flair.nn.Classifier#

class flair.nn.Classifier(*args, **kwargs)View on GitHub#

Bases: Model[DT], Generic[DT], ReduceTransformerVocabMixin, ABC

Abstract base class for all Flair models that do classification.

The classifier inherits from flair.nn.Model and adds unified functionality for both, single- and multi-label classification and evaluation. Therefore, it is ensured to have a fair comparison between multiple classifiers.

__init__(*args, **kwargs)View on GitHub#

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

Methods

__init__(*args, **kwargs)

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(data_points)

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, ...])

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_().

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

evaluate(data_points, gold_label_type, out_path=None, embedding_storage_mode='none', mini_batch_size=32, main_evaluation_metric=('micro avg', 'f1-score'), exclude_labels=None, gold_label_dictionary=None, return_loss=True, **kwargs)View on GitHub#

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

Implement this to enable evaluation.

Parameters:

  • data_points (Union[list[TypeVar(DT, bound= DataPoint)], Dataset]) – The labeled data_points to evaluate.

  • gold_label_type (str) – The label type indicating the gold labels

  • out_path (Union[str, Path, None]) – Optional output path to store predictions.

  • embedding_storage_mode (Literal['none', 'cpu', 'gpu']) – One of ‘none’, ‘cpu’ or ‘gpu’. ‘none’ means all embeddings are deleted and freshly recomputed, ‘cpu’ means all embeddings are stored on CPU, or ‘gpu’ means all embeddings are stored on GPU

  • mini_batch_size (int) – The batch_size to use for predictions.

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

  • exclude_labels (Optional[list[str]]) – Specify classes that won’t be considered in evaluation.

  • gold_label_dictionary (Optional[Dictionary]) – Specify which classes should be considered, all other classes will be taken as <unk>.

  • return_loss (bool) – Weather to additionally compute the loss on the data-points.

  • **kwargs – Arguments that will be ignored.

Return type:

Result

Returns:

The evaluation results.

abstract 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#

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 (Union[list[TypeVar(DT, bound= DataPoint)], TypeVar(DT, bound= DataPoint)]) – The data points for which the model should predict labels, most commonly Sentence objects.

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

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

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

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

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

  • embedding_storage_mode (Literal['none', 'cpu', 'gpu']) – 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

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

Iterable[list[str]]

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:

Classifier

Returns:

The loaded Flair model.