BERT Predictor

Applies a BERT Classification model to the input table.

Options

Settings

Sentence column

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

Batch size

The number of rows to feed to the model simultaniously. During prediction it is typically possible to use a larger batch size than during model training.

Change prediction column name

If active a column with provided name will be created in the output table. Otherwise the default name will be used for the column with predictions.

Append individual class probabilities

If active the columns with class probabilities will be created in the output table.

Suffix for probability columns

If not empty the suffix will be added to every column name for class probabilities columns.

Multi-label

Use custom threshold for assigning the classes

If active it is possible to change the probability threshold for assigning the class. By default class is assigned by the biggest probability value.

Probability threshold

The class is assigned if the class probability is equal or higher then the value. Several classes might be assigned.

Fixed number of classes per prediction

If active it is possible to set up the desired number of classes for a prediction.

Number of classes per prediction

The fixed number of classes that will be assigned to the prediction.

Use custom class separator

If active it is possible to enter the separation character for the prediction. Prediction is represented as a string with separated values.

Class separator

A character that separates multiple labels for prediction.

Python

Python

Select one of the Python execution environment options:

• use default Python environment for the Redfield BERT Nodes (can be configured on the preference page)
• use Conda environment from a Conda flow variable (only selectable if such a flow variable is available)

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• 09 Classification III - solutionKNIME Hub
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