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256 lines (190 loc) · 7.08 KB
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"""
Utilities for processing text.
"""
from pathlib import Path
from unidecode import unidecode
import re, math, random, html
import ftfy
from huggingface_hub import hf_hub_download
# based on wiki word occurence
person_token = [("a person", 282265), ("someone", 121194), ("somebody", 12219)]
temp_token = "xtokx" # avoid repeating chars
class HashtagProcessor:
# Adapted from wordninja library
# We use our wikipedia word count + a good heuristic to make it work
def __init__(self):
wiki_word_frequency = hf_hub_download(
"dalle-mini/dalle-mini", filename="enwiki-words-frequency.txt"
)
self._word_cost = (
l.split()[0] for l in Path(wiki_word_frequency).read_text().splitlines()
)
self._word_cost = {
str(k): math.log(float(i + 1)) for i, k in enumerate(self._word_cost)
}
self._max_word = max(len(x) for x in self._word_cost.keys())
self._SPLIT_RE = re.compile("[^a-zA-Z0-9']+")
def __call__(self, s):
"""Uses dynamic programming to infer the location of spaces in a string without spaces."""
l = [self._split(x) for x in self._SPLIT_RE.split(s)]
return " ".join([item for sublist in l for item in sublist])
def _split(self, s):
# Find the best match for the i first characters, assuming cost has
# been built for the i-1 first characters.
# Returns a pair (match_cost, match_length).
def best_match(i):
candidates = enumerate(reversed(cost[max(0, i - self._max_word) : i]))
return min(
(c + self._word_cost.get(s[i - k - 1 : i].lower(), 9e999), k + 1)
for k, c in candidates
)
# Build the cost array
cost = [0]
for i in range(1, len(s) + 1):
c, k = best_match(i)
cost.append(c)
# Backtrack to recover the minimal-cost string.
out = []
i = len(s)
while i > 0:
c, k = best_match(i)
assert c == cost[i]
newToken = True
if not s[i - k : i] == "'": # ignore a lone apostrophe
if len(out) > 0:
# re-attach split 's and split digits
if out[-1] == "'s" or (
s[i - 1].isdigit() and out[-1][0].isdigit()
): # digit followed by digit
out[-1] = (
s[i - k : i] + out[-1]
) # combine current token with previous token
newToken = False
if newToken:
out.append(s[i - k : i])
i -= k
return reversed(out)
def replace_person_token(t):
"Used for CC12M"
t = re.sub("<person>([,\s]*(and)*[,\s]*<person>)+", " people ", t)
while "<person>" in t:
t = t.replace(
"<person>", f" {random.choices(*tuple(zip(*person_token)))[0]} ", 1
)
return t
def fix_html(t):
# from OpenAI CLIP
return html.unescape(html.unescape(t))
def replace_punctuation_with_commas(t):
return re.sub("[()[\].,|:;?!=+~\-\/{}]", ",", t)
def simplify_quotes(t):
return re.sub("""['"`]""", ' " ', t)
def merge_quotes(t):
return re.sub('(\s*"+\s*)+', ' " ', t)
def remove_comma_numbers(t):
def _f(t):
return re.sub("(\d),(\d{3})", r"\1\2", t)
return _f(_f(t))
def pre_process_dot_numbers(t):
return re.sub("(\w)\.(\w)", fr"\1{temp_token}dot{temp_token}\2", t)
def post_process_dot_numbers(t):
return re.sub(f"{temp_token}dot{temp_token}", ".", t)
def pre_process_quotes(t):
# allows quotes only for 's, 't, 'd, 'm, 'll, 're, 've
return re.sub(
r"'(?=([stdm]|(ll)|(re)|(ve)|(ll))\b)", fr"{temp_token}quote{temp_token}", t
)
def post_process_quotes(t):
return re.sub(f"{temp_token}quote{temp_token}", "'", t)
def pre_process_dates(t):
return re.sub("(\d)/(\d)", fr"\1{temp_token}slash{temp_token}\2", t)
def post_process_dates(t):
return re.sub(f"{temp_token}slash{temp_token}", "/", t)
def merge_commas(t):
return re.sub("(\s*,+\s*)+", ", ", t)
def add_space_after_commas(t):
return re.sub(",", ", ", t)
def handle_special_chars(t):
"Handle special characters"
# replace "-" with a space when between words without space
t = re.sub("(\w)-(\w)", r"\1 \2", t)
# always add space around some characters
return re.sub("([%&\/$*])", r" \1 ", t)
def expand_hashtags(t, hashtag_processor):
"Remove # and try to split words"
return re.sub("#(\w+)", lambda m: hashtag_processor(m.group(1)), t)
_re_ignore_chars = r"[_#\\]"
def ignore_chars(t):
"Ignore useless characters"
return re.sub(_re_ignore_chars, " ", t)
def remove_extra_spaces(t):
"Remove extra spaces (including \t and \n)"
return re.sub("\s+", " ", t)
def remove_repeating_chars(t):
"If the same character is present 4+ times (not 3 because of roman 'VIII'), replace with single instance"
return re.sub(r"(\D)(\1{3,})", r"\1", t)
def remove_urls(t):
return re.sub(r"http\S+", "", t)
def remove_html_tags(t):
return re.sub("<[^<]+?>", "", t)
def remove_first_last_commas(t):
t = t.strip()
t = t[:-1] if t and t[-1] == "," else t
t = t[1:] if t and t[0] == "," else t
return t.strip()
def remove_wiki_ref(t):
t = re.sub(r"\A\s*\[\d+\]", "", t)
return re.sub(r"\[\d+\]\s*\Z", "", t)
class TextNormalizer:
"Normalize text"
def __init__(self):
self._hashtag_processor = HashtagProcessor()
def __call__(self, t):
# fix some characters
t = ftfy.fix_text(t)
# fix html
t = fix_html(t)
# decode and simplify text: see unidecode library
t = unidecode(t)
# lower case
t = t.lower()
# replace <PERSON> (for CC12M)
t = replace_person_token(t)
# remove wiki reference (for WIT)
t = remove_wiki_ref(t)
# remove html tags
t = remove_html_tags(t)
# remove urls
t = remove_urls(t)
# remove commas in numbers
t = remove_comma_numbers(t)
# handle dots in numbers and quotes - Part 1
t = pre_process_dot_numbers(t)
t = pre_process_quotes(t)
t = pre_process_dates(t)
# handle special characters
t = handle_special_chars(t)
# handle hashtags
t = expand_hashtags(t, self._hashtag_processor)
# ignore useless characters
t = ignore_chars(t)
# simplify quotes
t = simplify_quotes(t)
# all punctuation becomes commas
t = replace_punctuation_with_commas(t)
# handle dots in numbers and quotes - Part 2
t = post_process_dot_numbers(t)
t = post_process_quotes(t)
t = post_process_dates(t)
# handle repeating characters
t = remove_repeating_chars(t)
# merge quotes
t = merge_quotes(t)
# merge commas
t = merge_commas(t)
# remove multiple spaces
t = remove_extra_spaces(t)
# remove first and last comma
t = remove_first_last_commas(t)
# always start with a space
return f" {t}"