deeppavlov/models/morpho_tagger/network.py
def _make_sent_vector(self, sent: List, bucket_length: int =None) -> np.ndarray:
        """Transforms a sentence to Numpy array, which will be the network input.

        Args:
            sent: input sentence
            bucket_length: the width of the bucket

        Returns:
            A 3d array, answer[i][j][k] contains the index of k-th letter
            in j-th word of i-th input sentence.
        """
        bucket_length = bucket_length or len(sent)
        answer = np.zeros(shape=(bucket_length, MAX_WORD_LENGTH+2), dtype=np.int32)
        for i, word in enumerate(sent):
            answer[i, 0] = self.tags.tok2idx("BEGIN")
            m = min(len(word), MAX_WORD_LENGTH)
            for j, x in enumerate(word[-m:]):
                answer[i, j+1] = self.symbols.tok2idx(x)
            answer[i, m+1] = self.tags.tok2idx("END")
            answer[i, m+2:] = self.tags.tok2idx("PAD")
        return answer
def _make_sent_vector(self, sent: List, bucket_length: int =None) -> np.ndarray:
        bucket_length = bucket_length or len(sent)
        answer = np.zeros(shape=(bucket_length, MAX_WORD_LENGTH+2), dtype=np.int32)
        for i, word in enumerate(sent):
            answer[i, 0] = self.tags.tok2idx("BEGIN")
            m = min(len(word), MAX_WORD_LENGTH)
            for j, x in enumerate(word[-m:]):
                answer[i, j+1] = self.symbols.tok2idx(x)
            answer[i, m+1] = self.tags.tok2idx("END")
            answer[i, m+2:] = self.tags.tok2idx("PAD")
        return answer
def _make_sent_vector ( self , sent : List , bucket_length : int = None ) -> np . ndarray : bucket_length = bucket_length or len ( sent ) answer = np . zeros ( shape = ( bucket_length , MAX_WORD_LENGTH + 2 ) , dtype = np . int32 ) for i , word in enumerate ( sent ) : answer [ i , 0 ] = self . tags . tok2idx ( "BEGIN" ) m = min ( len ( word ) , MAX_WORD_LENGTH ) for j , x in enumerate ( word [ - m : ] ) : answer [ i , j + 1 ] = self . symbols . tok2idx ( x ) answer [ i , m + 1 ] = self . tags . tok2idx ( "END" ) answer [ i , m + 2 : ] = self . tags . tok2idx ( "PAD" ) return answer
Transforms a sentence to Numpy array, which will be the network input.

        Args:
            sent: input sentence
            bucket_length: the width of the bucket

        Returns:
            A 3d array, answer[i][j][k] contains the index of k-th letter
            in j-th word of i-th input sentence.
Transforms a sentence to Numpy array which will be the network input .