flair.nn.DefaultClassifier#

class flair.nn.DefaultClassifier(embeddings, label_dictionary, final_embedding_size, dropout=0.0, locked_dropout=0.0, word_dropout=0.0, multi_label=False, multi_label_threshold=0.5, loss_weights=None, decoder=None, inverse_model=False, train_on_gold_pairs_only=False, should_embed_sentence=True)View on GitHub#

Bases: Classifier[DT], Generic[DT, DT2], ABC

Default base class for all Flair models that do classification.

It inherits from flair.nn.Classifier and thus from flair.nn.Model. All features shared by all classifiers are implemented here, including the loss calculation, prediction heads for both single- and multi- label classification and the predict() method. Example implementations of this class are the TextClassifier, RelationExtractor, TextPairClassifier and TokenClassifier.

__init__(embeddings, label_dictionary, final_embedding_size, dropout=0.0, locked_dropout=0.0, word_dropout=0.0, multi_label=False, multi_label_threshold=0.5, loss_weights=None, decoder=None, inverse_model=False, train_on_gold_pairs_only=False, should_embed_sentence=True)View on GitHub#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(embeddings, 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.

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 multi_label_threshold#
forward_loss(sentences)View on GitHub#

Performs a forward pass and returns a loss tensor for backpropagation.

Implement this to enable training.

Return type:

tuple[Tensor, int]

predict(sentences, mini_batch_size=32, return_probabilities_for_all_classes=False, verbose=False, label_name=None, return_loss=False, embedding_storage_mode='none')View on GitHub#

Predicts the class labels for the given sentences. The labels are directly added to the sentences.

Parameters:

  • sentences (Union[list[TypeVar(DT, bound= DataPoint)], TypeVar(DT, bound= DataPoint)]) – list of sentences to predict

  • mini_batch_size (int) – the amount of sentences that will be predicted within one batch

  • return_probabilities_for_all_classes (bool) – return probabilities for all classes instead of only best predicted

  • verbose (bool) – set to True to display a progress bar

  • return_loss (bool) – set to True to return loss

  • label_name (Optional[str]) – set this to change the name of the label type that is predicted

  • embedding_storage_mode (Literal['none', 'cpu', 'gpu']) – default is ‘none’ which is the best is most cases. Only set to ‘cpu’ or ‘gpu’ if you wish to not only predict, but also keep the generated embeddings in CPU or GPU memory respectively. ‘gpu’ to store embeddings in GPU memory.

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:

DefaultClassifier

Returns:

The loaded Flair model.