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KIEFERSA/greek-nlu-bench

Greek NLU Benchmark Frozen (v1.0) Greek language-understanding benchmark — 9,751 items / 18 tasks, one split per task. Native-verified and train/eval-disjoint. Built to detect CPT/SFT gains and regressions on Greek, weighted toward morphology-sensitive tasks. Item schema One JSON object per line; identical envelope across tasks: { "id": "el-mcq-000042", "task": "mcq", "language": "el", "version": "1.0", "tags": {"category": "linguistic", "phenomenon":… See the full description on the dataset page: https://huggingface.co/datasets/KIEFERSA/greek-nlu-bench.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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Greek NLU Benchmark

Frozen (v1.0) Greek language-understanding benchmark — 9,751 items / 18 tasks, one split per task. Native-verified and train/eval-disjoint. Built to detect CPT/SFT gains and regressions on Greek, weighted toward morphology-sensitive tasks.

Item schema

One JSON object per line; identical envelope across tasks:

json
{
  "id": "el-mcq-000042", "task": "mcq", "language": "el", "version": "1.0",
  "tags":       {"category": "linguistic", "phenomenon": "case_agreement", "difficulty": "hard", "domain": "...", "use_case": "...", "split": "dev"},
  "input": { },   "gold": { }
}

input/gold shapes are task-specific (see table). Enums: category ∈ {linguistic, knowledge}; difficulty ∈ {easy, medium, hard}; split ∈ {dev, test} (this release is dev).

Tasks

Splitn`input` → `gold`Metric
acceptability820sentence → 0/1acc + MCC
assertion_reason450assertion+reason+4 choices → letteracc
cloze_open408text w/ ___ → accepted formsacc
cloze_select331text w/ ___ + inflected options → indexacc (sliced by phenomenon)
coreference447text + mention + gender-matched candidates → indexacc
extractive_qa438context+question → answer spans ([] = unanswerable)EM + token-F1
idiom_appropriateness900sentence+idiom → 0/1acc + F1
idiom_open_cloze325text w/ blanked idiom → acceptedacc
machine_translation436source + direction → referenceschrF + COMET
masked_token397text w/ [MASK] → acceptedacc
matching441left[]+right[] → pairsmean pair-acc (Jaccard)
mcq446question+4 choices → letteracc
nli1299premise+hypothesis → {entail, neutral, contradict}acc + macro-F1
paraphrase874sentence1+sentence2 → 0/1acc + F1
sentiment450text + label_set → labelacc + macro-F1
sequence_ordering450shuffled sentences → permutationKendall τ
sts450sentence1+sentence2 → score 0–5Pearson + Spearman
wsd389sentence + target + senses → indexacc

★ primary morphology sensor · † separate track — excluded from the NLU macro average

Scoring

  • Headline: macro-average of per-task accuracies (MT track reported separately).
  • Greek-aware normalization: NFC; accents stripped except where the phenomenon under test is the accent; final sigma folded unless tested.
  • Choice tasks support a logprob path (argmax over option continuations) to remove parse noise; generation/regression tasks use greedy decode + parse.
  • Primary deliverable is the sliced table (by category / phenomenon / difficulty) and a baseline→candidate diff with regression flags.
python
from datasets import load_dataset
nli = load_dataset("KIEFERSA/greek-nlu-bench", "nli")["dev"]   # one config per task

Reference harness: `greek-bench` · Leaderboard.

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