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ÈwéBench 🇹🇬

The Reference Benchmark for Evaluating LLMs in Ewe Language

Le benchmark de référence pour l'évaluation de LLMs en langue Ewe

English • Français • Documentation • Leaderboard

English

What is ÈwéBench?

ÈwéBench is the first standardized benchmark for evaluating Large Language Models (LLMs) on the Ewe language (ɛʋɛgbɛ) a Kwa language spoken by ~7 million people in Togo and Ghana.

Unlike generic multilingual benchmarks that treat African languages as afterthoughts, ÈwéBench is designed from the ground up for Ewe, with culturally relevant tests, native speaker validation, and evaluation criteria that understand Ewe's unique linguistic features (tonality, agglutination, proverbs).

Why ÈwéBench?

No existing benchmark specifically evaluates LLM capabilities in Ewe
Generic multilingual benchmarks (MMLU, HellaSwag) don't capture Ewe's nuances
African languages need dedicated evaluation tools to track real progress
Researchers and developers need a common standard to compare models

Key Features

Feature	Description
10 categories	From linguistic comprehension to agentic capabilities
107 tests	Manually crafted, culturally grounded
Weighted scoring	ÈwéScore single metric, weighted by category importance
Any model	Works with any OpenAI-compatible API (local or cloud)
CLI & API	Run from terminal or integrate into CI/CD
Leaderboard	Track and compare model progress
Presets	One-command evaluation for Gemini, local models

Quick Start

# Clone the repo
git clone https://github.com/joel710/EweBench.git
cd EweBench

# Install dependencies
pip install -r requirements.txt

# Run with a preset
python run_benchmark.py --preset model --verbose

# Run with a custom endpoint
python run_benchmark.py --endpoint http://localhost:11434/v1/chat/completions \
                        --model yawo-v10 --verbose

# Compare two results
python run_benchmark.py --compare results/model_a.json results/model_b.json

# View leaderboard
python run_benchmark.py --leaderboard

#	Category	Tests	Weight	Description
1	Linguistic Comprehension	15	15%	Grammar, vocabulary, tonality, morphology
2	Text Generation	12	15%	Fluency, coherence, natural Ewe output
3	Reasoning	12	12%	Logical reasoning expressed in Ewe
4	Translation	12	12%	Bidirectional FR↔Ewe, EN↔Ewe
5	Cultural Knowledge	10	10%	Proverbs, traditions, Ewe/Togolese history
6	Instruction Following	10	10%	Complex instruction compliance
7	Multi-turn	8	8%	Context coherence across turns
8	Agentic	10	8%	Function calling, tool use
9	Style Adaptation	8	5%	Register switching (formal/informal)
10	Robustness	10	5%	Consistency under adversarial inputs
	Total	107	100%

Scoring ÈwéScore

The ÈwéScore is a single number (0-100) representing overall Ewe language capability:

ÈwéScore = Σ (category_score × category_weight) / Σ active_weights

Each test is scored 0.0-1.0 using evaluation methods:

exact_match Normalized string comparison
keywords Presence of expected Ewe keywords
multiple_choice QCM answer detection
format Output format compliance (markdown, function_call, etc.)
ewe_quality Heuristic Ewe linguistic quality (character usage, vocabulary, structure)
composite Weighted combination of multiple methods

Passing threshold: A test is "passed" if score ≥ 0.7

Evaluation Methods

Method	Use case	How it works
`exact_match`	Factual QA	Normalized comparison with expected answer
`keywords`	Open-ended	Checks presence of expected Ewe keywords in response
`multiple_choice`	QCM	Detects correct answer letter (A/B/C/D)
`format`	Structured output	Validates format (markdown, function_call, length)
`ewe_quality`	Free generation	Scores Ewe character usage, vocabulary, sentence structure
`composite`	Complex tests	Average of keywords + ewe_quality + format

API Compatibility

ÈwéBench works with any API implementing the OpenAI chat completions format:

POST /v1/chat/completions
{
  "model": "model-name",
  "messages": [{"role": "user", "content": "..."}],
  "temperature": 0.3,
  "max_tokens": 1024
}

Tested providers:

Openai SDK
Google Gemini (OpenAI-compatible endpoint)
Ollama (local)
vLLM (local)
Any OpenAI-compatible server

Français

Qu'est-ce qu'ÈwéBench ?

ÈwéBench est le premier benchmark standardisé pour évaluer les grands modèles de langage (LLMs) sur la langue Ewe (ɛʋɛgbɛ) une langue Kwa parlée par ~7 millions de personnes au Togo et au Ghana.

Contrairement aux benchmarks multilingues génériques qui traitent les langues africaines comme des détails, ÈwéBench est conçu de zéro pour l'Ewe, avec des tests culturellement pertinents, une validation par des locuteurs natifs, et des critères d'évaluation qui comprennent les particularités linguistiques de l'Ewe (tonalité, agglutination, proverbes).

Pourquoi ÈwéBench ?

Aucun benchmark existant n'évalue spécifiquement les capacités LLM en Ewe
Les benchmarks multilingues génériques (MMLU, HellaSwag) ne capturent pas les nuances de l'Ewe
Les langues africaines ont besoin d'outils d'évaluation dédiés pour mesurer les vrais progrès
Les chercheurs et développeurs ont besoin d'un standard commun pour comparer les modèles

Démarrage rapide

# Cloner le repo
git clone https://github.com/joel710/EweBench.git
cd EweBench

# Installer les dépendances
pip install -r requirements.txt

# Configurer (optionnel   pour les presets cloud)
cp .env.example .env
# Ajouter vos clés API dans .env

# Lancer avec un preset
python run_benchmark.py --preset model --verbose

# Lancer sur un modèle local
python run_benchmark.py --endpoint http://localhost:11434/v1/chat/completions \
                        --model yawo-v10 --verbose

# Évaluer une seule catégorie
python run_benchmark.py --preset model --category cultural_knowledge -v

# Comparer deux modèles
python run_benchmark.py --compare results/model.json results/yawo.json

# Voir le classement
python run_benchmark.py --leaderboard

Catégories

#	Catégorie	Tests	Poids	Description
1	Compréhension Linguistique	15	15%	Grammaire, vocabulaire, tons, morphologie
2	Génération de Texte	12	15%	Fluence, cohérence, naturel du texte Ewe
3	Raisonnement	12	12%	Raisonnement logique exprimé en Ewe
4	Traduction	12	12%	Bidirectionnelle FR↔Ewe, EN↔Ewe
5	Connaissance Culturelle	10	10%	Proverbes, traditions, histoire Ewe/togolaise
6	Suivi d'Instructions	10	10%	Respect d'instructions complexes
7	Multi-tour	8	8%	Cohérence contextuelle sur plusieurs échanges
8	Agentique	10	8%	Function calling, utilisation d'outils
9	Adaptation Stylistique	8	5%	Registres formel/informel, technique/simple
10	Robustesse	10	5%	Cohérence face aux entrées adverses
	Total	107	100%

Scoring ÈwéScore

L'ÈwéScore est un nombre unique (0-100) représentant la capacité globale en Ewe :

ÈwéScore = Σ (score_catégorie × poids_catégorie) / Σ poids_actifs

Seuil de réussite : Un test est "réussi" si le score ≥ 0.7

Leaderboard

#	Model	ÈwéScore	Tests Passed	Date
🥇	En attente de soumissions	-	-	-

Soumettre vos résultats : Exécutez le benchmark, puis ouvrez une PR avec votre fichier de résultats dans results/.

Project Structure

EweBench/
├── README.md              # This file (bilingual EN/FR)
├── LICENSE                # CC BY-NC 4.0
├── requirements.txt       # Python dependencies
├── .env.example           # API keys template
├── ewe_bench.py           # Core benchmark engine
├── run_benchmark.py       # CLI runner with presets
├── leaderboard.json       # Public leaderboard data
├── tests/                 # Test suites (107 tests)
│   ├── linguistic_comprehension.json (15)
│   ├── text_generation.json (12)
│   ├── reasoning.json (12)
│   ├── translation.json (12)
│   ├── cultural_knowledge.json (10)
│   ├── instruction_following.json (10)
│   ├── multi_turn.json (8)
│   ├── agentic.json (10)
│   ├── style_adaptation.json (8)
│   └── robustness.json (10)
├── results/               # Benchmark results (gitignored)
├── docs/
│   ├── METHODOLOGY.md     # Scoring methodology details
│   ├── CONTRIBUTING.md    # How to contribute tests
│   └── TEST_FORMAT.md     # Test JSON format specification
└── .github/
    └── ISSUE_TEMPLATE.md

Contributing

We welcome contributions! See docs/CONTRIBUTING.md for details.

Ways to contribute:

Add tests More tests improve coverage
Validate translations Native speaker review
Submit results Run on your model and share
Report issues Found a bad test? Let us know

License

CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International

✅ Free to use for research, education, and evaluation
✅ Free to modify and redistribute (with attribution)
⚠️ Commercial use requires explicit permission from Joel Elisée ADZONYA / Strive AI

Citation

@misc{ewebench2026,
  author = {Joel Elisée ADZONYA},
  title = {ÈwéBench: A Reference Benchmark for Evaluating LLMs in Ewe Language},
  year = {2026},
  publisher = {Strive AI},
  howpublished = {\url{https://github.com/joel710/EweBench}}
}

Created by Joel Elisée ADZONYA Strive AI

L'IA au service des langues africaines

Datasets:

jojonocode
/

EweBench

ÈwéBench 🇹🇬

The Reference Benchmark for Evaluating LLMs in Ewe Language

English

What is ÈwéBench?

Why ÈwéBench?

Key Features

Quick Start

Categories

Scoring ÈwéScore

Evaluation Methods

API Compatibility

Français

Qu'est-ce qu'ÈwéBench ?

Pourquoi ÈwéBench ?

Démarrage rapide

Catégories

Scoring ÈwéScore

Leaderboard

Project Structure

Contributing

License

Citation