(
    ns: Sequence[int] = (1, 2, 3),
    embed_dim: int = 100,
    hidden_width: int = 512,
    dropout: Optional[float] = 0.1,
)
return model
Build a language identification model inspired by Google's CLD3.

    Args:
        ns: Set of "n" for which character "n"-grams are extracted from input texts.
            If 1, only unigrams (single characters) are used; if [1, 2], then both
            unigrams and bigrams are used; and so on.
        embed_dim: Size of the vectors into which each set of ngrams are embedded.
        hidden_width: Width of the dense layer with Relu activation, just before
            the final prediction (Softmax) layer.
        dropout: Dropout rate to avoid overfitting.

    Returns:
        Thinc :class:`Model`.
Build a language identification model inspired by Google's CLD3.
Build a language identification model inspired by Google s CLD3 .
def LangIdentifierModelV2(
    ns: Sequence[int] = (1, 2, 3),
    embed_dim: int = 100,
    hidden_width: int = 512,
    dropout: Optional[float] = 0.1,
) -> Model[List[str], thinc.types.Floats2d]:
    """
    Build a language identification model inspired by Google's CLD3.

    Args:
        ns: Set of "n" for which character "n"-grams are extracted from input texts.
            If 1, only unigrams (single characters) are used; if [1, 2], then both
            unigrams and bigrams are used; and so on.
        embed_dim: Size of the vectors into which each set of ngrams are embedded.
        hidden_width: Width of the dense layer with Relu activation, just before
            the final prediction (Softmax) layer.
        dropout: Dropout rate to avoid overfitting.

    Returns:
        Thinc :class:`Model`.
    """
    with Model.define_operators({">>": chain}):
        model = (
            MultiCharNgramsEmbedding(
                ns=list(ns),
                max_chars=1000,
                lower=True,
                num_vectors=[2000 * n for n in ns],
                embed_dims=embed_dim,
                dropout=dropout,
            )
            >> thinc.layers.Relu(
                nI=embed_dim * len(ns),
                nO=hidden_width,
                dropout=dropout,
            )
            >> thinc.layers.Softmax(nI=hidden_width)
        )
    return model
def LangIdentifierModelV2 ( ns : Sequence [ int ] = ( 1 , 2 , 3 ) , embed_dim : int = 100 , hidden_width : int = 512 , dropout : Optional [ float ] = 0.1 , ) -> Model [ List [ str ] , thinc . types . Floats2d ] : with Model . define_operators ( { ">>" : chain } ) : model = ( MultiCharNgramsEmbedding ( ns = list ( ns ) , max_chars = 1000 , lower = True , num_vectors = [ 2000 * n for n in ns ] , embed_dims = embed_dim , dropout = dropout , ) >> thinc . layers . Relu ( nI = embed_dim * len ( ns ) , nO = hidden_width , dropout = dropout , ) >> thinc . layers . Softmax ( nI = hidden_width ) ) return model