---
language:
- ne
- en
license: apache-2.0
tags:
- evaluation
- benchmark
- nepali
- low-resource
- nlp
pretty_name: NepaliBench
size_categories:
- n<1K
task_categories:
- question-answering
- text-classification
- text-generation
---

# NepaliBench 🏔️

A rigorous evaluation benchmark for Nepali language models.

## Why this exists

There is no standard, publicly reproducible benchmark for evaluating
Nepali LLMs. This dataset was created after a systematic evaluation of
himalaya-ai's NanochatGPT and Gemma fine-tune revealed that models
claiming Nepali capability had no shared evaluation standard to measure
against.

## Dataset

100 carefully curated evaluation examples across 8 categories:

| Category | Code | Count | Description |
|---|---|---|---|
| Nepali Factual Knowledge | FACT | 20 | Geography, history, constitution, symbols |
| Nepali Cultural Knowledge | CULT | 15 | Festivals, ethnic traditions, proverbs |
| Nepali Language Competence | LANG | 15 | Grammar, script, translation |
| Mathematical Reasoning | MATH | 15 | Arithmetic in Nepali cultural context (mana/pathi, ropani, bigha) |
| Logical Reasoning | REASON | 10 | Syllogisms, analogies, sequences |
| Instruction Following | INST | 10 | Format compliance, length constraints |
| Nepali NLP Tasks | NLP | 10 | NER, sentiment, summarization, language ID |
| Safe Behavior | SAFE | 5 | Appropriate refusal, uncertainty acknowledgment |

## Schema

```json
{
  "id": "NB-FACT-001",
  "category": "Nepali Factual Knowledge",
  "subcategory": "geography",
  "prompt_ne": "सगरमाथाको उचाइ कति छ?",
  "prompt_en": "What is the height of Mount Everest?",
  "reference_answer_ne": "८,८४८.८६ मिटर",
  "reference_answer_en": "8,848.86 meters",
  "difficulty": "easy",
  "answer_type": "exact",
  "rubric": null,
  "verified_source": "Nepal-China Joint Survey 2020",
  "tags": ["geography", "everest", "height"]
}
```

## Answer Types

- `exact` — model answer must match reference exactly (or contain exact figure)
- `contains` — model answer must contain key information from reference
- `rubric` — model answer evaluated against rubric criteria

## Design Principles

- All FACT answers verified against official sources (Nepal Government, UN records, Constitution of Nepal 2072)
- Regional diversity: Madhesh/Terai culture explicitly represented (Chhath, Tharu Maghi, Maithili language)
- Nepali unit systems used in MATH (mana/pathi, ropani/aana, bigha/kattha)
- Difficulty genuinely distributed: 41% easy, 40% medium, 19% hard

## Origin

Created as part of an independent audit of himalaya-ai's Nepali AI
model ecosystem (June 2026). Two PRs were filed against himalayagpt-0.5b
and himalayagpt-0.5b-it fixing critical GenerationMixin bugs discovered
during evaluation.

## Author

Premanand Pathak (premmm) — AI Engineer, Kathmandu, Nepal

## Citation

```bibtex
@dataset{pathak2026nepalibench,
  author    = {Premanand Pathak},
  title     = {NepaliBench: A Benchmark for Nepali Language Model Evaluation},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/premmm/nepali-bench}
}
```