CLUEDatasetSearch Operating Skill

Use this skill when a task asks for NLP dataset discovery, dataset-catalogue search, or choosing Chinese/English NLP resources from the CLUEDatasetSearch catalogue. The skill is a self-contained guide to the repository's Markdown catalogue; it does not bundle the original datasets.

First checks

1. Decide whether the user wants a dataset recommendation, a catalogue lookup, a task-family comparison, or troubleshooting for a link/license/download.
2. Read references/catalogue-overview.md for category names, table fields, duplicate handling, and search strategy.
3. Read references/access-and-license-caveats.md before suggesting a download, publication, benchmark comparison, or training run that depends on an external dataset.
4. If the user names this repo, CLUE, CLUEDatasetSearch, or a dataset title, use the bundled helper:

python scripts/search_dataset_index.py --query dureader --limit 5
python scripts/search_dataset_index.py --category text-matching --query lcqmc
python scripts/search_dataset_index.py --language Chinese --query sentiment --json

The helper reads references/dataset-index.json, a bundled index distilled from the catalogue tables.

Route by task family

User intent	Read next	Typical signals
Named entity recognition, entity spans, sequence labels, BIO/BMEO labels	sequence-labeling	NER, CLUENER, CCKS medical entity extraction, MSRA, CoNLL-2003, People's Daily, Boson
Question answering or reading comprehension	qa-reading	QA, RC, DuReader, SQuAD, NewsQA, CMRC, CAIL, cloze, yes/no, conversational QA
Classification, topic labels, sentiment, emotion, aspect sentiment	classification-sentiment	THUCNews, IFLYTEK, ChnSentiCorp, weibo_senti, NLPCC emotion, aspect sentiment, entity sentiment
Text matching, semantic similarity, NLI, entailment, DBQA relevance	matching-nli	LCQMC, BQ, AFQMC, CMNLI, ChineseSTS, CHIP, CAIL SCM, query-title matching
Summarization, machine translation, broad corpora, pretraining corpora, knowledge graph/social graph data	generation-corpora	LCSTS, WMT, translation2019zh, wiki2019zh, webtext2019zh, NLPIR corpus, knowledge graph

When a request spans multiple task families, route through each owning sub-skill and use the root helper to collect candidate rows before comparing license, language, domain, scale, and access constraints.

What this skill can and cannot claim

• It can summarize catalogue metadata: dataset title, provider, update date, license field when present, task label, keywords, paper URL, notes, and external access URL.
• It can suggest candidate datasets for a task and explain why another category may be a better fit.
• It can warn about blank license fields, link rot, Baidu Pan/password links, paid LDC resources, privacy-sensitive social data, and large downloads.
• It cannot certify that an external download still exists, that a license is sufficient for commercial use, or that benchmark results are comparable.
• It cannot train, evaluate, or preprocess any dataset because no dataset files are bundled with this skill.

Validation workflow

For a dataset-discovery answer, validate the final recommendation with this checklist:

1. The task family is routed to the right sub-skill.
2. At least one bundled-index query supports the dataset title or task signal.
3. The answer distinguishes catalogue metadata from verified downloaded data.
4. License/access caveats are explicit when the licence field is blank, paid, password-protected, or externally hosted.
5. For duplicate or overlapping dataset names, the category and task reason are named, not just the title.

Maintenance and staleness

Read references/repo-provenance.md before refreshing this skill for a newer checkout. If the catalogue categories, Markdown table schema, source commit, or generated dataset index changed, run a repo-skill refresh rather than editing the bundled index by hand.

For cross-cutting failure modes, read references/troubleshooting.md.