import functools import gzip import hashlib import heapq import io import re import threading import nltk # 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 = 3 _MIN_NUM_SENTENCES = 5 _MAX_WORD_LENGTH = 1000 _END_MARKS = (".", "?", "!", "\"") _ELLIPSIS = "..." _POLICY_SUBSTRINGS = [ "terms of use", "privacy policy", "cookie policy", "uses cookies", "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" ] # Memoized sentence tokenizer. _SENTENCE_TOKENIZER = None UNKNOWN_LANGUAGE = "und" citation_regex = re.compile(r"\[\d*\]|\[edit\]|\[citation needed\]") from .badwords_ennl import badword_list badwords_regex = re.compile(r"(?:\W|^)({})(?:\W|$)".format("|".join(badword_list))) def badwords_filter(text): badwords_found = badwords_regex.search(text.lower()) if badwords_found is not None: return False return True def clean_text(text, citation_regex=citation_regex, 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: text: text of the page citation_regex: Regex to use for finding Wikipedia-like citations to filter. 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. """ lines = text.splitlines() valid_lines = [] num_sentences = 0 if not badwords_filter(text): counter_inc_fn("badword-filtered: not passed") return 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("line-filtered:too_long_word") continue line = citation_regex.sub("", line) if not line.endswith(_END_MARKS) or line.endswith(_ELLIPSIS): counter_inc_fn("line-filtered:no_endmark") continue if len(line.split()) < min_words_per_line: counter_inc_fn("line-filtered:too_short") continue line_lower = line.lower() # Remove documents which contain lorem ipsum if "lorem ipsum" in line_lower: counter_inc_fn("filtered:loremipsum") return # Remove "javascript must be enabled" notices if "javascript" in line_lower: counter_inc_fn("line-filtered:javascript") continue # Remove docs which probably contain javascript code if "{" in line: counter_inc_fn("filtered:squigglybracket") return # Remove policy lines if any(p in line_lower for p in _POLICY_SUBSTRINGS): counter_inc_fn("line-filtered:policy") continue num_sentences += len(_get_sentences(line)) valid_lines.append(line) counter_inc_fn("line-passed") if num_sentences < min_num_sentences: counter_inc_fn("filtered:too_few_sentences") return counter_inc_fn("passed") result = "\n".join(valid_lines).strip() return result 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))) _nltk_lock = threading.Lock() def _load_sentence_tokenizer(): """Returns a sentence tokenization function.""" # Lock to avoid a race-condition in the creation of the download directory. with _nltk_lock: nltk.download("punkt") return nltk.data.load("nltk:tokenizers/punkt/english.pickle") count_dict = dict() def counter_inc_fn(what): if what in count_dict: count_dict[what] += 1 else: count_dict[what] = 1