flair.models.FewshotClassifier

class flair.models.FewshotClassifierView on GitHub

Bases: Classifier[Sentence], ABC

__init__()View on GitHub

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

Methods

__init__()	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_and_switch_to_new_task(task_name, ...[, ...])	Adds a new task to an existing TARS model.
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_current_label_dictionary()
get_current_label_type()
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.
is_current_task_multi_label()
list_existing_tasks()	Lists existing tasks in the loaded TARS model on the console.
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.
predict_zero_shot(sentences, candidate_label_set)	Make zero shot predictions from the TARS model.
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.
switch_to_task(task_name)	Switches to a task which was previously added.
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])	Populate label similarity map based on cosine similarity before running epoch.
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
tars_embeddings
training

forward_loss(data_points)View on GitHub

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

Implement this to enable training.

Return type:

tuple[Tensor, int]

property tars_embeddings

train(mode=True)View on GitHub

Populate label similarity map based on cosine similarity before running epoch.

If the num_negative_labels_to_sample is set to an integer value then before starting each epoch the model would create a similarity measure between the label names based on cosine distances between their BERT encoded embeddings.

get_current_label_dictionary()View on GitHub

get_current_label_type()View on GitHub

is_current_task_multi_label()View on GitHub

add_and_switch_to_new_task(task_name, label_dictionary, label_type, multi_label=True, force_switch=False)View on GitHub

Adds a new task to an existing TARS model.

Sets necessary attributes and finally ‘switches’ to the new task. Parameters are similar to the constructor except for model choice, batch size and negative sampling. This method does not store the resultant model onto disk.

Parameters:

• task_name (str) – a string depicting the name of the task

• label_dictionary (Union[list, set, Dictionary, str]) – dictionary of the labels you want to predict

• label_type (str) – string to identify the label type (‘ner’, ‘sentiment’, etc.)

• multi_label (bool) – whether this task is a multi-label prediction problem

• force_switch (bool) – if True, will overwrite existing task with same name

list_existing_tasks()View on GitHub

Lists existing tasks in the loaded TARS model on the console.

Return type:

set[str]

switch_to_task(task_name)View on GitHub

Switches to a task which was previously added.

property label_type

Each model predicts labels of a certain type.

predict_zero_shot(sentences, candidate_label_set, multi_label=True)View on GitHub

Make zero shot predictions from the TARS model.

Parameters:

• sentences (Union[list[Sentence], Sentence]) – input sentence objects to classify

• candidate_label_set (Union[list[str], set[str], str]) – set of candidate labels

• multi_label (bool) – indicates whether multi-label or single class prediction. Defaults to True.

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:

FewshotClassifier

Returns:

The loaded Flair model.