A Chinese few-shot NLU benchmark: nine tasks learned from 8-32 labelled examples per class across five parallel splits, so a score measures few-shot learning rather than full-data task competence.
unassessed
| Category | composite |
|---|---|
| Subcategory | Chinese few-shot NLU suite: sentiment, matching, classification, cloze and coreference tasks |
| Page status | unknown |
| Metric | accuracy |
| Direction | higher_is_better |
| Unit | % |
| Dataset size | 16251 |
| Publisher | CLUE team |
FewCLUE bundles nine separate Chinese NLU tasks -- sentiment classification, long- and short-text topic classification, natural language inference, dialogue-intent matching, idiom cloze, scientific keyword verification, and pronoun coreference -- under one shared constraint: every task is learned from a handful of labelled examples (8 to 32 per class, depending on the task's number of labels) rather than a full-size training set. Five independent training/validation splits are provided per task specifically to average out the instability that small samples cause, plus up to 20,000 unlabelled examples per task for semi-supervised research. A FewCLUE score measures how much a model can learn from a handful of examples, which is a different quantity from a full-data score on the same underlying task.
Nine tasks: five single-sentence or sentence-pair classification tasks (2, 3, 15, 67 or 119 classes), one dialogue short-text matching task, and two reading-comprehension/cloze tasks (idiom cloze, keyword verification), all graded by accuracy against one correct label. Training draws 8-32 labelled examples per class from one of five parallel splits (train_0..train_4); a merged split and up to 20,000 unlabelled examples per task are also provided, unused for the scored metric.
No model card in ModelSpec reports this benchmark yet.