pyabsa.tasks.AspectSentimentTripletExtraction

Subpackages

Package Contents

Classes

ASTEConfigManager

Simple object for storing attributes.

ASTEDatasetList

The following datasets are for aspect polarity classification task.

ASTEModelList

Built-in mutable sequence.

AspectSentimentTripletExtractor

ASTETrainer

Trainer class for training PyABSA models
class pyabsa.tasks.AspectSentimentTripletExtraction.ASTEConfigManager(args, **kwargs)[source]

Bases: pyabsa.framework.configuration_class.configuration_template.ConfigManager

Simple object for storing attributes.

Implements equality by attribute names and values, and provides a simple string representation.

static set_aste_config(configType: str, newitem: dict)[source]
static set_aste_config_template(newitem)[source]
static set_aste_config_base(newitem)[source]
static set_aste_config_english(newitem)[source]
static set_aste_config_chinese(newitem)[source]
static set_aste_config_multilingual(newitem)[source]
static get_aste_config_template()[source]
static get_aste_config_base()[source]
static get_aste_config_english()[source]
static get_aste_config_chinese()[source]
static get_aste_config_multilingual()[source]
class pyabsa.tasks.AspectSentimentTripletExtraction.ASTEDatasetList[source]

Bases: list

The following datasets are for aspect polarity classification task. The datasets are collected from different sources, you can use the id to locate the dataset.

Laptop14
Restaurant14
Restaurant15
Restaurant16
SemEval
Chinese_Zhang
Multilingual
class pyabsa.tasks.AspectSentimentTripletExtraction.ASTEModelList[source]

Bases: list

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

EMCGCN
class pyabsa.tasks.AspectSentimentTripletExtraction.AspectSentimentTripletExtractor(checkpoint=None, **kwargs)[source]

Bases: pyabsa.framework.prediction_class.predictor_template.InferenceModel

task_code
batch_infer(target_file=None, print_result=True, save_result=False, ignore_error=True, **kwargs)[source]

A deprecated version of batch_predict method.

Parameters:

  • target_file (str) – the path to the target file for inference

  • print_result (bool) – whether to print the result

  • save_result (bool) – whether to save the result

  • ignore_error (bool) – whether to ignore the error

Returns:

a dictionary of the results

Return type:

result (dict)

infer(text: str = None, print_result=True, ignore_error=True, **kwargs)[source]

A deprecated version of the predict method.

Parameters:

  • text (str) – the text to predict

  • print_result (bool) – whether to print the result

  • ignore_error (bool) – whether to ignore the error

Returns:

a dictionary of the results

Return type:

result (dict)

batch_predict(target_file=None, print_result=True, save_result=False, ignore_error=True, **kwargs)[source]

Predict the sentiment from a file of sentences. param: target_file: the file path of the sentences to be predicted. param: print_result: whether to print the result. param: save_result: whether to save the result. param: ignore_error: whether to ignore the error when predicting. param: kwargs: other parameters.

predict(text: str | list = None, print_result=True, ignore_error=True, **kwargs)[source]

Predict the sentiment from a sentence or a list of sentences. param: text: the sentence to be predicted. param: print_result: whether to print the result. param: ignore_error: whether to ignore the error when predicting. param: kwargs: other parameters.

_run_prediction(save_path=None, print_result=True, **kwargs)[source]

This method should be implemented in the subclass for running predictions using the trained model.

Parameters:

kwargs – additional keyword arguments

Returns:

predicted labels or other prediction outputs

clear_input_samples()[source]
class pyabsa.tasks.AspectSentimentTripletExtraction.ASTETrainer(config: pyabsa.tasks.AspectSentimentTripletExtraction.configuration.configuration.ASTEConfigManager = None, dataset=None, from_checkpoint: str = None, checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, auto_device: bool | str = DeviceTypeOption.AUTO, path_to_save=None, load_aug=False)[source]

Bases: pyabsa.framework.trainer_class.trainer_template.Trainer

Trainer class for training PyABSA models