Datasets:
Languages:
Finnish
Multilinguality:
monolingual
Size Categories:
unknown
Source Datasets:
extended|mc4
Tags:
File size: 4,849 Bytes
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from fastcore.basics import listify
import unicodedata
import unidecode
from string import punctuation
import html
from itertools import groupby
import fasttext
import re
control_char_regex = re.compile(r'[\r\n\t]+')
url_regex = re.compile(
r'((http|https)\:\/\/)?[a-zA-Z0-9\.\/\?\:@\-_=#]+\.([a-zA-Z]){2,6}([a-zA-Z0-9\.\&\/\?\:@\-_=#])*')
username_regex = re.compile(r'(^|[^@\w])@(\w{1,15})\b')
FASTTEXT_MODEL_PATH = 'lid.176.bin'
fasttext_model = fasttext.load_model(FASTTEXT_MODEL_PATH)
def fix_html(example):
"From fastai: 'Fix messy things we've seen in documents'"
tmp_ls = []
for e in listify(example['text']):
e = e.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace('nbsp;', ' ').replace(
'#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace('<br />', "\n").replace(
'\\"', '"').replace('<unk>', ' ').replace(' @.@ ', '.').replace(' @-@ ', '-').replace('...', ' …')
tmp_ls.append(html.unescape(e))
example['text'] = tmp_ls
return example
def remove_control_char(example):
tmp_ls = []
for e in listify(example['text']):
tmp_ls.append(re.sub(control_char_regex, '.', e))
example['text'] = tmp_ls
return example
def remove_remaining_control_chars(example):
tmp_ls = []
for e in listify(example['text']):
tmp_ls.append(
''.join(ch for ch in e if unicodedata.category(ch)[0] != 'C'))
example['text'] = tmp_ls
return example
def remove_unicode_symbols(example):
tmp_ls = []
for e in listify(example['text']):
tmp_ls.append(
''.join(ch for ch in e if unicodedata.category(ch)[0] != 'So'))
example['text'] = tmp_ls
return example
def standardise_punc(example):
transl_table = dict([(ord(x), ord(y))
for x, y in zip(u"‘’´“”–-", u"'''\"\"--")])
tmp_ls = []
for e in listify(example['text']):
e = e.translate(transl_table)
e = re.sub(r"[^a-zA-Z0-9ÖÄÅöäå .,'%&€$=*@+;<>/()!?%:-]", " ", e)
tmp_ls.append(e)
example['text'] = tmp_ls
return example
def remove_news_tags(example):
tmp_ls = []
for e in listify(example['text']):
e = re.sub(r"(<[A-Z].+?>)|(</[A-Z].+?>)", "", e)
tmp_ls.append(e)
example['text'] = tmp_ls
return example
def replace_urls(example):
filler, tmp_ls = '', []
for e in listify(example['text']):
e = re.sub(r"(<a.+?>)|(</a>)|(<ref.+?>)", "", e)
e = re.sub(url_regex, filler, e)
tmp_ls.append(e)
example['text'] = tmp_ls
return example
def replace_usernames(example):
filler, tmp_ls = '', []
for e in listify(example['text']):
occ = e.count('@')
for _ in range(occ):
e = e.replace('@<user>', f'{filler}')
# replace other user handles by filler
e = re.sub(username_regex, filler, e)
# add spaces between, and remove double spaces again
e = e.replace(filler, f' {filler} ')
e = ' '.join(e.split())
tmp_ls.append(e)
example['text'] = tmp_ls
return example
def remove_duplicate_words_punctuation(example):
tmp_ls = []
for e in listify(example['text']):
e = re.sub(r'\b(\w+)( \1\b)+', r'\1', e)
punc = set(punctuation)
newtext = []
for k, g in groupby(e):
if k in punc:
newtext.append(k)
else:
newtext.extend(g)
e = ''.join(newtext)
tmp_ls.append(e)
example['text'] = tmp_ls
return example
def remove_multi_space(example):
tmp_ls = []
for e in listify(example['text']):
tmp_ls.append(' '.join(e.split()))
example['text'] = tmp_ls
return example
def count_alphabet(batch):
batch['alphabet_len'] = len(re.findall(r'[äÄöÖåÅa-zA-Z]', batch['text']))
return batch
def count_numbers(batch):
batch['number_len'] = len(re.findall(r'[0-9]', batch['text']))
return batch
def count_upper(batch):
batch['upper_len'] = len(re.findall(r'[ÄÖÅA-Z]', batch['text']))
return batch
def count_str_len(batch):
batch['total_len'] = len(batch['text'])
return batch
def predict_lang(batch):
pred = fasttext_model.predict(batch['text'])
batch['predicted_lang'] = pred[0][0]
batch['predicted_lang_percentage'] = float(pred[1][0])
return batch
def calculate_alphabet_ratio(batch):
batch['alphabet_ratio'] = int(
batch['alphabet_len']) / int(batch['total_len'])
return batch
def calculate_number_ratio(batch):
batch['number_ratio'] = int(batch['number_len']) / int(batch['total_len'])
return batch
def calculate_upper_ratio(batch):
batch['upper_ratio'] = int(batch['upper_len']) / int(batch['total_len'])
return batch
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