{
 "body": "\nPart of the [FewCLUE](fewclue.md) family.\n\n## What it measures\n\nEPRSTMT is binary sentiment classification on Chinese e-commerce product reviews -- Positive or\nNegative -- collected and filtered by Beijing Normal University's ICIP Lab and reorganised for\nFewCLUE. It is one of three tasks built new for FewCLUE rather than resampled from the CLUE\nbenchmark, and the simplest task in the suite by format: a single short review sentence in, one of\ntwo labels out.\n\n## Reading the numbers\n\nThe labelled public test set holds 610 reviews, scored by accuracy against a 50% two-way baseline.\nHuman evaluators scored 90.0%; the paper's best few-shot method (P-tuning on RoBERTa) reached 88.3%,\nwithin two points of human performance and the closest any method came to human parity on any\nFewCLUE task, while zero-shot prompting alone still managed 85.2% -- evidence that sentiment polarity\nis comparatively easy for a pretrained model to surface with little or no task-specific training.\nBecause it is the easiest task in the suite, a strong EPRSTMT score alone is a weak signal of a\nmodel's overall few-shot ability; read it alongside the harder tasks rather than in isolation. See\nthe [FewCLUE](fewclue.md) family page for the shared few-shot protocol and contamination notes that\napply here too.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "fewclue_eprstmt",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [],
 "page": {
  "category": "reasoning",
  "dataset": {
   "languages": [
    "zh"
   ],
   "license": "",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 610,
   "size_note": "610 labelled public test reviews (test_public.json, used for scoring), plus 753 in the private test set (original leaderboard only), 32 train and 32 dev examples per split (5 parallel splits, train_0..train_4 / dev_0..dev_4, plus a merged train_few_all/dev_few_all), and 19,565 unlabelled reviews not used for scoring. Figures from the FewCLUE paper's Table 1, matching the GitHub README. Reviews were collected and filtered by Beijing Normal University's ICIP Lab and reorganised for FewCLUE.\n",
   "splits": "train_0..train_4 (32 each) + train_few_all; dev_0..dev_4 (32 each) + dev_few_all; test_public (610, labelled); test (753, private); unlabeled (19,565)",
   "url": "https://github.com/CLUEbenchmark/FewCLUE/tree/main/datasets/eprstmt"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 5, slice B"
  },
  "harness": {
   "opencompass": "FewCLUE_eprstmt"
  },
  "id": "fewclue_eprstmt",
  "leaderboard_url": "https://www.cluebenchmarks.com/fewclue.html",
  "lineage": {
   "family": "fewclue"
  },
  "measures": "A short Chinese e-commerce product review; the model classifies its sentiment as Positive or Negative, learned few-shot from 32 labelled training reviews.\n",
  "metric": {
   "baseline_note": "50.0% is the paper's majority-class baseline; the public test set's two labels are roughly balanced, so this matches the two-way random-guess rate. See the fewclue family page for the top overall few-shot method scores.\n",
   "direction": "higher_is_better",
   "human_baseline": 90.0,
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": 50.0,
   "unit": "%"
  },
  "name": "FewCLUE: EPRSTMT (E-commerce Sentiment Analysis)",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2107.07498",
   "title": "FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark",
   "url": "https://arxiv.org/abs/2107.07498",
   "year": 2021
  },
  "publisher": {
   "authors": [
    "Liang Xu",
    "Xiaojing Lu",
    "Chenyang Yuan",
    "Xuanwei Zhang",
    "Huilin Xu",
    "Hu Yuan",
    "Guoao Wei",
    "Xiang Pan",
    "Xin Tian",
    "Libo Qin",
    "Hu Hai"
   ],
   "org": "CLUE team",
   "url": "https://github.com/CLUEbenchmark/FewCLUE"
  },
  "released": "2021-04",
  "repo_url": "https://github.com/CLUEbenchmark/FewCLUE",
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark (Xu et al., arXiv:2107.07498)",
    "url": "https://arxiv.org/abs/2107.07498"
   },
   {
    "accessed": "2026-09-08",
    "title": "CLUEbenchmark/FewCLUE GitHub repository (task description, dataset statistics)",
    "url": "https://github.com/CLUEbenchmark/FewCLUE"
   },
   {
    "accessed": "2026-09-08",
    "title": "OpenCompass FewCLUE_eprstmt dataset config",
    "url": "https://raw.githubusercontent.com/open-compass/opencompass/main/opencompass/configs/datasets/FewCLUE_eprstmt/FewCLUE_eprstmt_gen.py"
   }
  ],
  "status": "unknown",
  "subcategory": "binary sentiment classification of e-commerce product reviews, few-shot",
  "summary": "FewCLUE's sentiment task: classify a Chinese e-commerce product review as positive or negative, learned from 32 labelled training reviews.",
  "tags": [
   "chinese",
   "few-shot",
   "fewclue-subset",
   "sentiment"
  ],
  "task_format": "Binary sentiment classification, graded on the single correct label; evaluated from a 32-example few-shot training split, one of five parallel splits FewCLUE provides for this task.\n"
 }
}