---

name: native-workflows description: "Guides local HanLP native Python workflows with pretrained model loading, pipeline composition, task selection, cache/device controls, and no-download smoke checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Native Workflows

Use this sub-skill when a task needs local Python HanLP rather than only a RESTful service: hanlp.load, hanlp.pretrained, hanlp.pipeline, MTL task selection, sentence splitting, device/cache controls, or a safe no-download smoke check.

Read First

• Read references/native-api-reference.md for verified signatures, local inference patterns, pipeline composition, tasks, skip_tasks, and device arguments.
• Read references/pretrained-models.md for model-family routing, identifier names, language/task coverage, and download/cache caveats.
• Read references/troubleshooting.md for model download, cache, device, and local input-shape failures.
• Run scripts/pipeline_smoke.py to verify no-download hanlp.pipeline behavior.
• Run scripts/split_sentence_smoke.py for deterministic sentence splitting without loading a pretrained EOS model.

Minimal Native Check

python -c "import hanlp; print(hanlp.__version__, len(hanlp.pretrained.ALL))"

This confirms import and identifier registration. It does not prove that a pretrained model archive is downloaded or that GPU acceleration works.

Route by Native Task

User need	Use
Load a pretrained MTL or single-task model by identifier or URL	hanlp.load(identifier, devices=..., verbose=...) in references/native-api-reference.md
Pick model families for tokenization, POS, NER, dependency/constituency parsing, SRL, SDP, AMR, classification, STS, or language ID	references/pretrained-models.md
Run fewer tasks or use pre-tokenized input with an MTL model	tasks= and skip_tasks= notes in references/native-api-reference.md
Split a document before local sentence-level models	scripts/split_sentence_smoke.py and sentence-splitting notes
Compose several single-task components	hanlp.pipeline().append(..., input_key=..., output_key=...)

Common Decisions

• Native MTL models expect sentence-level inputs. Split raw documents before passing them to local MTL models.
• Loading a model may recursively download dependencies. Make cache and network assumptions explicit before promising offline execution.
• Use devices=-1 for CPU-oriented checks when the component supports it. Use GPU devices only after verifying a compatible GPU-enabled backend.
• Use tasks to limit output work and skip_tasks='tok*' when passing pre-tokenized sentences to compatible MTL models.