# Fewclue Eval

> Evaluates Chinese NLP models on few-shot learning across nine tasks, including single-sentence classification, sentence-pair classification, and machine reading comprehension. It tests the ability of pre-trained language models and few-shot prompting/fine-tuning methods to generalize with limited labeled data. Use when the user wants to benchmark on FewCLUE, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/fewclue-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/fewclue-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/fewclue-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/fewclue-eval

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# fewclue-eval

> FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark — Xu et al. (2021) (arXiv:2107.07498, 2021)

## What this evaluates

Evaluates Chinese NLP models on few-shot learning across nine tasks, including single-sentence classification, sentence-pair classification, and machine reading comprehension. It tests the ability of pre-trained language models and few-shot prompting/fine-tuning methods to generalize with limited labeled data.

## Datasets

- **FewCLUE** — total ?; splits: train (-1), test (-1); repo https://github.com/CLUEbenchmark/FewCLUE

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly predicted labels out of total instances. Calculated as correct predictions divided by total test instances.

## Input / output format

**Input**: Chinese text sentences or sentence pairs, optionally reformatted into cloze-style prompts with [MASK] tokens and candidate label words for few-shot methods.

**Output**: Predicted label token or probability distribution over candidate label tokens.

## Scoring recipe

```python
correct = 0
for pred, gold in zip(predictions, gold_labels):
    if pred == gold:
        correct += 1
return correct / len(gold_labels)
```

## Common pitfalls

- Template and label-word selection significantly impact few-shot performance in PET/ADAPET.
- Small labeled subsets cause high variance; results should be averaged over multiple random seeds/splits.
- Cloze-style reformulation may alter semantic meaning, affecting model predictions.

## Evidence (verbatim from paper)

> Finally, supervised training is performed on the dataset by using cross-entropy loss.

## Citation

```bibtex
@misc{xu2021fewclue,
  title={FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark},
  author={Xu et al. (2021)},
  year={2021},
  note={arXiv:2107.07498}
}
```

- arXiv: 2107.07498