Text classification dataset config

Attributes

The dataset config can be a Python dictionary or be saved as a YAML file.

For text classification, it should contain the following attributes:

Attribute	Type	Default	Description	Comments
`classNames`	List[str]	-	List of class names indexed by label integer in the dataset.	E.g. ["Retained", "Exited"] when 0, 1 are in your label column.
`label`	str	-	Type of dataset.	Must be one of `training` or `validation`
`labelColumnName`	str	-	Name of the column with the labels.	The data in this column must be zero-indexed integers, matching the list provided in classNames.
`language`	str	'en'	The language of the dataset in ISO 639-1 (alpha-2 code) format.
`metadata`	Dict[str, any]	{}	Dictionary containing metadata about the dataset.	This is the metadata that will be displayed on the Openlayer platform.
`predictionsColumnName`	str	-	Name of the column with the model's predictions as zero-indexed integers.	Applies only if you are uploading a model as well.
`predictionScoresColumnName`	str	-	Name of the column with the model's predictions as lists of class probabilities.	Applies only if you are uploading a model as well.
`sep`	str	‘,’	Delimiter to use.	Applies only if you are uploading the dataset as a CSV file. E.g. '\t'.
`textColumnName`	str	-	Name of the column with the text.

For a training dataset as the one below — which contains predictions scores,

a valid dataset_config.yaml file would be:

classNames:
- negative
- positive
label: training
labelColumnName: polarity
predictionScoresColumnName: predictionScores
textColumnName: text

Alternatively, for a training dataset as the one below — which contains zero-indexed model predictions,

a valid dataset_config.yaml file would be:

classNames:
- negative
- positive
label: training
labelColumnName: polarity
predictionsColumnName: predictions
textColumnName: text