(
    n: int,
    max_chars: int,
    lower: bool,
    num_vectors: int,
    embed_dim: int,
    dropout: Optional[float],
)
return model
Args:
        n
        max_chars
        lower
        num_vectors
        embed_dim
        dropout
Args:
        n
        max_chars
        lower
        num_vectors
        embed_dim
        dropout
Args : n max_chars lower num_vectors embed_dim dropout
def CharNgramsEmbedding(
    n: int,
    max_chars: int,
    lower: bool,
    num_vectors: int,
    embed_dim: int,
    dropout: Optional[float],
) -> Model[List[str], thinc.types.Floats1d]:
    """
    Args:
        n
        max_chars
        lower
        num_vectors
        embed_dim
        dropout
    """
    with Model.define_operators({">>": chain}):
        model = (
            text_to_char_ngrams(n, max_chars, lower)
            >> thinc.layers.strings2arrays()
            >> thinc.layers.with_array(
                thinc.layers.HashEmbed(
                    nO=embed_dim,
                    nV=num_vectors,
                    dropout=dropout,
                    column=0,
                )
            )
            >> thinc.layers.list2ragged()
            >> thinc.layers.reduce_mean()
        )
    return model
def CharNgramsEmbedding ( n : int , max_chars : int , lower : bool , num_vectors : int , embed_dim : int , dropout : Optional [ float ] , ) -> Model [ List [ str ] , thinc . types . Floats1d ] : with Model . define_operators ( { ">>" : chain } ) : model = ( text_to_char_ngrams ( n , max_chars , lower ) >> thinc . layers . strings2arrays ( ) >> thinc . layers . with_array ( thinc . layers . HashEmbed ( nO = embed_dim , nV = num_vectors , dropout = dropout , column = 0 , ) ) >> thinc . layers . list2ragged ( ) >> thinc . layers . reduce_mean ( ) ) return model