import string
from spacy.lang.en import English
from spacy.tokenizer import Tokenizer
STOP_WORDS = ['the', 'a', 'an']
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer'))


def normalize(text):
    text = text.lower().strip()
    doc = nlp(text)
    filtered_sentences = []
    for sentence in doc.sents:
        filtered_tokens = list()
        for i, w in enumerate(sentence):
            s = w.string.strip()
            if len(s) == 0 or s in string.punctuation and i < len(doc) - 1:
                continue
            if s not in STOP_WORDS:
                s = s.replace(',', '.')
                filtered_tokens.append(s)
        filtered_sentences.append(' '.join(filtered_tokens))
    return filtered_sentences

403784f88c23b48197d2fc2825ca135fa693cbf7
<mask token>


def normalize(text):
    text = text.lower().strip()
    doc = nlp(text)
    filtered_sentences = []
    for sentence in doc.sents:
        filtered_tokens = list()
        for i, w in enumerate(sentence):
            s = w.string.strip()
            if len(s) == 0 or s in string.punctuation and i < len(doc) - 1:
                continue
            if s not in STOP_WORDS:
                s = s.replace(',', '.')
                filtered_tokens.append(s)
        filtered_sentences.append(' '.join(filtered_tokens))
    return filtered_sentences

<mask token>
nlp.add_pipe(nlp.create_pipe('sentencizer'))


def normalize(text):
    text = text.lower().strip()
    doc = nlp(text)
    filtered_sentences = []
    for sentence in doc.sents:
        filtered_tokens = list()
        for i, w in enumerate(sentence):
            s = w.string.strip()
            if len(s) == 0 or s in string.punctuation and i < len(doc) - 1:
                continue
            if s not in STOP_WORDS:
                s = s.replace(',', '.')
                filtered_tokens.append(s)
        filtered_sentences.append(' '.join(filtered_tokens))
    return filtered_sentences

<mask token>
STOP_WORDS = ['the', 'a', 'an']
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer'))


def normalize(text):
    text = text.lower().strip()
    doc = nlp(text)
    filtered_sentences = []
    for sentence in doc.sents:
        filtered_tokens = list()
        for i, w in enumerate(sentence):
            s = w.string.strip()
            if len(s) == 0 or s in string.punctuation and i < len(doc) - 1:
                continue
            if s not in STOP_WORDS:
                s = s.replace(',', '.')
                filtered_tokens.append(s)
        filtered_sentences.append(' '.join(filtered_tokens))
    return filtered_sentences

import string
from spacy.lang.en import English
from spacy.tokenizer import Tokenizer
STOP_WORDS = ['the', 'a', 'an']
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer'))


def normalize(text):
    text = text.lower().strip()
    doc = nlp(text)
    filtered_sentences = []
    for sentence in doc.sents:
        filtered_tokens = list()
        for i, w in enumerate(sentence):
            s = w.string.strip()
            if len(s) == 0 or s in string.punctuation and i < len(doc) - 1:
                continue
            if s not in STOP_WORDS:
                s = s.replace(',', '.')
                filtered_tokens.append(s)
        filtered_sentences.append(' '.join(filtered_tokens))
    return filtered_sentences
