flair.models.RelationExtractor

class flair.models.RelationExtractor(embeddings, label_type, entity_label_type, entity_pair_filters=None, pooling_operation='first_last', train_on_gold_pairs_only=False, **classifierargs)View on GitHub

Bases: DefaultClassifier[Sentence, Relation]

__init__(embeddings, label_type, entity_label_type, entity_pair_filters=None, pooling_operation='first_last', train_on_gold_pairs_only=False, **classifierargs)View on GitHub

Initializes a RelationClassifier.

Parameters:

• embeddings (TokenEmbeddings) – embeddings used to embed each data point

• label_type (str) – name of the label

• entity_label_type (str) – name of the labels used to represent entities

• entity_pair_filters (Optional[list[tuple[str, str]]]) – if provided, only classify pairs that apply the filter

• pooling_operation (str) – either “first” or “first_last” how the embeddings of the entities should be used to create relation embeddings

• train_on_gold_pairs_only (bool) – if True, relations with “O” (no relation) label will be ignored in training.

• **classifierargs – The arguments propagated to flair.nn.DefaultClassifier.__init__()

Methods

__init__(embeddings, label_type, ...[, ...])	Initializes a RelationClassifier.
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.
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
label_type	Each model predicts labels of a certain type.
model_card
multi_label_threshold
training

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:

RelationExtractor

Returns:

The loaded Flair model.