modelzoo.transformers.pytorch.bert.fine_tuning.classifier.input.BertClassifierDataProcessor.SST2Dataset#

class modelzoo.transformers.pytorch.bert.fine_tuning.classifier.input.BertClassifierDataProcessor.SST2Dataset[source]#

Bases: modelzoo.transformers.pytorch.bert.fine_tuning.classifier.input.BertClassifierDataProcessor.ClassifierDataset

SST2 dataset processor for sentiment analysis.

Parameters

  • params (dict) – List of training input parameters for creating dataset.

  • is_training (bool) – Indicator for training or validation dataset.

Methods

encode_sequence

Tokenizes a single text (if text2 is None) or a pair of texts.

read_tsv

__call__(*args: Any, **kwargs: Any) → Any#

Call self as a function.

__init__(params, is_training)[source]#
static __new__(cls, *args: Any, **kwargs: Any) → Any#
encode_sequence(text1, text2=None)#

Tokenizes a single text (if text2 is None) or a pair of texts. Truncates and adds special tokens as needed.

Parameters

  • text1 (str) – First text to encode.

  • text2 (str) – Second text to encode or None.

Returns

A list for input_ids, segment_ids and attention_mask. - input_ids (np.array[int.32]): Numpy array with input token indices.

Shape: (max_sequence_length).

  • segment_ids (np.array[int.32]): Numpy array with segment indices.

    Shape: (max_sequence_length).

  • attention_mask (np.array[int.32]): Numpy array with input masks.

    Shape: (max_sequence_length).

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