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Looking at some samples of text, some minimal text cleaning may include: # clean a list of lines def clean_lines(lines): cleaned = list() # prepare regex for char filtering re_print = re.compile('[^%s]' % re.escape(string.printable)) # prepare translation table for removing punctuation table = str.maketrans('', '', string.punctuation) for line in lines: # normalize unicode characters line = normalize('NFD', line).encode('ascii', 'ignore') line = line.decode('UTF-8') # tokenize on white space line = line.split() # convert to lower case line = [word.lower() for word in line] # remove punctuation from each token line = [word.translate(table) for word in line] # remove non-printable chars form each token line = [re_print.sub('', w) for w in line] # remove tokens with numbers in them line = [word for word in line if word.isalpha()] # store as string cleaned.append(' '.join(line)) return cleaned Once normalized, we save the lists of clean lines directly in binary format using the pickle API.This will speed up loading for further operations later and in the future.

A lot happens in reading over the course of kindergarten, so here's a handy guide to help you know where your child should be with reading skills at the beginning of the kindergarten year, as well as at the end.Machine translation is the challenging task of converting text from a source language into coherent and matching text in a target language.Neural machine translation systems such as encoder-decoder recurrent neural networks are achieving state-of-the-art results for machine translation with a single end-to-end system trained directly on source and target language.Reusing the loading and splitting functions developed in the previous sections, the complete example is listed below.import string import re from pickle import dump from unicodedata import normalize # load doc into memory def load_doc(filename): # open the file as read only file = open(filename, mode='rt', encoding='utf-8') # read all text text = file.read() # close the file file.close() return text # split a loaded document into sentences def to_sentences(doc): return doc.strip().split('\n') # clean a list of lines def clean_lines(lines): cleaned = list() # prepare regex for char filtering re_print = re.compile('[^%s]' % re.escape(string.printable)) # prepare translation table for removing punctuation table = str.maketrans('', '', string.punctuation) for line in lines: # normalize unicode characters line = normalize('NFD', line).encode('ascii', 'ignore') line = line.decode('UTF-8') # tokenize on white space line = line.split() # convert to lower case line = [word.lower() for word in line] # remove punctuation from each token line = [word.translate(table) for word in line] # remove non-printable chars form each token line = [re_print.sub('', w) for w in line] # remove tokens with numbers in them line = [word for word in line if word.isalpha()] # store as string cleaned.append(' '.join(line)) return cleaned # save a list of clean sentences to file def save_clean_sentences(sentences, filename): dump(sentences, open(filename, 'wb')) print('Saved: %s' % filename) # load English data filename = 'europarl-v7.fr-en.en' doc = load_doc(filename) sentences = to_sentences(doc) sentences = clean_lines(sentences) save_clean_sentences(sentences, 'english.pkl') # spot check for i in range(10): print(sentences[i]) # load French data filename = 'europarl-v7.fr-en.fr' doc = load_doc(filename) sentences = to_sentences(doc) sentences = clean_lines(sentences) save_clean_sentences(sentences, 'french.pkl') # spot check for i in range(10): print(sentences[i]) resumption of the session i declare resumed the session of the european parliament adjourned on friday december and i would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period although as you will have seen the dreaded millennium bug failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful you have requested a debate on this subject in the course of the next few days during this partsession in the meantime i should like to observe a minute s silence as a number of members have requested on behalf of all the victims concerned particularly those of the terrible storms in the various countries of the european union please rise then for this minute s silence the house rose and observed a minute s silence madam president on a point of order you will be aware from the press and television that there have been a number of bomb explosions and killings in sri lanka one of the people assassinated very recently in sri lanka was mr kumar ponnambalam who had visited the european parliament just a few months agoin the meantime i should like to observe a minute s silence as a number of members have requested on behalf of all the victims concerned particularly those of the terrible storms in the various countries of the european union reprise de la session je declare reprise la session du parlement europeen qui avait ete interrompue le vendredi decembre dernier et je vous renouvelle tous mes vux en esperant que vous avez passe de bonnes vacances comme vous avez pu le constater le grand bogue de lan ne sest pas produit en revanche les citoyens dun certain nombre de nos pays ont ete victimes de catastrophes naturelles qui ont vraiment ete terribles vous avez souhaite un debat a ce sujet dans les prochains jours au cours de cette periode de session en attendant je souhaiterais comme un certain nombre de collegues me lont demande que nous observions une minute de silence pour toutes les victimes des tempetes notamment dans les differents pays de lunion europeenne qui ont ete touches je vous invite a vous lever pour cette minute de silence le parlement debout observe une minute de silence madame la presidente cest une motion de procedure vous avez probablement appris par la presse et par la television que plusieurs attentats a la bombe et crimes ont ete perpetres au sri lanka lune des personnes qui vient detre assassinee au sri lanka est m kumar ponnambalam qui avait rendu visite au parlement europeen il y a quelques mois a peinecomme vous avez pu le constater le grand bogue de lan ne sest pas produit en revanche les citoyens dun certain nombre de nos pays ont ete victimes de catastrophes naturelles qui ont vraiment ete terriblesen attendant je souhaiterais comme un certain nombre de collegues me lont demande que nous observions une minute de silence pour toutes les victimes des tempetes notamment dans les differents pays de lunion europeenne qui ont ete touches‘ characters for plurals.Standard datasets are required to develop, explore, and familiarize yourself with how to develop neural machine translation systems.

In this tutorial, you will discover the Europarl standard machine translation dataset and how to prepare the data for modeling.

A large range of topics are covered in these educational articles, from back-talking toddlers to college-bound teenagers.

There are also articles about best practices to use with kids and teens with specific mental and physical needs.

We will focus on the parallel French-English dataset.

This is a prepared corpus of aligned French and English sentences recorded between 19.

You have requested a debate on this subject in the course of the next few days, during this part-session.