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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 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)
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