(tf.compat.as_text(text))) def _get_sentences_by_line(text, lower=False): sentences = [] for line in text.splitlines(): sentences.append([ s.lower() if lower else s for s in _get_sentences(line) ]) return sentences def is_language(page, language, min_probability=0.99): """Returns True iff text is in `language` with at least `min_probability`.""" unused_url, features = page text = features["text"] counter_inc_fn = get_counter_inc_fn("detected-lang") langdetect = tfds.core.lazy_imports.langdetect # Make langdetect predictions deterministic. langdetect.DetectorFactory.seed = 0 try: predictions = langdetect.detect_langs(text) except langdetect.lang_detect_exception.LangDetectException: counter_inc_fn("langdetect-exception") return False if not predictions: counter_inc_fn("page-filtered-nolangpredictions") return False best_prediction = predictions[0] if best_prediction.prob < min_probability: counter_inc_fn("page-filtered-lowlangdetectconf") return False if best_prediction.lang != language: counter_inc_fn("page-filtered-ignoredlang") counter_inc_fn("page-filtered-ignoredlang-%s" % (best_prediction.lang)) return False counter_inc_fn("page-emited-%s" % best_prediction.lang) return True def get_clean_page_fn(badwords=None): """Returns `clean_page` with pre-compiled badword and citation regexes.""" # Used to filter citation from Wikipedia pages (among others). citation_regex = re.compile(r"\[\d*\]|\[edit\]|\[citation needed\]") if badwords: badwords_regex = re.compile( "[^a-z]({})[^a-z]".format("|".join(badwords or []))) else: badwords_regex = None return functools.partial( clean_page, citation_regex=citation_regex, badwords_regex=badwords_regex) def clean_page(url_and_features, citation_regex, badwords_regex=None, counter_inc_fn=None, min_words_per_line=_MIN_WORDS_PER_LINE, min_num_sentences=_MIN_NUM_SENTENCES, max_word_length=_MAX_WORD_LENGTH): """Cleans a CommonCrawl page, yielding nothing if it should be skipped. Cleaning removes lines with no end marks or with too few words. After line filtering, pages are filtered out if they have too few sentences based on a simple count of end marks. Args: url_and_features: tuple(string, dict), the url and features of the page. citation_regex: Regex to use for finding Wikipedia-like citations to filter. badwords_regex: Regex to use for finding badwords. Default None, which means don't apply badwords filtering. counter_inc_fn: function, a function taking the name of a counter to be incremented and the (optional) amount. Defaults to a beam Metric counter. min_words_per_line: int, the minimum number of words a line needs to not be removed. min_num_sentences: int, the minimum number of sentences a page needs to not be skipped. max_word_length: int, the maximum number of characters allowed in a word. Lines containing a word with too many characters are removed. Yields: The url and cleaned text for the page. """ url, features = url_and_features text = features["text"] if not counter_inc_fn: counter_inc_fn = get_counter_inc_fn("clean-page") lines = text.splitlines() valid_lines = [] num_sentences = 0 def line_has_too_long_word(line): for word in line.split(): if len(word) > max_word_length: return True return False for line in lines: line = line.strip() if line_has_too_long_word(line): counter_inc_fn("lines-with-too-long-word") continue line = citation_regex.sub("", line) if not line.endswith(_END_MARKS) or line.endswith(_ELLIPSIS): counter_inc_fn("lines-no-endmark") continue if len(line.split()) < min_words_per_line: counter_inc_fn("lines-too-short") continue line_lower = line.lower() # Remove documents which contain lorem ipsum if "lorem ipsum" in line_lower: counter_inc_fn("filtered-page-loremipsum") return # Remove "javascript must be enabled" notices if "javascript" in line_lower: counter_inc_fn("lines-javascript") continue # Remove docs which probably contain javascript code if "{" in line: counter_inc_fn("filtered-page-squigglybracket") return # Remove policy lines if any(p in line_lower for p in _POLICY_SUBSTRINGS): counter_inc_fn("lines-policy") continue # If any badword appears on its own in the line, skip this doc if badwords_regex: badwords_found = badwords_regex.search(line_lower) if badwords_found is not None: counter_inc_fn("filtered-page-badword") return num_sentences += len(_get_sentences(line)) valid_lines.append(line) counter_inc_fn("lines-valid") if num_sentences < min_num_sentences: counter_inc_fn("filtered-page-toofewsentences") return counter_inc_fn("emitted-clean-pages") features["text"] = "\n".join(valid_lines).strip() yield url, features def _hash_line(line): m = hashlib.md5() m.update(tf.compat.as_text(line).encode("utf-8").strip().lower()) return m.hexdigest() def _emit_url_to_lines(page): """Emits url to all (lower-cased, hashed) lines.""" url, features = page text = features["text"] for line in text.split("\n"): yield _hash_line(line), url def _emit_line_to_urls(el, counter_inc_fn): """Emits (hashed) line to all but one url.""" line, urls = el # Materialize urls as a list. urls = list(urls) # Hash urls and sort to have a consistent, but unbiased, selection when the # same urls exist for multiple lines. skip_url = min( urls, key=lambda x: hashlib.md5(tf.compat.as_text(x).encode("utf-8")). hexdigest()) for url in urls: if url != skip_url: yield url, line counter_inc_fn("emitted-line-duplicate", amt=len(urls)-1) def _remove_lines_from_text( el, counter_inc_fn, min_num_sentences=_MIN_NUM_SENTENCES): """Removes matching lines from the page. Process the result of a join containing a single value for 'features' and zero or more values for 'lines'. Each value in 'lines' is a lower-cased, hashed line. If a line has fewer sentences than `max_window_size`, the full line is compared for a match. Args: el: `(string, {'features': features_dict, 'lines': [string]})`, element containing the result of a join on key with both the page text and lower-cased, hashed lines to remove. counter_inc_fn: function, a function taking the name of a counter to be incremented and the (optional) amount. min_num_sentences: int, the minimum number of sentences a page needs to not be skipped. Yields: url: The URL of the page. features: The page features with lines removed from text. """ url, join_values = el features = join_values["features"] assert len(features) == 1, "Invalid page count (%d) for %s" % ( len(features), url) features = features[0] text = features["text"] lines_to_remove = set(join_values["lines"]) new_lines = [] hashed_lines = set() for line in text.split("\n"): hashed_line = _hash_line(line) if hashed_line in lines_to_remove: counter_inc_fn("filtered-lines-duplicate") elif hashed_line not in hashed_lines: new_lines.append(line) hashed_lines.add(hashed_line) new_text = "\n".join(new_lines) if len(_get_sentences(new_text)) < min_num_sentences: counter_inc_fn("filtered-doc-toofewsentences") return new_features = features.copy() new_features["text"] = new_text yield (url, new_features) def remove_duplicate_text(pages): """Utility to remove duplicate lines across text documents.""" # Output: url, lines beam = tfds.core.lazy_imports.apache_beam counter_inc_fn = get_counter_inc_fn("dedupe-lines") lines_to_remove = ( pages | beam.FlatMap(_emit_url_to_lines) | "group_sentences" >> beam.GroupByKey() | beam.FlatMap(_emit_line_to_urls, counter_inc_fn=counter_inc_fn)) # Output: url, text final_docs = ({ "features": pages, "lines": lines_to_remove } | "group_features_and_lines_by_url" >> beam.CoGroupByKey() | beam.FlatMap( _remove_lines_from_text, counter_inc_fn=counter_inc_fn)) return final_docs def split_wet_file(wet_file_path, counter_inc_fn=None): """Split a WET file into separate pages.""" logging.info("Splitting file: %s", wet_file_path) if not counter_inc_fn: counter_inc_fn = get_counter_inc_fn("split-wet-file") counter_inc_fn("wet-file") with tf.io.gfile.GFile(wet_file_path, "rb") as f, gzip.GzipFile( fileobj=f) as g: url = None content = None content_len = None content_type = None timestamp = None def _maybe_get_page(): """Generate a (url, {features}) page.""" if not url and url is not None: counter_inc_fn("page-filtered-nourl") if not content and content is not None: counter_inc_fn("page-filtered-nocontent") if not content_type and content_type is not None: counter_inc_fn("page-nocontenttype") if not content_len and content_len is not None: counter_inc_fn("page-nocontentlen") if not timestamp and timestamp is not None: counter_inc_fn("page-notimestamp") if content and url: counter_inc_fn("page-emitted") return (url, { "text": "\n".join(content), "content-type": content_type, "content-length": content_len, "timestamp": timestamp, "url": url }) return None for line in io.TextIOWrapper(g, encoding="utf-8"): # pytype: disable=wrong-arg-types line = line.strip() if not line: continue if line == _PAGE_DELIMITER: page = _maybe_get_page() if page: yield page url = "" content = [] content_len = "" content_type = "" timestamp = "" if line.startswith(_URL_KEY): url = line[len(_URL_KEY):].strip() if line.startswith(_URL_DATE): timestamp = line[len(_URL_DATE):].strip() if line.startswith(_CONTENT_TYPE): content_type = line[len(_CONTENT_TYPE):].strip() if line.startswith(_CONTENT_LEN): content_len = line[len(_CONTENT_LEN):].strip() if line.startswith(_METADATA_PREFIXES): continue content.append(line) # pytype: disable=attribute-error page = _maybe_get_page() if page: yield page def dedupe_urls(el): """Returns the first value for a given URL.""" counter_inc_fn = get_counter_inc_fn("dedupe-urls") url, vals = el cnt = 0 v = None for v in vals: cnt += 1 counter_inc_fn("filtered-url-duplicate", cnt - 1) counter_inc_fn("unique-url") return url, v def is_valid_length(el, max_length=1.9e5): """Returns False iff page's text is too long.""" counter_inc_fn = get_counter_inc_fn("is-valid-length") _, page = el if len(page["text"]) > max_length: counter_inc_fn("filtered-page-contenttoolong") return False counter_inc_fn("valid-length") return True def is_realnews_domain(el, realnews_domains): """Returns False iff page's (sub)domain is not allowed.""" counter_inc_fn = get_counter_inc_fn("is-realnews-domain") url, _ = el ext = tfds.core.lazy_imports.tldextract.extract(url) main_domain = ext.domain + "." + ext.suffix if main_domain not in realnews_domains: counter_inc_fn("filtered-url-invaliddomain") return False allowed_subdomains = realnews_domains[main_domain] if (isinstance(allowed_subdomains, list) and ext.subdomain not in allowed_subdomains): counter_inc_fn("filtered-url-invalidsubdomain") return False counter_inc_fn("realnews-domain") return True def filter_by_webtextlike(el): """Yields only pages with a matching WebText-like URL.""" counter_inc_fn = get_counter_inc_fn("filter-by-webtextlike") url, join_values = el text = join_values["text"] webtextlike = join_values["webtextlike_urls"] if not webtextlike: counter_inc_fn("filtered-url-notwebtextlike") return if not text: counter_inc_fn("missing-webtextlike") return assert len(text) == 1 counter_inc_fn("found-webtextlike") yield url, text[0] def normalize_url(el): url, val = el url = tf.compat.as_text(url) url = re.sub(r"https?:\/\/(www\.)?", "", url) url = re.sub(r"\?(utm_|ref|feed).*", "", url) url = url.rstrip("/") return url, val