flair.embeddings.token.FlairEmbeddings#
- class flair.embeddings.token.FlairEmbeddings(model, fine_tune=False, chars_per_chunk=512, with_whitespace=True, tokenized_lm=True, is_lower=False, name=None, has_decoder=False)View on GitHub#
Bases:
TokenEmbeddings
Contextual string embeddings of words, as proposed in Akbik et al., 2018.
- __init__(model, fine_tune=False, chars_per_chunk=512, with_whitespace=True, tokenized_lm=True, is_lower=False, name=None, has_decoder=False)View on GitHub#
Initializes contextual string embeddings using a character-level language model.
- Parameters:
model – model string, one of ‘news-forward’, ‘news-backward’, ‘news-forward-fast’, ‘news-backward-fast’, ‘mix-forward’, ‘mix-backward’, ‘german-forward’, ‘german-backward’, ‘polish-backward’, ‘polish-forward’ depending on which character language model is desired.
fine_tune (
bool
) – if set to True, the gradient will propagate into the language model. This dramatically slows down training and often leads to overfitting, so use with caution.chars_per_chunk (
int
) – max number of chars per rnn pass to control speed/memory tradeoff. Higher means faster but requires more memory. Lower means slower but less memory.with_whitespace (
bool
) – If True, use hidden state after whitespace after word. If False, use hidden state at last character of word.tokenized_lm (
bool
) – Whether this lm is tokenized. Default is True, but for LMs trained over unprocessed text False might be better.has_decoder (
bool
) – Weather to load the decoder-head of the LanguageModel. This should only be true, if you intend to generate text.is_lower (
bool
) – Whether this lm is trained on lower-cased data.name (
Optional
[str
]) – The name of the embeddings
Methods
__init__
(model[, fine_tune, ...])Initializes contextual string embeddings using a character-level language model.
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
(data_points)Add embeddings to all words in a 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.
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
training
- train(mode=True)View on GitHub#
Set the module in training mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout
,BatchNorm
, etc.- Parameters:
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns:
self
- Return type:
Module
- property embedding_length: int#
Returns the length of the embedding vector.
- to_params()View on GitHub#
- classmethod from_params(params)View on GitHub#
- embeddings_name: str = 'FlairEmbeddings'#