README.md

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 languages
  • zlos.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 scoring
  • layer3_chrf.py: chrF++ sentence-level character and word-order baseline
  • layer4_llm_judge.py: Multi-model LLM-as-judge ensemble with digital presence gating
  • blaser3.py: Meta BLASER 3 quality scoring
  • understanding_coherence.py: Universal discourse coherence forced binary-choice audit
  • understanding_sib200.py: SIB-200 topic classification accuracy
  • understanding_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 pipeline
• zlos_core.py: Shared ZLOS scoring functions and baseline ranking loaders
• sensitivity_analysis.py: Part A: 2,000-draw Monte Carlo weights sensitivity sweep
• sensitivity_anchors.py: Part B: Log-scale population/economic anchors and cross-border bonus sensitivity
• requirements.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)