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name: classification description: "Operate Snorkel PyTorch classification, training, logging, and evaluation APIs." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Classification

Use this sub-skill for Snorkel discriminative modeling workflows built around:

• DictDataset, DictDataLoader, and collate_dicts
• Operation, Task, MultitaskClassifier, and cross_entropy_with_probs
• Trainer, logging, checkpointing, and batch / learning-rate schedulers
• Scorer, metric_score, label utilities, and error-analysis helpers

Route elsewhere when needed

• Weak labels, labeling functions, and LabelModel: ../labeling/SKILL.md
• Slice-aware classifier internals and slice-function-driven tasks: ../slicing/SKILL.md
• Generic preprocessors, mapping, and augmentation: ../data-transforms/SKILL.md

Start here

1. Read references/api-reference.md for the public surface and defaults.
2. Use references/training-and-evaluation.md for the end-to-end workflow.
3. Check references/troubleshooting.md for common failures and fixes.
4. Run scripts/classification_smoke.py for a tiny CPU-only validation.

What to emphasize

• Build the shortest task graph that still exposes the behavior being debugged.
• Prefer in-memory tensors and small fixtures.
• Keep label semantics explicit: hard labels for scoring, probabilistic labels for soft-loss training.
• Use the metric namespace task/dataset/split/metric when reading scores, logs, and checkpoints.

What this sub-skill does not own

• Weak supervision model training and LF analysis
• Slice-function authoring and slice-aware modeling details
• Data augmentation and generic preprocessing pipelines

When a request crosses those boundaries, route it to the adjacent sub-skill instead of stretching this one.