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Utility Functions

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setfit.get_templated_dataset

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( dataset: typing.Optional[datasets.arrow_dataset.Dataset] = None candidate_labels: typing.Optional[typing.List[str]] = None reference_dataset: typing.Optional[str] = None template: str = 'This sentence is {}' sample_size: int = 2 text_column: str = 'text' label_column: str = 'label' multi_label: bool = False label_names_column: str = 'label_text' ) →
`Dataset`

Parameters

- dataset (`Dataset`, optional) — A Dataset to add templated examples to.
- candidate_labels (`List[str]`, optional) — The list of candidate labels to be fed into the template to construct examples.
- reference_dataset (`str`, optional) — A dataset to take labels from, if `candidate_labels` is not supplied.
- template (`str`, optional, defaults to `"This sentence is {}"`) — The template used to turn each label into a synthetic training example. This template must include a {} for the candidate label to be inserted into the template. For example, the default template is “This sentence is {}.” With the candidate label “sports”, this would produce an example “This sentence is sports”.
- sample_size (`int`, optional, defaults to 2) — The number of examples to make for each candidate label.
- text_column (`str`, optional, defaults to `"text"`) — The name of the column containing the text of the examples.
- label_column (`str`, optional, defaults to `"label"`) — The name of the column in `dataset` containing the labels of the examples.
- multi_label (`bool`, optional, defaults to `False`) — Whether or not multiple candidate labels can be true.
- label_names_column (`str`, optional, defaults to “label_text”) — The name of the label column in the `reference_dataset`, to be used in case there is no ClassLabel feature for the label column.

Returns

`Dataset`

A copy of the input Dataset with templated examples added.

Raises

`ValueError`

- `ValueError` — If the input Dataset is not empty and one or both of the provided column names are missing.

Create templated examples for a reference dataset or reference labels.

If `candidate_labels` is supplied, use it for generating the templates. Otherwise, use the labels loaded from `reference_dataset`.

If input Dataset is supplied, add the examples to it, otherwise create a new Dataset. The input Dataset is assumed to have a text column with the name `text_column` and a label column with the name `label_column`, which contains one-hot or multi-hot encoded label sequences.

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setfit.sample_dataset

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( dataset: Dataset label_column: str = 'label' num_samples: int = 8 seed: int = 42 )

Samples a Dataset to create an equal number of samples per class (when possible).

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