flair.models.SpanClassifier#
- class flair.models.SpanClassifier(embeddings, label_dictionary, pooling_operation='first_last', label_type='nel', span_label_type=None, candidates=None, **classifierargs)View on GitHub#
Bases:
DefaultClassifier
[Sentence
,Span
]Entity Linking Model.
The model expects text/sentences with annotated entity mentions and predicts entities to these mentions. To this end a word embedding is used to embed the sentences and the embedding of the entity mention goes through a linear layer to get the actual class label. The model is able to predict ‘<unk>’ for entity mentions that the model can not confidently match to any of the known labels.
- __init__(embeddings, label_dictionary, pooling_operation='first_last', label_type='nel', span_label_type=None, candidates=None, **classifierargs)View on GitHub#
Initializes an SpanClassifier.
- Parameters:
embeddings (
TokenEmbeddings
) – embeddings used to embed the tokens of the sentences.label_dictionary (
Dictionary
) – dictionary that gives ids to all classes. Should contain <unk>.pooling_operation (
str
) – either average, first, last or first_last. Specifies the way of how text representations of entity mentions (with more than one token) are handled. E.g. average means that as text representation we take the average of the embeddings of the token in the mention. first_last concatenates the embedding of the first and the embedding of the last token.label_type (
str
) – name of the label you use.span_label_type (
Optional
[str
]) – name of the label you use for inputs of predictions.candidates (
Optional
[CandidateGenerator
]) – If provided, use aCandidateGenerator
for prediction candidates.**classifierargs – The arguments propagated to
flair.nn.DefaultClassifier.__init__()
Methods
__init__
(embeddings, label_dictionary[, ...])Initializes an SpanClassifier.
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.emb_first
(span, embedding_names)emb_firstAndLast
(span, embedding_names)emb_last
(span, embedding_names)emb_mean
(span, embedding_names)eval
()Set the module in evaluation mode.
evaluate
(data_points, gold_label_type[, ...])Evaluates the model.
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.
forward_loss
(sentences)Performs a forward pass and returns a loss tensor for backpropagation.
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.get_used_tokens
(corpus[, context_length, ...])half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load
(model_path)Loads a Flair model from the given file or state dictionary.
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.
predict
(sentences[, mini_batch_size, ...])Predicts the class labels for the given sentences.
print_model_card
()This method produces a log message that includes all recorded parameters the model was trained with.
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
(model_file[, checkpoint])Saves the current model to the provided file.
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
Each model predicts labels of a certain type.
model_card
multi_label_threshold
training
- emb_first(span, embedding_names)View on GitHub#
- emb_last(span, embedding_names)View on GitHub#
- emb_firstAndLast(span, embedding_names)View on GitHub#
- emb_mean(span, embedding_names)View on GitHub#
- property label_type#
Each model predicts labels of a certain type.
- classmethod load(model_path)View on GitHub#
Loads a Flair model from the given file or state dictionary.
- Parameters:
model_path (
Union
[str
,Path
,dict
[str
,Any
]]) – Either the path to the model (as string or Path variable) or the already loaded state dict.- Return type:
- Returns:
The loaded Flair model.