Spaces:
Running
Running
Commit History
-
feat: OpenEEGBench submission flow (arena runs the eval)
Replace the leftover LLM-leaderboard submission with a first-class OpenEEGBench flow. Users submit a model reference (model_cls + checkpoint + finetuning strategy/head/datasets); the backend validates it, enforces a 5-per-month per-user cap, and queues a request to braindecode/requests. A worker (stub) runs oeb.benchmark() and maps results to braindecode/contents. Backend: - submission_config: OEB enums/defaults (single source of truth) - submissions: pure validation, request building, monthly cap - results_mapping: OEB DataFrame -> contents schema (balanced_accuracy) - model_validation: EEG/OEB weights-source + license checks - OEB-shaped submit endpoint/service; eval worker stub (run_eval_worker.py) Frontend: - OEB submission form (model_cls, weights source, strategies, heads, PEFT modules, datasets, seeds, contact/visibility) with conditional fields - rewritten submission guide (5/month, help -> OpenEEGBench issues, citation) - queue/limit copy aligned; fixed duplicate style key…4db3be0
-
Add HF eval-results benchmark registry and API endpoint
- Add canonical EEG_BENCHMARKS registry (8 datasets) in config/benchmarks.py - Add GET /api/benchmarks endpoint for benchmark metadata - Refactor leaderboard transform_data to loop over registry instead of hardcoding - Extract _build_entry_id helper for consistent unique ID generation - Add push_eval_yaml.py script to register benchmarks on HF Hub - Add pyyaml dependency6f5cc9b
-
Migrate from Gradio to React + FastAPI architecture
Replace the old Gradio-based leaderboard with a React frontend and FastAPI backend, adapted from the Open LLM Leaderboard codebase for EEG model evaluation. Backend: FastAPI app with leaderboard, model submission, and voting endpoints. Frontend: React + MUI with leaderboard table, filters, model submission, voting, about page, and citations. Infrastructure: Dockerfile, docker-compose, and env configuration for HF Spaces.ee472e7