BERT Multi-label Classification Learner

The node uses BERT model and adds a predefined neural network on top. There are 3 layers added:

• GlobalAveragePooling1D layer
• Dropout layer
• Dense layer

Options

Settings

Sentence column

A column with a plain text (String), that contains text to be classified. No special pre-processing is needed.

Class column

A column that contains class labels.

Max sequence length

The maximum length of a sequence after tokenization, limit is 512.

Advanced

Number of epochs

The number of epochs used for training the classifier.

Batch size

The size of a chunk of the input data used for model update.

Validation batch size

The size of a chunk of the validation data to process.

Fine tune BERT

If checked than BERT model will be trained along with the additional classifier. It takes longer time to fine tune BERT, but the results are usually better.

Optimizer

Available optimizers and their configuration.

Python

Python

Select one of Python execution environment options:

• use default Python environment for Deep Learning
• use Conda environment

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Workflows

• Multalabel_classification_complaints_caseNodePit Space
• Multilabel_classification_stackoverflow_caseNodePit Space

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Developers