pyabsa.tasks.TextClassification

Subpackages

Package Contents

Classes

TCTrainer

Trainer class for training PyABSA models

TCConfigManager

Simple object for storing attributes.

BERTTCModelList

Built-in mutable sequence.

GloVeTCModelList

Built-in mutable sequence.

TCDatasetList

Text Classification or Sentiment analysis datasets

TextClassifier

Predictor

class pyabsa.tasks.TextClassification.TCTrainer(config: pyabsa.tasks.TextClassification.configuration.tc_configuration.TCConfigManager = 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

class pyabsa.tasks.TextClassification.TCConfigManager(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_tc_config(configType: str, newitem: dict)[source]
static set_tc_config_template(newitem)[source]
static set_tc_config_base(newitem)[source]
static set_tc_config_english(newitem)[source]
static set_tc_config_chinese(newitem)[source]
static set_tc_config_multilingual(newitem)[source]
static set_tc_config_glove(newitem)[source]
static get_tc_config_template()[source]
static get_tc_config_base()[source]
static get_tc_config_english()[source]
static get_tc_config_chinese()[source]
static get_tc_config_multilingual()[source]
static get_tc_config_glove()[source]
class pyabsa.tasks.TextClassification.BERTTCModelList[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.

BERT_MLP
BERT
class pyabsa.tasks.TextClassification.GloVeTCModelList[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.

LSTM
class pyabsa.tasks.TextClassification.TCDatasetList[source]

Bases: list

Text Classification or Sentiment analysis datasets

SST1
SST5
SST2
AGNews10K
IMDB10K
SST
class pyabsa.tasks.TextClassification.TextClassifier(checkpoint=None, cal_perplexity=False, **kwargs)[source]

Bases: pyabsa.framework.prediction_class.predictor_template.InferenceModel

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

Batch predicts the sentiment of a target file using the model. :param target_file: The path to the target file. :param print_result: Whether to print the result. :param save_result: Whether to save the result. :param ignore_error: Whether to ignore errors and continue. :param defense: The adversarial defense to apply to the input text. :param **kwargs: Additional keyword arguments. :return: The predicted sentiment labels.

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

Predicts the sentiment of a text using the model. :param text: The input text. :param print_result: Whether to print the result. :param ignore_error: Whether to ignore errors and continue. :param defense: The adversarial defense to apply to the input text. :param **kwargs: Additional keyword arguments. :return: The predicted sentiment labels.

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

Predict 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 from a sentence or a list of sentences. param: text: the sentence or a list of 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)[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.TextClassification.Predictor(checkpoint=None, cal_perplexity=False, **kwargs)[source]

Bases: TextClassifier