`pyabsa.tasks.AspectPolarityClassification`

Subpackages

Package Contents

Classes

`APCTrainer`	Trainer class for training PyABSA models
`APCConfigManager`	Simple object for storing attributes.
`APCModelList`	Built-in mutable sequence.
`BERTBaselineAPCModelList`	Built-in mutable sequence.
`GloVeAPCModelList`	Built-in mutable sequence.
`LCFAPCModelList`	Built-in mutable sequence.
`PLMAPCModelList`	Built-in mutable sequence.
`ClassicAPCModelList`	Built-in mutable sequence.
`APCDatasetList`	The following datasets are for aspect polarity classification task.
`SentimentClassifier`
`Predictor`

class pyabsa.tasks.AspectPolarityClassification.APCTrainer(config: pyabsa.tasks.AspectPolarityClassification.configuration.apc_configuration.APCConfigManager = None, dataset=None, from_checkpoint: str = None, checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, auto_device: Union[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.AspectPolarityClassification.APCConfigManager(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_apc_config(configType: str, newitem: dict)

static set_apc_config_template(newitem)

static set_apc_config_base(newitem)

static set_apc_config_english(newitem)

static set_apc_config_chinese(newitem)

static set_apc_config_multilingual(newitem)

static set_apc_config_glove(newitem)

static set_apc_config_bert_baseline(newitem)

static get_apc_config_template()

static get_apc_config_base()

static get_apc_config_english()

static get_apc_config_chinese()

static get_apc_config_multilingual()

static get_apc_config_glove()

static get_apc_config_bert_baseline()

class pyabsa.tasks.AspectPolarityClassification.APCModelList

Bases: LCFAPCModelList

Built-in mutable sequence.

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

class pyabsa.tasks.AspectPolarityClassification.BERTBaselineAPCModelList

Bases: PLMAPCModelList

Built-in mutable sequence.

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

class pyabsa.tasks.AspectPolarityClassification.GloVeAPCModelList

Bases: ClassicAPCModelList

Built-in mutable sequence.

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

class pyabsa.tasks.AspectPolarityClassification.LCFAPCModelList

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.

SLIDE_LCF_BERT

SLIDE_LCFS_BERT

LSA_T

LSA_S

FAST_LSA_T

FAST_LSA_S

FAST_LSA_T_V2

FAST_LSA_S_V2

DLCF_DCA_BERT

DLCFS_DCA_BERT

LCF_BERT

FAST_LCF_BERT

LCF_DUAL_BERT

LCFS_BERT

FAST_LCFS_BERT

LCFS_DUAL_BERT

LCA_BERT

BERT_MLP

BERT_SPC

BERT_SPC_V2

FAST_LCF_BERT_ATT

LCF_TEMPLATE_BERT

class pyabsa.tasks.AspectPolarityClassification.PLMAPCModelList

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.

AOA_BERT

ASGCN_BERT

ATAE_LSTM_BERT

Cabasc_BERT

IAN_BERT

LSTM_BERT

MemNet_BERT

MGAN_BERT

RAM_BERT

TC_LSTM_BERT

TD_LSTM_BERT

TNet_LF_BERT

__str__(): Return str(self).

class pyabsa.tasks.AspectPolarityClassification.ClassicAPCModelList

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.

AOA

ASGCN

ATAE_LSTM

Cabasc

IAN

LSTM

MemNet

MGAN

RAM

TC_LSTM

TD_LSTM

TNet_LF

class pyabsa.tasks.AspectPolarityClassification.APCDatasetList[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

ARTS_Laptop14

ARTS_Restaurant14

Restaurant15

Restaurant16

ACL_Twitter

MAMS

Television

TShirt

Yelp

Phone

Car

Notebook

Camera

Shampoo

MOOC

MOOC_En

Kaggle

Chinese_Zhang

Chinese

Binary_Polarity_Chinese

Triple_Polarity_Chinese

SemEval2016Task5

Arabic_SemEval2016Task5

Dutch_SemEval2016Task5

Spanish_SemEval2016Task5

Turkish_SemEval2016Task5

Russian_SemEval2016Task5

French_SemEval2016Task5

English_SemEval2016Task5

English

SemEval

Restaurant

Multilingual

class pyabsa.tasks.AspectPolarityClassification.SentimentClassifier(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)

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)

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): 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: Union[str, list] = None, print_result=True, ignore_error=True, **kwargs): 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.

merge_results(results): merge APC results have the same input text

_run_prediction(save_path=None, print_result=True, **kwargs)

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()

class pyabsa.tasks.AspectPolarityClassification.Predictor(checkpoint=None, **kwargs)[source]: Bases: SentimentClassifier

pyabsa.tasks.AspectPolarityClassification

Subpackages

Package Contents

Classes

`pyabsa.tasks.AspectPolarityClassification`