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name: slicing description: "Operate Snorkel slicing functions, slice monitoring, and slice-aware classifiers." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Slicing

Use this skill for Snorkel slice functions, slice monitoring, and slice-aware models.

Use this skill when

• creating SlicingFunction objects or @slicing_function decorators
• creating NLPSlicingFunction or @nlp_slicing_function when slice logic depends on spaCy docs
• applying SFApplier, PandasSFApplier, DaskSFApplier, PandasParallelSFApplier, or SparkSFApplier
• filtering a DataFrame with slice_dataframe
• adding slice labels with add_slice_labels
• converting a base task into slice tasks with convert_to_slice_tasks
• building or scoring SliceAwareClassifier and SliceCombinerModule

Route other work elsewhere

• Generic transforms or spaCy preprocessing mechanics -> ../data-transforms/SKILL.md
• DictDataset, DictDataLoader, Task, Trainer, or tensor-shape issues -> ../classification/SKILL.md
• Weak-supervision LFs, LFApplier, or LabelModel -> ../labeling/SKILL.md

Start with

• API reference
• Workflows
• Troubleshooting
• Smoke script

Operating notes

• Slice memberships should be binary masks; use truthy in-slice values.
• SFApplier and PandasSFApplier return named-field recarrays.
• make_slice_dataloader needs the base task labels already in the dataset.
• score_slices evaluates pred labels on the base task and skips ind labels.
• SliceAwareClassifier and SliceCombinerModule are binary-only.
• Optional backends live under snorkel.slicing.apply.dask and snorkel.slicing.apply.spark; see troubleshooting if Dask, PySpark, Java, or spaCy are missing.