Text Convolutional Neural Network

class sentivi.classifier.TextCNNClassifier(num_labels: int, embedding_size: Optional[int] = None, max_length: Optional[int] = None, device: Optional[str] = 'cpu', num_epochs: Optional[int] = 10, learning_rate: Optional[float] = 0.001, batch_size: Optional[int] = 2, shuffle: Optional[bool] = True, random_state: Optional[int] = 101, *args, **kwargs)

__init__(num_labels: int, embedding_size: Optional[int] = None, max_length: Optional[int] = None, device: Optional[str] = 'cpu', num_epochs: Optional[int] = 10, learning_rate: Optional[float] = 0.001, batch_size: Optional[int] = 2, shuffle: Optional[bool] = True, random_state: Optional[int] = 101, *args, **kwargs)

Initialize TextCNNClassifier

Parameters

• num_labels – number of polarities

• embedding_size – input embedding size

• max_length – maximum length of input text

• device – training device

• num_epochs – maximum number of epochs

• learning_rate – training learning rate

• batch_size – training batch size

• shuffle – whether DataLoader is shuffle or not

• random_state – random.seed

• args – arbitrary arguments

• kwargs – arbitrary keyword arguments

forward(data, *args, **kwargs)

Training and evaluating method

Parameters

• data – TextEncoder output

• args – arbitrary arguments

• kwargs – arbitrary keyword arguments

Returns

training and evaluating results

Return type

str

predict(X, *args, **kwargs)

Predict polarity with given sentences

Parameters

• X – TextEncoder.predict output

• args – arbitrary arguments

• kwargs – arbitrary keyword arguments

Returns

list of numeric polarities

Return type

list