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name: legacy-extension description: "Guides LTP legacy perceptron and ltp_extension workflows for fast CWS/POS/NER, custom segmentation rules, and trainer APIs." disable-model-invocation: true metadata: disco-role: operating license: CC BY-NC 4.0

Legacy Extension

Use this sub-skill for the Rust-backed Python extension surface: fast legacy CWS/POS/NER, sentence splitting utilities, custom word hooks, CWS character-type rules, direct model files, and perceptron trainer APIs.

Choose this route when

• The user asks for LTP("LTP/legacy"), ltp_extension, CWSModel, POSModel, NERModel, CharacterType, Algorithm, Trainer, CWSTrainer, POSTrainer, or NERTrainer.
• Speed is more important than the full neural task set and only CWS/POS/NER are needed.
• The task involves direct legacy model binary files, custom CWS rules for digits/roman/kanji, or legacy perceptron training data.
• The user needs to understand benchmark/performance claims without running large benchmark scripts.

For neural SRL/DEP/SDP/SDPG, use ../python-pipeline/SKILL.md. For Rust-native crate use, use ../rust-bindings/SKILL.md.

Main workflow

1. Verify imports without model downloads:

   python ../../scripts/check_ltp_install.py --json
   python scripts/legacy_rules_smoke.py --no-model
   

2. For high-level legacy inference, use the factory when a complete legacy model directory or cache is available:

   from ltp import LTP
   ltp = LTP("LTP/legacy")
   output = ltp.pipeline(["他叫汤姆去拿外衣。"], tasks=["cws", "pos", "ner"])
   

3. For direct model files, use specialized extension classes:

   from ltp_extension.perceptron import CWSModel, POSModel, NERModel
   cws = CWSModel.load("cws_model.bin")
   pos = POSModel.load("pos_model.bin")
   ner = NERModel.load("ner_model.bin")
   words = cws.predict("他叫汤姆去拿外衣。")
   tags = pos.predict(words)
   entities = ner.predict(words, tags)
   

4. Validate trainer inputs before training:

   python scripts/legacy_trainer_config_check.py --task cws --train-file train.txt --eval-file dev.txt --algorithm AP
   

Read these references

• references/perceptron-api.md for inspected classes, methods, and trainer properties.
• references/workflows.md for high-level legacy, direct model, custom-rule, and trainer recipes.
• references/performance-and-benchmarks.md for what the benchmark numbers mean and why bundled helpers do not run large benchmarks.
• references/troubleshooting.md for missing model files, task dependencies, labels/data, model-type mismatches, and parallelism issues.

Bundled helpers

• scripts/legacy_rules_smoke.py checks extension imports, CharacterType, and optional local CWS model rule setup without downloads.
• scripts/legacy_trainer_config_check.py validates legacy trainer task, labels, train/eval file presence, and algorithm choice without training.

Boundaries

• Legacy only covers CWS/POS/NER. Do not route SRL/DEP/SDP/SDPG here.
• Direct model classes require local model binary files. The skill does not ship pretrained model weights.
• Trainer APIs can run training, but the bundled script only validates configuration; running training is a user-approved expensive action.