flair.models.MultitaskModel#

class flair.models.MultitaskModel(models, task_ids=None, loss_factors=None, use_all_tasks=False)View on GitHub#

Bases: Classifier

Multitask Model class which acts as wrapper for creating custom multitask models.

Takes different tasks as input, parameter sharing is done by objects in flair, i.e. creating a Embedding Layer and passing it to two different Models, will result in a hard parameter-shared embedding layer. The abstract class takes care of calling the correct forward propagation and loss function of the respective model.

__init__(models, task_ids=None, loss_factors=None, use_all_tasks=False)View on GitHub#

Instantiates the MultiTaskModel.

Parameters:

  • models (list[Classifier]) – The child models used during multitask training.

  • task_ids (Optional[list[str]]) – If given, add each corresponding model a specified task id. Otherwise, tasks get the ids ‘Task_0’, ‘Task_1’, …

  • loss_factors (Optional[list[float]]) – If given, weight the losses of teh corresponding models during training.

  • use_all_tasks (bool) – If True, each sentence will be trained on all tasks parallel, otherwise each epoch 1 task will be sampled to train the sentence on.

Methods

__init__(models[, task_ids, loss_factors, ...])

Instantiates the MultiTaskModel.

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(*args)

Define the computation performed at every call.

forward_loss(sentences)

Calls the respective forward loss of each model and sums them weighted by their loss factors.

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, **predictargs)

Uses the model to predict labels for a given set of data points.

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_().

split_batch_to_task_ids(sentences[, all_tasks])

Splits a batch of sentences to its respective model.

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

training

forward(*args)View on GitHub#

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_loss(sentences)View on GitHub#

Calls the respective forward loss of each model and sums them weighted by their loss factors.

Parameters:

sentences (Union[list[Sentence], Sentence]) – batch of sentences

Return type:

tuple[Tensor, int]

Returns: loss and sample count

predict(sentences, **predictargs)View on GitHub#

Uses the model to predict labels for a given set of data points.

The method does not directly return the predicted labels. Rather, labels are added as flair.data.Label objects to the respective data points. You can then access these predictions by calling flair.data.DataPoint.get_labels() on each data point that you passed through this method.

Parameters:

  • sentences – The data points for which the model should predict labels, most commonly Sentence objects.

  • mini_batch_size – The mini batch size to use. Setting this value higher typically makes predictions faster, but also costs more memory.

  • return_probabilities_for_all_classes – If set to True, the model will store probabilities for all classes instead of only the predicted class.

  • verbose – If set to True, will display a progress bar while predicting. By default, this parameter is set to False.

  • return_loss – Set this to True to return loss (only possible if gold labels are set for the sentences).

  • label_name – Optional parameter that if set, changes the identifier of the label type that is predicted. # noqa: E501

  • embedding_storage_mode – Default is ‘none’ which is always best. 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. # noqa: E501

static split_batch_to_task_ids(sentences, all_tasks=False)View on GitHub#

Splits a batch of sentences to its respective model.

If single sentence is assigned to several tasks (i.e. same corpus but different tasks), then the model assignment for this batch is randomly chosen.

Parameters:

  • sentences (Union[list[Sentence], Sentence]) – batch of sentences

  • all_tasks (bool) – use all tasks of each sentence. If deactivated, a random task will be sampled

Return type:

dict[str, list[int]]

Returns: Key-value pairs as (task_id, list of sentences ids in batch)

evaluate(data_points, gold_label_type, out_path=None, main_evaluation_metric=('micro avg', 'f1-score'), evaluate_all=True, **evalargs)View on GitHub#

Evaluates the model. Returns a Result object containing evaluation results and a loss value.

Parameters:

  • data_points – batch of sentences

  • gold_label_type (str) – if evaluate_all is False, specify the task to evaluate by the task_id.

  • out_path (Union[str, Path, None]) – if not None, predictions will be created and saved at the respective file.

  • main_evaluation_metric (tuple[str, str]) – Specify which metric to highlight as main_score

  • evaluate_all (bool) – choose if all tasks should be evaluated, or a single one, depending on gold_label_type

  • **evalargs – arguments propagated to flair.nn.Model.evaluate()

Return type:

Result

Returns: Tuple of Result object and loss value (float)

get_used_tokens(corpus, context_length=0, respect_document_boundaries=True)View on GitHub#
Return type:

Iterable[list[str]]

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:

MultitaskModel

Returns:

The loaded Flair model.