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feat(data): UniversalCEFR EDA report and M1 training-mix decision (#1)
Arthur_DiazUnverified signature## What & why Discovers and profiles the UniversalCEFR subsets to fix the M1 training mix (reading-level classifier). The committed EDA reports are the evidence behind the updated ADR 0003. ## Changes - scripts/eda_universalcefr.py: org discovery, per-subset stats, markdown report (replaces the M0 placeholder download_data.py) - docs/evals/m1_data_eda.md + m1_data_eda_all.md: committed reports (EN + all) - docs/adr/0003: training-mix decision + multilingual addendum - pyproject/uv.lock: huggingface-hub added to the data group ## Decision - In (reference only): cambridge_exams_en, elg_cefr_en, cefr_sp_en, readme_en; non-EN reference subsets as a *measured* experiment arm (+13.6k rows) - Out: icle500_en, cefr_asag_en (learner production → M3) - Labels: X+ → X (recovers 114 en + 1,505 nl rows); drop NA / EMPTY / unrated / Unassessable / bare A-B-C - Unit: passage (docs chunked ~200 words, label inherited); split at document level, stratified corpus×level, fixed seed - Key constr…aaf2b86 -
feat: M0 walking skeleton (app, service protocols, CI/CD, Space deploy)
- Gradio app: one tab per skill + diagnostics tab with LLM ping - Provider-agnostic services via typing.Protocol + tested fakes (single OpenAI-compatible LLM client: Gemini/Mistral/Ollama/OpenAI) - Env-driven config (pydantic-settings), offline-safe defaults - CI: ruff + pytest, Docker image pushed to GHCR on main - Repo sync workflow to HF Space (Docker SDK, cpu-basic) - ADRs: stack & architecture, LLM provider strategy, datasets & licensing00d5ae5