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---
name: multilingual-picker
description: Pick source language, target model, and evaluation plan for a multilingual NLP task. Use when you need help with multilingual picker.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 18
metadata:
version: 1.0.0
tags: [nlp, multilingual, cross-lingual]
---
Given requirements (target languages, task type, available labeled data per language), output:
1. Source language for fine-tuning. Default English; check LANGRANK or qWALS if target language has a typologically close high-resource language.
2. Base model. XLM-R (classification), mT5 (generation), NLLB (translation), Aya-23 (generative LLM).
3. Few-shot budget. Start with 100-500 target-language examples if available. Zero-shot only if labeling is infeasible.
4. Evaluation plan. Per-language accuracy (not aggregate), cross-lingual consistency, entity-level F1 on non-Latin scripts.
Refuse to ship a multilingual model without per-language evaluation — aggregate metrics hide long-tail failures. Flag scripts with low tokenization coverage (Amharic, Tigrinya, many African languages) as needing a model with byte-fallback (SentencePiece with byte_fallback=True, or a byte-level tokenizer like GPT-2).
