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name: training-and-data description: "Guides optional ltp_core training, evaluation, Hydra configuration, and data-format preparation workflows." disable-model-invocation: true metadata: disco-role: operating license: CC BY-NC 4.0

Training and Data

Use this sub-skill for optional deep-learning training/evaluation workflows in ltp_core: Hydra config selection, command construction, checkpoint evaluation, task data layouts, data validation, and safe CPU/GPU trainer decisions.

Choose this route when

• The user asks about ltp_core.train, ltp_core.eval, Hydra overrides, configs/, train.sh, eval.sh, checkpoints, or custom model training.
• The task is to prepare CWS/POS/NER/SRL/DEP/SDP data, vocabs, or config overrides.
• The user needs to validate a tiny dataset or build a train/eval command without launching training.
• The task names CRF, task adapters, Lightning modules, metrics, or config groups.

For inference-only work, use ../python-pipeline/SKILL.md. For legacy perceptron trainer APIs, use ../legacy-extension/SKILL.md.

Safe workflow

1. Check basic ltp_core importability from the root:

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

2. Validate the user's data layout before command construction:

   python scripts/validate_ltp_training_data.py --task ner --data-dir /path/to/ner-data
   python scripts/validate_ltp_training_data.py --task conllu --data-dir /path/to/conllu-data
   

3. Build a command string without running it:

   python scripts/build_train_command.py --mode train --experiment cws --trainer cpu
   python scripts/build_train_command.py --mode eval --experiment dep --ckpt-path /path/to/checkpoint.ckpt
   

4. Run training/eval only after the user accepts long-running jobs, data/model access, and optional GPU/logging side effects.

Read these references

• references/training-workflows.md for train/eval command patterns and checkpoint flow.
• references/data-formats.md for BIO, CoNLL-U, SRL, and vocab directory expectations.
• references/configuration.md for Hydra config groups, task experiments, trainer choices, and overrides.
• references/troubleshooting.md for optional dependencies, Hydra errors, checkpoint/data failures, and device/logging issues.

Bundled helpers

• scripts/build_train_command.py emits a safe command template and validates required ckpt_path for eval.
• scripts/validate_ltp_training_data.py performs lightweight format/layout checks for CWS/BIO/CoNLL-U/SRL-style directories.

Boundaries

• This sub-skill does not automatically install broad training dependencies or run training.
• Training configs and data examples are distilled here; future agents should not open original repo config files as runtime documentation.
• GPU/CUDA is optional for command construction. Actual GPU training verification requires an explicit user-approved run.