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deeppavlov

deeppavlov

DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: deeppavlov description: "DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

DeepPavlov

Use this repo skill when the task involves the DeepPavlov Python package, its deeppavlov.configs tree, or the python -m deeppavlov command family.

What this skill covers

  • Installing and smoke-checking the package.
  • Choosing the right CLI mode for a config-driven workflow.
  • Routing to the right model family for text classification, tagging, retrieval/QA, or serving.
  • Troubleshooting package-wide install/import and config issues.

Start here

  1. Read references/installation-and-cli.md for install, CLI modes, and the offline smoke helper.
  2. Read references/model-overview.md to route to the right sub-skill for the user’s task family.
  3. Read references/troubleshooting.md when the failure is package-wide rather than family-specific.
  4. Read references/repo-provenance.md if you need the source baseline used to create this skill.

Route to a sub-skill

sub-skills/pipelines/SKILL.md

Use 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.md

Use 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.md

Use this for document retrieval, ranking, FAQ, SQuAD, ODQA, and KBQA.

sub-skills/serving/SKILL.md

Use this for riseapi, risesocket, /probe, /api, socket framing, service settings, TLS flags, metrics, and runtime request validation.

Minimal package smoke

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.

Common decisions

  • If the user only gives a config stem, let DeepPavlov resolve it unless a deprecation alias warning appears.
  • If the task is about the shape of a config or how to wire a pipeline, go to pipelines.
  • If the task is about which built-in model family to use, go to text-models or retrieval-qa.
  • If the task is about exposing a chosen model over HTTP or sockets, go to serving.
  • If the task is about a broken install, import, or dependency conflict and does not depend on a specific family, use the root troubleshooting reference.

What not to do here

  • Do not re-open the original repository checkout for runtime guidance when the bundled references already answer the question.
  • Do not bury model-family specifics in this root file; keep them in the owned sub-skill.
  • Do not send serving questions to a model-family sub-skill just because the model family appears in the request.

Useful commands

  • python -m deeppavlov --help
  • python scripts/smoke_deeppavlov_pipeline.py
  • python -m pip check

Provenance and selection metadata

  • See references/repo-provenance.md for the source baseline.
  • See references/repo-routing-metadata.json for router placement and usage signals.
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