计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
deeppavlov【效率与协作】DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this repo skill when the task involves the DeepPavlov Python package, its
deeppavlov.configs tree, or the python -m deeppavlov command family.
references/installation-and-cli.md
for install, CLI modes, and the offline smoke helper.references/model-overview.md to route
to the right sub-skill for the user’s task family.references/troubleshooting.md when
the failure is package-wide rather than family-specific.references/repo-provenance.md if you
need the source baseline used to create this skill.sub-skills/pipelines/SKILL.mdUse this for config parsing, build_model, train_model, evaluate_model,
train_evaluate_model_from_config, parse_config, deep_download, nested
configs, registries, custom components, cross-validation, and parameter
search.
sub-skills/text-models/SKILL.mdUse this for classifiers, NER, entity extraction, spelling correction, sentence segmentation, morpho-syntax parsing, relation extraction, multitask models, and embedding extraction.
sub-skills/retrieval-qa/SKILL.mdUse this for document retrieval, ranking, FAQ, SQuAD, ODQA, and KBQA.
sub-skills/serving/SKILL.mdUse this for riseapi, risesocket, /probe, /api, socket framing,
service settings, TLS flags, metrics, and runtime request validation.
After installing DeepPavlov in the target environment, run the offline smoke script:
python scripts/smoke_deeppavlov_pipeline.py
Expected result: the tiny lowercasing/tokenization pipeline prints a tokenized output for the default text.
pipelines.text-models or retrieval-qa.serving.python -m deeppavlov --helppython scripts/smoke_deeppavlov_pipeline.pypython -m pip checkreferences/repo-provenance.md for the source baseline.references/repo-routing-metadata.json for router placement and usage
signals.