flair.embeddings.legacy.DocumentLSTMEmbeddings#
- class flair.embeddings.legacy.DocumentLSTMEmbeddings(embeddings, hidden_size=128, rnn_layers=1, reproject_words=True, reproject_words_dimension=None, bidirectional=False, dropout=0.5, word_dropout=0.0, locked_dropout=0.0)View on GitHub#
Bases:
DocumentEmbeddings
- __init__(embeddings, hidden_size=128, rnn_layers=1, reproject_words=True, reproject_words_dimension=None, bidirectional=False, dropout=0.5, word_dropout=0.0, locked_dropout=0.0)View on GitHub#
The constructor takes a list of embeddings to be combined.
- Parameters:
embeddings (
list
[TokenEmbeddings
]) – a list of token embeddingshidden_size – the number of hidden states in the lstm
rnn_layers – the number of layers for the lstm
reproject_words (
bool
) – boolean value, indicating whether to reproject the token embeddings in a separate linear layer before putting them into the lstm or not.reproject_words_dimension (
Optional
[int
]) – output dimension of reprojecting token embeddings. If None the same output dimension as before will be taken.bidirectional (
bool
) – boolean value, indicating whether to use a bidirectional lstm or notdropout (
float
) – the dropout value to be usedword_dropout (
float
) – the word dropout value to be used, if 0.0 word dropout is not usedlocked_dropout (
float
) – the locked dropout value to be used, if 0.0 locked dropout is not used.
Deprecated since version 0.4: The functionality of this class is moved to ‘DocumentRNNEmbeddings’
Methods
__init__
(embeddings[, hidden_size, ...])The constructor takes a list of embeddings to be combined.
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.embed
(sentences)Add embeddings to all sentences in the given list of sentences.
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
(*input)Define the computation performed at every call.
from_params
(params)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_instance_parameters
(locals)get_names
()Returns a list of embedding names.
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_embedding
(params)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.
save_embeddings
([use_state_dict])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.
to_params
()train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.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
Returns the length of the embedding vector.
embedding_type
embeddings_name
training
- property embedding_length: int#
Returns the length of the embedding vector.
- embed(sentences)View on GitHub#
Add embeddings to all sentences in the given list of sentences. If embeddings are already added, update only if embeddings are non-static.