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ZarnL Evaluation Pipeline
An implementation of the 70-language evaluation and benchmarking pipeline for Zarnite. This repository implements a multi-tiered language evaluation structure, routing each language to its most reliable metric, and computing the Zarnite Language Opportunity Score (ZLOS) to prioritize investment.
Repository Structure
registry/schema.py: LanguageEntry and Registry classes (JSON storage/dataframe generation)seed_languages.csv: Reference seeds of the 70 confirmed African languageszlos.py: Implementation of the Zarnite Language Opportunity Score
layers/layer1_ssacomet.py: SSA-COMET model scoring (MTL and QE variants)layer2_metricx.py: MetricX-24 Google hybrid scoringlayer3_chrf.py: chrF++ sentence-level character and word-order baselinelayer4_llm_judge.py: Multi-model LLM-as-judge ensemble with digital presence gatingblaser3.py: Meta BLASER 3 quality scoringunderstanding_coherence.py: Universal discourse coherence forced binary-choice auditunderstanding_sib200.py: SIB-200 topic classification accuracyunderstanding_irokobench.py: IrokoBench multiple-choice reasoning accuracy
experiments/blaser3_vs_ssacomet.py: Validation experiment correlating BLASER 3 vs SSA-COMET against SSA-MTE human labels
run_translation_eval.py: Main translation quality evaluation routing pipelinezlos_core.py: Shared ZLOS scoring functions and baseline ranking loaderssensitivity_analysis.py: Part A: 2,000-draw Monte Carlo weights sensitivity sweepsensitivity_anchors.py: Part B: Log-scale population/economic anchors and cross-border bonus sensitivityrequirements.txt: Python package dependencies
Running Sensitivity Analysis
To replicate the statistical robustness tests and Monte Carlo sweeps documented in Section 7 of the report, run the following supplementary scripts from the command line:
# Run the 2,000-draw Monte Carlo weights sweep
python sensitivity_analysis.py
# Run the log-scale anchors and cross-border bonus sensitivity checks
python sensitivity_anchors.py
Requirements
- Python 3.10+
- Hugging Face account and token (with access to models)
- Gemini API key (for Gated LLM-as-judge and text understanding evaluation)
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