import html
import logging
import re
import pyarabic.araby as araby
ACCEPTED_MODELS = [
"bert-base-arabertv01",
"bert-base-arabert",
"bert-base-arabertv02",
"bert-base-arabertv2",
"bert-large-arabertv02",
"bert-large-arabertv2",
"araelectra-base",
"araelectra-base-discriminator",
"araelectra-base-generator",
"aragpt2-base",
"aragpt2-medium",
"aragpt2-large",
"aragpt2-mega",
]
SEGMENTED_MODELS = [
"bert-base-arabert",
"bert-base-arabertv2",
"bert-large-arabertv2",
]
class ArabertPreprocessor:
"""
A Preprocessor class that cleans and preprocesses text for all models in the AraBERT repo.
It also can unprocess the text ouput of the generated text
Args:
model_name (:obj:`str`): model name from the HuggingFace Models page without the aubmindlab tag. Defaults to "bert-base-arabertv02". Current accepted models are:
- :obj:`"bert-base-arabertv01"`: No farasa segmentation.
- :obj:`"bert-base-arabert"`: with farasa segmentation.
- :obj:`"bert-base-arabertv02"`: No farasas egmentation.
- :obj:`"bert-base-arabertv2"`: with farasa segmentation.
- :obj:`"bert-large-arabertv02"`: No farasas egmentation.
- :obj:`"bert-large-arabertv2"`: with farasa segmentation.
- :obj:`"araelectra-base"`: No farasa segmentation.
- :obj:`"araelectra-base-discriminator"`: No farasa segmentation.
- :obj:`"araelectra-base-generator"`: No farasa segmentation.
- :obj:`"aragpt2-base"`: No farasa segmentation.
- :obj:`"aragpt2-medium"`: No farasa segmentation.
- :obj:`"aragpt2-large"`: No farasa segmentation.
- :obj:`"aragpt2-mega"`: No farasa segmentation.
keep_emojis(:obj: `bool`): don't remove emojis while preprocessing. Defaults to False
remove_html_markup(:obj: `bool`): Whether to remove html artfacts, should be set to False when preprocessing TyDi QA. Defaults to True
replace_urls_emails_mentions(:obj: `bool`): Whether to replace email urls and mentions by special tokens. Defaults to True
strip_tashkeel(:obj: `bool`): remove diacritics (FATHATAN, DAMMATAN, KASRATAN, FATHA, DAMMA, KASRA, SUKUN, SHADDA)
strip_tatweel(:obj: `bool`): remove tatweel '\\u0640'
insert_white_spaces(:obj: `bool`): insert whitespace before and after all non Arabic digits or English digits or Arabic and English Alphabet or the 2 brackets, then inserts whitespace between words and numbers or numbers and words
remove_elongation(:obj: `bool`): replace repetition of more than 2 non-digit character with 2 of this character
Returns:
ArabertPreprocessor: the preprocessor class
Example:
from preprocess import ArabertPreprocessor
arabert_prep = ArabertPreprocessor("aubmindlab/bert-base-arabertv2")
arabert_prep.preprocess("SOME ARABIC TEXT")
"""
def __init__(
self,
model_name,
keep_emojis=False,
remove_html_markup=True,
replace_urls_emails_mentions=True,
strip_tashkeel=True,
strip_tatweel=True,
insert_white_spaces=True,
remove_elongation=True,
):
"""
model_name (:obj:`str`): model name from the HuggingFace Models page without the aubmindlab tag. Defaults to "bert-base-arabertv02". Current accepted models are:
- :obj:`"bert-base-arabertv01"`: No farasa segmentation.
- :obj:`"bert-base-arabert"`: with farasa segmentation.
- :obj:`"bert-base-arabertv02"`: No farasas egmentation.
- :obj:`"bert-base-arabertv2"`: with farasa segmentation.
- :obj:`"bert-large-arabertv02"`: No farasas egmentation.
- :obj:`"bert-large-arabertv2"`: with farasa segmentation.
- :obj:`"araelectra-base"`: No farasa segmentation.
- :obj:`"araelectra-base-discriminator"`: No farasa segmentation.
- :obj:`"araelectra-base-generator"`: No farasa segmentation.
- :obj:`"aragpt2-base"`: No farasa segmentation.
- :obj:`"aragpt2-medium"`: No farasa segmentation.
- :obj:`"aragpt2-large"`: No farasa segmentation.
- :obj:`"aragpt2-mega"`: No farasa segmentation.
keep_emojis(:obj: `bool`): don't remove emojis while preprocessing. Defaults to False
remove_html_markup(:obj: `bool`): Whether to remove html artfacts, should be set to False when preprocessing TyDi QA. Defaults to True
replace_urls_emails_mentions(:obj: `bool`): Whether to replace email urls and mentions by special tokens. Defaults to True
strip_tashkeel(:obj: `bool`): remove diacritics (FATHATAN, DAMMATAN, KASRATAN, FATHA, DAMMA, KASRA, SUKUN, SHADDA)
strip_tatweel(:obj: `bool`): remove tatweel '\\u0640'
insert_white_spaces(:obj: `bool`): insert whitespace before and after all non Arabic digits or English digits or Arabic and English Alphabet or the 2 brackets, then inserts whitespace between words and numbers or numbers and words
remove_elongation(:obj: `bool`): replace repetition of more than 2 non-digit character with 2 of this character
"""
model_name = model_name.replace("aubmindlab/", "")
if model_name not in ACCEPTED_MODELS:
logging.warning(
"Model provided is not in the accepted model list. Assuming you don't want Farasa Segmentation"
)
self.model_name = "bert-base-arabertv02"
else:
self.model_name = model_name
self.keep_emojis = keep_emojis
self.remove_html_markup = remove_html_markup
self.replace_urls_emails_mentions = replace_urls_emails_mentions
self.strip_tashkeel = strip_tashkeel
self.strip_tatweel = strip_tatweel
self.insert_white_spaces = insert_white_spaces
self.remove_elongation = remove_elongation
def preprocess(self, text):
"""
Preprocess takes an input text line an applies the same preprocessing used in AraBERT
pretraining
Args:
text (:obj:`str`): inout text string
Returns:
string: A preprocessed string depending on which model was selected
"""
text = str(text)
text = html.unescape(text)
if self.strip_tashkeel:
text = araby.strip_tashkeel(text)
if self.strip_tatweel:
text = araby.strip_tatweel(text)
if self.replace_urls_emails_mentions:
# replace all possible URLs
for reg in url_regexes:
text = re.sub(reg, " [رابط] ", text)
# REplace Emails with [بريد]
for reg in email_regexes:
text = re.sub(reg, " [بريد] ", text)
# replace mentions with [مستخدم]
text = re.sub(user_mention_regex, " [مستخدم] ", text)
if self.remove_html_markup:
# remove html line breaks
text = re.sub("
", " ", text)
# remove html markup
text = re.sub("]+>", " ", text)
# remove repeated characters >2
if self.remove_elongation:
text = self._remove_elongation(text)
# insert whitespace before and after all non Arabic digits or English Digits and Alphabet and the 2 brackets
if self.insert_white_spaces:
text = re.sub(
"([^0-9\u0621-\u063A\u0641-\u064A\u0660-\u0669a-zA-Z\[\]])",
r" \1 ",
text,
)
# insert whitespace between words and numbers or numbers and words
text = re.sub(
"(\d+)([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)", r" \1 \2 ", text
)
text = re.sub(
"([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)(\d+)", r" \1 \2 ", text
)
text = re.sub(rejected_chars_regex, " ", text)
# remove extra spaces
text = " ".join(text.replace("\uFE0F", "").split())
# ALl the other models dont require Farasa Segmentation
return text
def unpreprocess(self, text, desegment=True):
"""Re-formats the text to a classic format where punctuations, brackets, parenthesis are not seperated by whitespaces.
The objective is to make the generated text of any model appear natural and not preprocessed.
Args:
text (str): input text to be un-preprocessed
desegment (bool, optional): [whether or not to remove farasa pre-segmentation before]. Defaults to True.
Returns:
str: The unpreprocessed (and possibly Farasa-desegmented) text.
"""
# removes the spaces around quotation marks ex: i " ate " an apple --> i "ate" an apple
# https://stackoverflow.com/a/53436792/5381220
text = re.sub(white_spaced_double_quotation_regex, '"' + r"\1" + '"', text)
text = re.sub(white_spaced_single_quotation_regex, "'" + r"\1" + "'", text)
text = re.sub(white_spaced_back_quotation_regex, "\`" + r"\1" + "\`", text)
text = re.sub(white_spaced_back_quotation_regex, "\—" + r"\1" + "\—", text)
# during generation, sometimes the models don't put a space after the dot, this handles it
text = text.replace(".", " . ")
text = " ".join(text.split())
# handle decimals
text = re.sub(r"(\d+) \. (\d+)", r"\1.\2", text)
text = re.sub(r"(\d+) \, (\d+)", r"\1,\2", text)
text = re.sub(left_and_right_spaced_chars, r"\1", text)
text = re.sub(left_spaced_chars, r"\1", text)
text = re.sub(right_spaced_chars, r"\1", text)
return text
def _remove_elongation(self, text):
"""
:param text: the input text to remove elongation
:return: delongated text
"""
# loop over the number of times the regex matched the text
for index_ in range(len(re.findall(regex_tatweel, text))):
elongation = re.search(regex_tatweel, text)
if elongation:
elongation_pattern = elongation.group()
elongation_replacement = elongation_pattern[0]
elongation_pattern = re.escape(elongation_pattern)
text = re.sub(
elongation_pattern, elongation_replacement, text, flags=re.MULTILINE
)
else:
break
return text
def _remove_redundant_punct(self, text):
text_ = text
result = re.search(redundant_punct_pattern, text)
dif = 0
while result:
sub = result.group()
sub = sorted(set(sub), key=sub.index)
sub = " " + "".join(list(sub)) + " "
text = "".join(
(text[: result.span()[0] + dif], sub, text[result.span()[1] + dif :])
)
text_ = "".join(
(text_[: result.span()[0]], text_[result.span()[1] :])
).strip()
dif = abs(len(text) - len(text_))
result = re.search(redundant_punct_pattern, text_)
text = re.sub(r"\s+", " ", text)
return text.strip()
prefix_list = [
"ال",
"و",
"ف",
"ب",
"ك",
"ل",
"لل",
"\u0627\u0644",
"\u0648",
"\u0641",
"\u0628",
"\u0643",
"\u0644",
"\u0644\u0644",
"س",
]
suffix_list = [
"ه",
"ها",
"ك",
"ي",
"هما",
"كما",
"نا",
"كم",
"هم",
"هن",
"كن",
"ا",
"ان",
"ين",
"ون",
"وا",
"ات",
"ت",
"ن",
"ة",
"\u0647",
"\u0647\u0627",
"\u0643",
"\u064a",
"\u0647\u0645\u0627",
"\u0643\u0645\u0627",
"\u0646\u0627",
"\u0643\u0645",
"\u0647\u0645",
"\u0647\u0646",
"\u0643\u0646",
"\u0627",
"\u0627\u0646",
"\u064a\u0646",
"\u0648\u0646",
"\u0648\u0627",
"\u0627\u062a",
"\u062a",
"\u0646",
"\u0629",
]
other_tokens = ["[رابط]", "[مستخدم]", "[بريد]"]
# the never_split list is ussed with the transformers library
prefix_symbols = [x + "+" for x in prefix_list]
suffix_symblos = ["+" + x for x in suffix_list]
never_split_tokens = list(set(prefix_symbols + suffix_symblos + other_tokens))
url_regexes = [
r"(http(s)?:\/\/.)?(www\.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0-9@:%_\+.~#?&//=]*)",
r"@(https?|ftp)://(-\.)?([^\s/?\.#-]+\.?)+(/[^\s]*)?$@iS",
r"http[s]?://[a-zA-Z0-9_\-./~\?=%&]+",
r"www[a-zA-Z0-9_\-?=%&/.~]+",
r"[a-zA-Z]+\.com",
r"(?=http)[^\s]+",
r"(?=www)[^\s]+",
r"://",
]
user_mention_regex = r"@[\w\d]+"
email_regexes = [r"[\w-]+@([\w-]+\.)+[\w-]+", r"\S+@\S+"]
redundant_punct_pattern = (
r"([!\"#\$%\'\(\)\*\+,\.:;\-<=·>?@\[\\\]\^_ـ`{\|}~—٪’،؟`୍“؛”ۚ【»؛\s+«–…‘]{2,})"
)
regex_tatweel = r"(\D)\1{2,}"
rejected_chars_regex = r"[^0-9\u0621-\u063A\u0640-\u066C\u0671-\u0674a-zA-Z\[\]!\"#\$%\'\(\)\*\+,\.:;\-<=·>?@\[\\\]\^_ـ`{\|}~—٪’،؟`୍“؛”ۚ»؛\s+«–…‘]"
regex_url_step1 = r"(?=http)[^\s]+"
regex_url_step2 = r"(?=www)[^\s]+"
regex_url = r"(http(s)?:\/\/.)?(www\.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0-9@:%_\+.~#?&//=]*)"
regex_mention = r"@[\w\d]+"
regex_email = r"\S+@\S+"
chars_regex = r"0-9\u0621-\u063A\u0640-\u066C\u0671-\u0674a-zA-Z\[\]!\"#\$%\'\(\)\*\+,\.:;\-<=·>?@\[\\\]\^_ـ`{\|}~—٪’،؟`୍“؛”ۚ»؛\s+«–…‘"
white_spaced_double_quotation_regex = r'\"\s+([^"]+)\s+\"'
white_spaced_single_quotation_regex = r"\'\s+([^']+)\s+\'"
white_spaced_back_quotation_regex = r"\`\s+([^`]+)\s+\`"
white_spaced_em_dash = r"\—\s+([^—]+)\s+\—"
left_spaced_chars = r" ([\]!#\$%\),\.:;\?}٪’،؟”؛…»·])"
right_spaced_chars = r"([\[\(\{“«‘*\~]) "
left_and_right_spaced_chars = r" ([\+\-\<\=\>\@\\\^\_\|\–]) "