(tokens)
return tuple(words), features, n_feats - 1
Args:
            tokens: A list of tokens, where each token consists of a word,
                optionally followed by u"￨"-delimited features.
        Returns:
            A sequence of words, a sequence of features, and num of features.
Args:
            tokens: A list of tokens, where each token consists of a word,
                optionally followed by u"￨"-delimited features.
        Returns:
            A sequence of words, a sequence of features, and num of features.
Args : tokens : A list of tokens where each token consists of a word optionally followed by u ￨ - delimited features . Returns : A sequence of words a sequence of features and num of features .
def extract_text_features(tokens):
        """
        Args:
            tokens: A list of tokens, where each token consists of a word,
                optionally followed by u"￨"-delimited features.
        Returns:
            A sequence of words, a sequence of features, and num of features.
        """
        if not tokens:
            return [], [], -1

        specials = [PAD_WORD, UNK_WORD, BOS_WORD, EOS_WORD]
        words = []
        features = []
        n_feats = None
        for token in tokens:
            split_token = token.split(u"￨")
            assert all([special != split_token[0] for special in specials]), \
                "Dataset cannot contain Special Tokens"

            if split_token[0]:
                words += [split_token[0]]
                features += [split_token[1:]]

                if n_feats is None:
                    n_feats = len(split_token)
                else:
                    assert len(split_token) == n_feats, \
                        "all words must have the same number of features"
        features = list(zip(*features))
        return tuple(words), features, n_feats - 1
def extract_text_features ( tokens ) : if not tokens : return [ ] , [ ] , - 1 specials = [ PAD_WORD , UNK_WORD , BOS_WORD , EOS_WORD ] words = [ ] features = [ ] n_feats = None for token in tokens : split_token = token . split ( u"￨") assert all ( [ special != split_token [ 0 ] for special in specials ] ) , "Dataset cannot contain Special Tokens" if split_token [ 0 ] : words += [ split_token [ 0 ] ] features += [ split_token [ 1 : ] ] if n_feats is None : n_feats = len ( split_token ) else : assert len ( split_token ) == n_feats , "all words must have the same number of features" features = list ( zip ( * features ) ) return tuple ( words ) , features , n_feats - 1