cached_transformers
allennlp.common.cached_transformers
TransformerSpec¶
class TransformerSpec(NamedTuple)
model_name¶
class TransformerSpec(NamedTuple):
| ...
| model_name: str = None
override_weights_file¶
class TransformerSpec(NamedTuple):
| ...
| override_weights_file: Optional[str] = None
override_weights_strip_prefix¶
class TransformerSpec(NamedTuple):
| ...
| override_weights_strip_prefix: Optional[str] = None
get¶
def get(
model_name: str,
make_copy: bool,
override_weights_file: Optional[str] = None,
override_weights_strip_prefix: Optional[str] = None,
load_weights: bool = True,
**kwargs
) -> transformers.PreTrainedModel
Returns a transformer model from the cache.
Parameters¶
- model_name :
str
The name of the transformer, for example"bert-base-cased"
- make_copy :
bool
If this isTrue
, return a copy of the model instead of the cached model itself. If you want to modify the parameters of the model, set this toTrue
. If you want only part of the model, set this toFalse
, but make sure tocopy.deepcopy()
the bits you are keeping. - override_weights_file :
str
, optional (default =None
)
If set, this specifies a file from which to load alternate weights that override the weights from huggingface. The file is expected to contain a PyTorchstate_dict
, created withtorch.save()
. - override_weights_strip_prefix :
str
, optional (default =None
)
If set, strip the given prefix from the state dict when loading it. - load_weights :
bool
, optional (default =True
)
If set toFalse
, no weights will be loaded. This is helpful when you only want to initialize the architecture, like when you've already fine-tuned a model and are going to load the weights from a state dict elsewhere.
get_tokenizer¶
def get_tokenizer(
model_name: str,
**kwargs
) -> transformers.PreTrainedTokenizer