(
    name: str,
    get_spans: Callable,
    tokenizer_config: dict = {},
    transformer_config: dict = {},
    mixed_precision: bool = False,
    grad_scaler_config: dict = {},
)
return model
Pretrained transformer model that can be finetuned for downstream tasks.

    name (str): Name of the pretrained Huggingface model to use.
    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.
    transformers_config (dict): Settings to pass to the transformers forward pass
        of the transformer.
    mixed_precision (bool): Enable mixed-precision. Mixed-precision replaces
        whitelisted ops to half-precision counterparts. This speeds up training
        and prediction on modern GPUs and reduces GPU memory use.
    grad_scaler_config (dict): Configuration for gradient scaling in mixed-precision
        training. Gradient scaling is enabled automatically when mixed-precision
        training is used.

        Setting `enabled` to `False` in the gradient scaling configuration disables
        gradient scaling. The `init_scale` (default: `2 ** 16`) determines the
        initial scale. `backoff_factor` (default: `0.5`) specifies the factor
        by which the scale should be reduced when gradients overflow.
        `growth_interval` (default: `2000`) configures the number of steps
        without gradient overflows after which the scale should be increased.
        Finally, `growth_factor` (default: `2.0`) determines the factor by which
        the scale should be increased when no overflows were found for
        `growth_interval` steps.
Pretrained transformer model that can be finetuned for downstream tasks.
Pretrained transformer model that can be finetuned for downstream tasks .
def create_TransformerModel_v3(
    name: str,
    get_spans: Callable,
    tokenizer_config: dict = {},
    transformer_config: dict = {},
    mixed_precision: bool = False,
    grad_scaler_config: dict = {},
) -> Model[List[Doc], "FullTransformerBatch"]:
    """Pretrained transformer model that can be finetuned for downstream tasks.

    name (str): Name of the pretrained Huggingface model to use.
    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.
    transformers_config (dict): Settings to pass to the transformers forward pass
        of the transformer.
    mixed_precision (bool): Enable mixed-precision. Mixed-precision replaces
        whitelisted ops to half-precision counterparts. This speeds up training
        and prediction on modern GPUs and reduces GPU memory use.
    grad_scaler_config (dict): Configuration for gradient scaling in mixed-precision
        training. Gradient scaling is enabled automatically when mixed-precision
        training is used.

        Setting `enabled` to `False` in the gradient scaling configuration disables
        gradient scaling. The `init_scale` (default: `2 ** 16`) determines the
        initial scale. `backoff_factor` (default: `0.5`) specifies the factor
        by which the scale should be reduced when gradients overflow.
        `growth_interval` (default: `2000`) configures the number of steps
        without gradient overflows after which the scale should be increased.
        Finally, `growth_factor` (default: `2.0`) determines the factor by which
        the scale should be increased when no overflows were found for
        `growth_interval` steps.
    """
    model = TransformerModel(
        name,
        get_spans,
        tokenizer_config,
        transformer_config,
        mixed_precision,
        grad_scaler_config,
    )
    return model
def create_TransformerModel_v3 ( name : str , get_spans : Callable , tokenizer_config : dict = { } , transformer_config : dict = { } , mixed_precision : bool = False , grad_scaler_config : dict = { } , ) -> Model [ List [ Doc ] , "FullTransformerBatch" ] : model = TransformerModel ( name , get_spans , tokenizer_config , transformer_config , mixed_precision , grad_scaler_config , ) return model