flair.nn.LabelVerbalizerDecoder#
- class flair.nn.LabelVerbalizerDecoder(label_embedding, label_dictionary)View on GitHub#
Bases:
Module
A class for decoding labels using the idea of siamese networks / bi-encoders. This can be used for all classification tasks in flair.
- Parameters:
label_encoder (flair.embeddings.TokenEmbeddings) – The label encoder used to encode the labels into an embedding.
label_dictionary (flair.data.Dictionary) – The label dictionary containing the mapping between labels and indices.
- label_encoder#
The label encoder used to encode the labels into an embedding.
- Type:
flair.embeddings.TokenEmbeddings
- label_dictionary#
The label dictionary containing the mapping between labels and indices.
- Type:
- forward(self, label_embeddings
torch.Tensor, context_embeddings: torch.Tensor) -> torch.Tensor: Takes the label embeddings and context embeddings as input and returns a tensor of label scores.
Examples
label_dictionary = corpus.make_label_dictionary(“ner”) label_encoder = TransformerWordEmbeddings(‘bert-base-ucnased’) label_verbalizer_decoder = LabelVerbalizerDecoder(label_encoder, label_dictionary)
- __init__(label_embedding, label_dictionary)View on GitHub#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Methods
__init__
(label_embedding, label_dictionary)Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module's forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
extra_repr
()Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.forward
(inputs)Forward pass of the label verbalizer decoder.
get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.get_extra_state
()Return any extra state to include in the module's state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, ...])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children
()Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, ...])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, ...])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, ...])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
register_load_state_dict_post_hook
(hook)Register a post-hook to be run after module's
load_state_dict()
is called.register_load_state_dict_pre_hook
(hook)Register a pre-hook to be run before module's
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
register_state_dict_post_hook
(hook)Register a post-hook for the
state_dict()
method.register_state_dict_pre_hook
(hook)Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.share_memory
()See
torch.Tensor.share_memory_()
.state_dict
(*args[, destination, prefix, ...])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.verbalize_labels
(label_dictionary)Takes a label dictionary and returns a list of sentences with verbalized labels.
xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes
T_destination
call_super_init
dump_patches
training
- static verbalize_labels(label_dictionary)View on GitHub#
Takes a label dictionary and returns a list of sentences with verbalized labels.
- Parameters:
label_dictionary (flair.data.Dictionary) – The label dictionary to verbalize.
- Return type:
list
[Sentence
]- Returns:
A list of sentences with verbalized labels.
Examples
label_dictionary = corpus.make_label_dictionary(“ner”) verbalized_labels = LabelVerbalizerDecoder.verbalize_labels(label_dictionary) print(verbalized_labels) [Sentence: “begin person”, Sentence: “inside person”, Sentence: “end person”, Sentence: “single org”, …]
- forward(inputs)View on GitHub#
Forward pass of the label verbalizer decoder.
- Parameters:
inputs (torch.Tensor) – The input tensor.
- Return type:
Tensor
- Returns:
The scores of the decoder.
- Raises:
RuntimeError – If an unknown decoding type is specified.