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:

flair.data.Dictionary

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()	Return 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.