---

name: supervised-extraction description: "Guide DeepKE supervised NER, RE, AE, EE, cnSchema, and scenario-specific extraction workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepKE supervised extraction

Use this sub-skill when a task asks to train, predict with, configure, or troubleshoot DeepKE's classic supervised extraction workflows: named entity recognition (NER), relation extraction (RE), attribute extraction (AE), event extraction (EE), cnSchema off-the-shelf NER/RE, and the standard, few-shot, multimodal, document-level, or cross-domain variants.

Route by intent

• Choose the right task and scenario: read references/workflows.md for task selection and end-to-end recipes.
• Prepare or inspect input files and Hydra YAMLs: read references/data-and-config.md for columns, split names, config knobs, checkpoint fields, and path-resolution caveats.
• Pick a model family or import surface: read references/model-overview.md for the scenario/model table and DeepKE package layout.
• Debug installation, model, dataset, GPU, or workflow failures: read references/troubleshooting.md before changing code or rerunning long training.
• Check the local runtime safely: run scripts/check_supervised_env.py to inspect installed imports, CUDA visibility, and optional data/checkpoint path expectations without training or downloading.

Owns

• Standard NER with BERT, BiLSTM-CRF, and W2NER.
• Few-shot NER with LightNER-style prompt tuning and cross-domain NER with CP-NER-style prefix transfer.
• Multimodal NER and RE with text plus detected/grounded visual objects.
• Standard RE and AE with CNN/RNN/Capsule/GCN/Transformer/LM families.
• Few-shot RE, document-level RE, and cnSchema quick-load NER/RE.
• Standard EE trigger and role extraction pipelines, including the DEGREE-style variant as a reference-only path.

Does not own

• Creating or converting datasets from annotation exports; use the sibling data-preparation sub-skill for conversion and weak/distant supervision helpers.
• PRGC, PURE, ASP, MT5, or other triple-extraction workflows; use the sibling triple-extraction sub-skill.
• DeepKE-LLM, instruction KGC, OpenAI/API workflows, or OneKE-style large-model inference; use the sibling llm-workflows sub-skill.
• MCP server/client deployment; use the sibling mcp-tools sub-skill.

Quick operating pattern

1. Classify the request by task (NER, RE, AE, EE) and scenario (standard, few-shot, multimodal, document, cross-domain, cnSchema).
2. Confirm whether the user wants training, prediction, config diagnosis, or data validation.
3. Verify the environment with scripts/check_supervised_env.py when imports, CUDA, model files, or data layout are uncertain.
4. Read the workflow recipe and data/config reference for the selected scenario.
5. Treat long training, checkpoint downloads, GPU-only multimodal work, and DEGREE/EE variants as explicit resource-dependent operations; do not silently start them without user approval.