Python ftfy.fix_text方法代碼示例

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def encode(self, texts, verbose=True):
        texts_tokens = []
        if verbose:
            for text in tqdm(texts, ncols=80, leave=False):
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend(
                        [self.encoder.get(t, 0) for t in
                         self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        else:
            for text in texts:
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend(
                        [self.encoder.get(t, 0) for t in
                         self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        return texts_tokens 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def cnndm_generator(mode, seed=0, shuffle=False, comm=None):
    # data originally from https://github.com/abisee/cnn-dailymail
    if mode == 'valid':
        mode = 'val'
    with open(gcs.download_file_cached(f'gs://lm-human-preferences/datasets/cnndm/url_lists/all_{mode}.txt', comm=comm)) as f:
        urls = [line.strip() for line in f]
    if shuffle:
        random.seed(seed)
        random.shuffle(urls)
    # if n_eval > 0:
    #     urls = urls[:n_eval]

    urls_dir = gcs.download_directory_cached(f'gs://lm-human-preferences/datasets/cnndm/cache_{mode}', comm=comm)

    for i, url in enumerate(urls):
        path = os.path.join(urls_dir, get_path_of_url(url))
        text = open(path).read()
        text = clean_up_start(text)
        text = ftfy.fix_text(text)

        text = re.sub(r"\n{3,}", "\n\n", text)
        text = text.split('@highlight')[0].strip()
        yield text
        # _, ref_sents = get_art_abs(path) 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def tldr_generator(mode, seed=0, shuffle=False, comm=None):
    random.seed(seed)

    if mode == 'test':
        mode = 'valid' # validation set serves as training set, since we don't have access..
    assert mode in ['train', 'valid']

    with open(gcs.download_file_cached(f'gs://lm-human-preferences/tldr/{mode}-subset.json', comm=comm)) as f:
        datas = json.load(f)

    if shuffle:
        random.seed(seed)
        random.shuffle(datas)

    for data in datas:
        text = data['content']
        text = ftfy.fix_text(text)
        text = re.sub(r"\n{3,}", "\n\n", text)
        text = text.strip()
        yield text 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
        try:
            import ftfy
            import spacy
            self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
            self.fix_text = ftfy.fix_text
        except ImportError:
            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
            self.nlp = BasicTokenizer(do_lower_case=True,
                                      never_split=special_tokens if special_tokens is not None else [])
            self.fix_text = None

        self.max_len = max_len if max_len is not None else int(1e12)
        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
        self.decoder = {v:k for k,v in self.encoder.items()}
        merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
        merges = [tuple(merge.split()) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {}
        self.special_tokens = {}
        self.special_tokens_decoder = {}
        self.set_special_tokens(special_tokens) 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
        super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs)

        try:
            import ftfy
            from spacy.lang.en import English
            _nlp = English()
            self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
            self.fix_text = ftfy.fix_text
        except ImportError:
            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
            self.nlp = BasicTokenizer(do_lower_case=True)
            self.fix_text = None

        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
        self.decoder = {v:k for k,v in self.encoder.items()}
        merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
        merges = [tuple(merge.split()) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {} 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def encode(self, texts, verbose=True):
        texts_tokens = []
        if verbose:
            for text in tqdm(texts, ncols=80, leave=False):
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        else:
            for text in texts:
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        return texts_tokens 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def encode(self, texts, verbose=True):
        texts_tokens = []
        if verbose:
            for text in tqdm(texts, ncols=80, leave=False):
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend(
                        [self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        else:
            for text in texts:
                text = self.nlp(text_standardize(ftfy.fix_text(text)))
                text_tokens = []
                for token in text:
                    text_tokens.extend(
                        [self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
                texts_tokens.append(text_tokens)
        return texts_tokens 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
        try:
            import ftfy
            import spacy
            self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
            self.fix_text = ftfy.fix_text
        except ImportError:
            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
            self.nlp = BasicTokenizer(do_lower_case=True,
                                      never_split=special_tokens if special_tokens is not None else [])
            self.fix_text = None

        self.max_len = max_len if max_len is not None else int(1e12)
        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
        self.decoder = {v:k for k,v in self.encoder.items()}
        merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
        merges = [tuple(merge.split()) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {}
        self.set_special_tokens(special_tokens) 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def summarize_text(request):
    if request.html:
        parser = HtmlParser.from_file(file_path=request.html,
                                      url=request.url,
                                      tokenizer=Tokenizer(LANGUAGE))
    else:
        parser = PlaintextParser.from_file(file_path=request.html,
                                           tokenizer=Tokenizer(LANGUAGE))

    stemmer = Stemmer(LANGUAGE)

    summarizer = Summarizer(stemmer)
    summarizer.stop_words = get_stop_words(LANGUAGE)
    sentences = [fix_text(str(s)) for s in summarizer(parser.document, SENTENCES_COUNT)]
    html = generate_html(sentences, fix_text(request.title)).render()
    request.send_html(html) 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
        super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs)

        try:
            import ftfy
            import spacy
            self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
            self.fix_text = ftfy.fix_text
        except ImportError:
            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
            self.nlp = BasicTokenizer(do_lower_case=True)
            self.fix_text = None

        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
        self.decoder = {v:k for k,v in self.encoder.items()}
        merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
        merges = [tuple(merge.split()) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {} 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def get_modified_content(self, format="srt", debug=False):
        """
        :return: string 
        """
        if not self.mods:
            return fix_text(self.content.decode(encoding=self._guessed_encoding), **ftfy_defaults).encode(
                encoding=self._guessed_encoding)

        submods = SubtitleModifications(debug=debug)
        if submods.load(content=self.text, language=self.language):
            logger.info("Applying mods: %s", self.mods)
            submods.modify(*self.mods)
            self.mods = submods.mods_used

            content = fix_text(self.pysubs2_to_unicode(submods.f, format=format), **ftfy_defaults)\
                .encode(encoding=self._guessed_encoding)
            submods.f = None
            del submods
            return content
        return None 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def test(data):
    text, expected = data

    text2 = text.encode('cp437').decode('utf-8')

    text3 = unidecode(text2)
    text4 = unicodedata.normalize('NFC', text2)

    text5 = unidecode(text4)

    print('                                text:', text, '| len:', len(text))
    print('                            expected:', expected, '  | len:', len(expected))
    print('                    text == expected:', text == expected)
    print('-------------------------------------')
    print('text.encode("cp437").decode("utf-8"):', text2, '  | len:', len(text2), '| expected:', text2 == expected)
    print('                      unicode(text2):', text3, '  | len:', len(text3), '| expected:', text3 == expected)
    print('-------------------------------------')
    print(' unicodedata.normalize("NFC", text2):', text4, '  | len:', len(text4), '| expected:', text4 == expected)
    print('                      unicode(text4):', text5, '  | len:', len(text5), '| expected:', text5 == expected)
    print('-------------------------------------')
    print('                 ftfy.fix_text(text):', ftfy.fix_text(text))
    print('-------------------------------------') 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def search_citation(text, exp):
    '''Finds sentences around citations, where the regexp `exp matches'''

    text = text.decode('utf-8')
    lines = text.split('\n')
    text = ' '.join(lines)
    text = ' '.join(text.split())
    text = ftfy.fix_text(text)
    logging.info("Search...'{0!s}'".format(exp))

    sentences = split_sentences(text)
    regex = re.compile(exp, flags=(re.I))

    founds = set()
    for sent in sentences:
        if regex.search(sent):
            founds.add(sent)
    return founds 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def fix_string(val):
	if isinstance(val, list):
		val = [fixCase(tmp) for tmp in val]
		return val

	if not val:
		return val
	val = fixUnicodeSpaces(val)
	val = fixSmartQuotes(val)
	val = fixCase(val)
	val = fixHtmlEntities(val)
	val = ftfy.fix_text(val)
	return val 

# 需要導入模塊: import ftfy [as 別名]
# 或者: from ftfy import fix_text [as 別名]
def set_special_tokens(self, special_tokens):
        """ Add a list of additional tokens to the encoder.
            The additional tokens are indexed starting from the last index of the
            current vocabulary in the order of the `special_tokens` list.
        """
        if not special_tokens:
            self.special_tokens = {}
            self.special_tokens_decoder = {}
            return
        self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
        self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
        if self.fix_text is None:
            # Using BERT's BasicTokenizer: we can update the tokenizer
            self.nlp.never_split = special_tokens
        logger.info("Special tokens {}".format(self.special_tokens))