---

name: task-selection-and-finetuning description: "Choose Chinese-BERT-wwm family models and fine-tuning strategy for Chinese downstream tasks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Task Selection and Fine-Tuning

Use this sub-skill when the user needs to choose an HFL Chinese whole-word-masked BERT-family model, set first-pass fine-tuning expectations, or diagnose benchmark/reproducibility confusion for Chinese downstream tasks.

Use this when asked about

• Choosing among BERT-wwm, BERT-wwm-ext, RoBERTa-wwm-ext, RoBERTa-wwm-ext-large, RBT3, RBTL3, RBT4, or RBT6.
• Long-text Chinese reading comprehension, document classification, sentence matching, NLI, sentiment, or Traditional Chinese tasks such as DRCD.
• Whole Word Masking (WWM), Chinese word segmentation during pretraining, and whether downstream inputs must be segmented.
• Initial learning-rate choices, random-seed variation, batch-size effects, and interpreting max/average reported results.
• Whether additional pretraining is needed for a large domain shift.

Start here

1. Read references/selection-guide.md to choose a model family by task, domain, resource budget, and evidence strength.
2. Read references/fine-tuning-and-reproducibility.md for the README learning-rate table, run-count assumptions, and reproduction cautions.
3. Read references/troubleshooting.md when the issue involves unexpected low scores, CWS misconceptions, RoBERTa naming/class confusion, or missing pretraining code.

Immediate routing rules

• For code-level loading, model ids, tokenizer/model classes, offline cache checks, or PaddleHub module names, route to ../model-loading/SKILL.md.
• For dataset file schemas, included zip archives, benchmark table details, metric definitions, or dataset copyright/download notes, route to ../data-and-benchmarks/SKILL.md.
• For original pretraining implementation code, state the gap: this repository documents that pretraining code is not released. Do not invent a repository-specific pretraining command.

High-confidence operating facts

• WWM changes pretraining sample construction, not downstream input requirements. Chinese CWS was used to decide which characters belong to the same word during pretraining; future downstream inputs do not need manual Chinese word segmentation solely because the checkpoint is WWM.
• The RoBERTa-wwm-ext names are RoBERTa-like training names for BERT-family models. They should be selected by benchmark/resource fit here, but loading details belong in ../model-loading/SKILL.md.
• Initial learning rate must be tuned for the target task. The README table provides starting points, not guaranteed optima.
• Published benchmark numbers were reported over 10 runs with different random seeds using maximum and average scores; exact reproduction of the maximum is not expected.