Datasets:
Languages:
Finnish
Multilinguality:
monolingual
Size Categories:
unknown
Source Datasets:
extended|mc4
Tags:
| 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 | |