Chinese-BERT-wwm

Use this repo skill when a task involves HFL Chinese BERT/RoBERTa/RBT checkpoints released from the Chinese-BERT-wwm resource repository, Chinese Whole Word Masking (WWM), model loading through Transformers or PaddleHub, downstream Chinese NLP benchmark interpretation, or the bundled sentiment/NER dataset schemas.

What this skill covers

• HFL model ids such as hfl/chinese-bert-wwm, hfl/chinese-bert-wwm-ext, hfl/chinese-roberta-wwm-ext, hfl/chinese-roberta-wwm-ext-large, hfl/rbt3, hfl/rbt4, hfl/rbt6, and hfl/rbtl3.
• Correct Transformers class choice: use BertTokenizer/BertModel or AutoTokenizer/AutoModel; do not use RoBERTa classes for the RoBERTa-wwm-named checkpoints.
• Offline/cache-aware model-id validation and optional online checkpoint loading decisions.
• Model selection and fine-tuning expectations for Chinese reading comprehension, sentiment, sentence-pair matching, NLI, document classification, and Traditional Chinese tasks.
• Dataset source pointers, copyright/download constraints, benchmark metrics, and schema validation for included ChnSentiCorp, Weibo, and PeopleDaily fixture archives.

First steps

1. Read references/model-family-overview.md for the compact model catalog, WWM meaning, and route map.
2. If the task is about loading a checkpoint or validating a model id/cache, open sub-skills/model-loading/SKILL.md.
3. If the task is about choosing a model, learning rates, compact variants, WWM/CWS implications, or reproduction expectations, open sub-skills/task-selection-and-finetuning/SKILL.md.
4. If the task is about datasets, benchmark metrics, included zip schemas, or missing/copyright-restricted data, open sub-skills/data-and-benchmarks/SKILL.md.
5. For setup failures that span multiple workflows, open references/troubleshooting.md.

Sub-skill routes

User task	Read
"Load hfl/chinese-roberta-wwm-ext with Transformers", "is RobertaTokenizer correct?", "check offline cache for hfl/rbt3", "what PaddleHub module name should I use?"	sub-skills/model-loading/SKILL.md
"Which checkpoint should I fine-tune for CMRC/DRCD/XNLI/LCQMC?", "what LR did the README use?", "do I need Chinese word segmentation for WWM?", "should I use RBTL3 or truncate a large model?"	sub-skills/task-selection-and-finetuning/SKILL.md
"Where is BQ Corpus/LCQMC data?", "validate chnsenticorp.zip", "what metric does DRCD use?", "is CJRC test data official?"	sub-skills/data-and-benchmarks/SKILL.md

Minimal setup check

This repository is not an installable Python package. Transformers workflows require an environment with transformers and, for full model materialization, a backend such as PyTorch. In the user's chosen Python environment, a typical public setup is:

python -m pip install transformers torch

The root helper performs an offline import/model-id check and never downloads checkpoints:

python scripts/check_chinese_bert_wwm_setup.py --help
python scripts/check_chinese_bert_wwm_setup.py --require-transformers
python scripts/check_chinese_bert_wwm_setup.py --list-models

For model-specific cache/load checks, use sub-skills/model-loading/scripts/check_transformers_model.py. For included dataset archive schemas, use sub-skills/data-and-benchmarks/scripts/validate_dataset_schema.py.

Key operating facts

• WWM is a pretraining data-construction strategy. It does not require manual word segmentation of downstream input text.
• The RoBERTa-wwm-ext models are RoBERTa-like BERT-family models. Load them as BERT models or through Auto* classes.
• TensorFlow checkpoint zip downloads and Hugging Face/PyTorch model directories are different file formats; choose the loading/conversion path explicitly.
• Published benchmark values are empirical references, often max/average over multiple runs, not guaranteed reproduction targets.
• Some dataset folders provide only source pointers due to copyright, large file size, or in-house test constraints. Do not create downloaders that bypass those constraints.

Required references

• references/repo-provenance.md: source commit, evidence paths, package/library context, and refresh baseline.
• references/repo-routing-metadata.json: structured scenario metadata used if this verified skill is later imported into the managed repo-skill library.
• references/model-family-overview.md: shared model catalog and route map.
• references/troubleshooting.md: cross-cutting environment, cache, dataset, and reproducibility failures.

Boundaries

This skill does not provide the unreleased original pretraining implementation, full model weights, full dataset downloads, PaddleHub/PaddlePaddle as required dependencies, or guaranteed benchmark reproduction. Ask for user approval before downloading large checkpoints, installing broad optional frameworks, using credentials, mutating caches, or redistributing restricted datasets.