LTP Repo Skill

Use this skill when a task involves LTP / Language Technology Platform for Chinese NLP: sentence splitting, Chinese word segmentation (CWS), POS tagging, NER, SRL, dependency parsing, semantic dependency parsing, legacy perceptron models, optional ltp_core training/config work, or Rust/C bindings.

This is a self-contained operating guide. Do not depend on the source checkout that produced the skill; use the bundled references and scripts here.

Fast route map

User task	Read next
Install/import LTP, check package split, diagnose ltp, ltp_core, or ltp_extension imports	references/packaging-and-install.md, then run scripts/check_ltp_install.py
Load LTP(...), run pipeline, choose tasks, interpret LTPOutput, use sentence splitting, add custom words, or handle local/HF model paths	sub-skills/python-pipeline/SKILL.md
Use fast legacy CWS/POS/NER, LTP("LTP/legacy"), ltp_extension.perceptron, custom CWS type rules, or legacy trainer APIs	sub-skills/legacy-extension/SKILL.md
Prepare data/configs for optional deep-learning training/evaluation with ltp_core, Hydra overrides, checkpoints, or task data formats	sub-skills/training-and-data/SKILL.md
Use the Rust ltp crate or ltp-cffi C bindings for legacy CWS/POS/NER	sub-skills/rust-bindings/SKILL.md
Compare model/task coverage, label sets, output shapes, and backend expectations	references/model-catalog-and-tasks.md
Debug model downloads, package version mismatches, CUDA, missing model files, training dependencies, or Rust toolchain issues	references/troubleshooting.md plus the nearest sub-skill troubleshooting reference
Check whether this skill matches a checkout/version before refreshing it	references/repo-provenance.md

Package shape to remember

LTP 4 is split across three Python distributions:

pip install torch transformers
pip install ltp ltp-core ltp-extension
python - <<'PY'
from ltp import LTP, StnSplit
print(LTP, StnSplit().split('汤姆生病了。他去了医院。'))
PY

• ltp is the high-level Python interface with the LTP(...) factory and LTPOutput.
• ltp-core contains the neural model, task heads, data modules, training/eval code, and algorithms.
• ltp-extension is the Rust-backed Python extension for sentence splitting, legacy perceptron CWS/POS/NER, hooks, and utility algorithms.
• Rust users use the separate ltp crate; C users use the ltp-cffi crate/library surface.

Safe first checks

Run the bundled environment probe before attempting model downloads or training:

python scripts/check_ltp_install.py --json
python scripts/check_ltp_install.py --check-cuda

The probe imports the package, checks distribution versions, runs a sentence split and utility-function smoke, and optionally checks CUDA availability. It does not download Hugging Face models, run training, or build Rust crates.

Common decision points

• Choose LTP("LTP/tiny"), LTP("LTP/small"), LTP("LTP/base"), LTP("LTP/base1"), or LTP("LTP/base2") for neural tasks (cws, pos, ner, srl, dep, sdp, sdpg). Use LTP("LTP/legacy") for fast legacy cws, pos, ner only.
• Pass a local model directory when offline. The directory must contain config.json; neural models also need tokenizer/model weights, and legacy models need the files named by their config.
• If you omit cws from pipeline(tasks=...), provide pre-tokenized words, not raw text.
• For legacy NER, include POS or pass POS results explicitly; NER depends on words and POS tags.
• Treat CUDA as optional acceleration unless the user explicitly asks for GPU validation. CPU is sufficient for import/API checks and many small diagnostics.
• Treat training and Rust/C builds as advanced workflows with extra dependencies/toolchains; use the bundled command builders and validators before running expensive commands.

Scope boundaries

This skill helps future agents use and troubleshoot LTP. It is not a model cache, benchmark dataset, or replacement for large pretrained weights. It intentionally avoids automatic network downloads, long training runs, and Rust/C builds unless a user explicitly asks for those actions in a suitable environment.