(
    name: str,
    get_spans,
    tokenizer_config: dict,
    pooling: Model[Ragged, Floats2d],
    grad_factor: float = 1.0,
)
return chain(
        TransformerModel(name, get_spans, tokenizer_config),
        split_trf_batch(),
        trfs2arrays(pooling, grad_factor),
    )
Use a transformer as a "Tok2Vec" layer directly. This does not allow
    multiple components to share the transformer weights, and does not allow
    the transformer to set annotations into the `Doc` object, but it's a
    simpler solution if you only need the transformer within one component.

    get_spans (Callable[[List[Doc]], List[List[Span]]]): A function to extract
        spans from the batch of Doc objects. See the "TransformerModel" layer
        for details.
    tokenizer_config (dict): Settings to pass to the transformers tokenizer.
    pooling (Model[Ragged, Floats2d]): A reduction layer used to calculate
        the token vectors based on zero or more wordpiece vectors. If in doubt,
        mean pooling (see `thinc.layers.reduce_mean`) is usually a good choice.
     grad_factor (float): Reweight gradients from the component before passing
        them to the transformer. You can set this to 0 to "freeze" the transformer
        weights with respect to the component, or to make it learn more slowly.
        Leaving it at 1.0 is usually fine.
Use a transformer as a "Tok2Vec" layer directly. This does not allow
    multiple components to share the transformer weights, and does not allow
    the transformer to set annotations into the `Doc` object, but it's a
    simpler solution if you only need the transformer within one component.
Use a transformer as a Tok2Vec layer directly . This does not allow multiple components to share the transformer weights and does not allow the transformer to set annotations into the Doc object but it s a simpler solution if you only need the transformer within one component .
def transformer_tok2vec_v1(
    name: str,
    get_spans,
    tokenizer_config: dict,
    pooling: Model[Ragged, Floats2d],
    grad_factor: float = 1.0,
) -> Model[List[Doc], List[Floats2d]]:
    """Use a transformer as a "Tok2Vec" layer directly. This does not allow
    multiple components to share the transformer weights, and does not allow
    the transformer to set annotations into the `Doc` object, but it's a
    simpler solution if you only need the transformer within one component.

    get_spans (Callable[[List[Doc]], List[List[Span]]]): A function to extract
        spans from the batch of Doc objects. See the "TransformerModel" layer
        for details.
    tokenizer_config (dict): Settings to pass to the transformers tokenizer.
    pooling (Model[Ragged, Floats2d]): A reduction layer used to calculate
        the token vectors based on zero or more wordpiece vectors. If in doubt,
        mean pooling (see `thinc.layers.reduce_mean`) is usually a good choice.
     grad_factor (float): Reweight gradients from the component before passing
        them to the transformer. You can set this to 0 to "freeze" the transformer
        weights with respect to the component, or to make it learn more slowly.
        Leaving it at 1.0 is usually fine.
    """
    return chain(
        TransformerModel(name, get_spans, tokenizer_config),
        split_trf_batch(),
        trfs2arrays(pooling, grad_factor),
    )
def transformer_tok2vec_v1 ( name : str , get_spans , tokenizer_config : dict , pooling : Model [ Ragged , Floats2d ] , grad_factor : float = 1.0 , ) -> Model [ List [ Doc ] , List [ Floats2d ] ] : return chain ( TransformerModel ( name , get_spans , tokenizer_config ) , split_trf_batch ( ) , trfs2arrays ( pooling , grad_factor ) , )