Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
structured-decisions
calibration
probabilistic-classification
system-one
workflow-evaluation
Synthetic
License:
| """Create the Typed Decisions eval YAML and open a PR on the model repo. | |
| Usage: | |
| python submit_results.py MODEL_ID [RESULTS_JSON] [--mode general|specialist] [--dry-run] | |
| Reads the summary produced by score_with_jobs.py (accuracy, kl_from_gold, | |
| brier, ece), writes the Hub eval-results YAML, and opens a pull request on | |
| the model repo (`.eval_results/<model>.yaml`). With --dry-run the YAML is | |
| printed instead of uploaded. | |
| """ | |
| import argparse | |
| import datetime | |
| import json | |
| BENCH = "LocalLLaMA/typed-decisions" | |
| TASKS = ["accuracy", "kl_from_gold", "brier", "ece"] | |
| NOTE_SPECIALIST = ( | |
| "Model was fitted/fine-tuned on this benchmark's train split; " | |
| "not comparable with zero-shot rows." | |
| ) | |
| def build_yaml(summary: dict, mode: str, date: str) -> str: | |
| lines = [] | |
| for task_id in TASKS: | |
| value = round(float(summary[task_id]), 4) | |
| lines += [ | |
| "- dataset:", | |
| f" id: {BENCH}", | |
| f" task_id: {task_id}", | |
| f" value: {value}", | |
| f' date: "{date}"', | |
| " source:", | |
| f" url: https://huggingface.co/datasets/{BENCH}", | |
| ' name: "Typed Decisions benchmark run on HF Jobs"', | |
| ] | |
| if mode == "specialist": | |
| lines.append(f" notes: {json.dumps(NOTE_SPECIALIST)}") | |
| return "\n".join(lines) + "\n" | |
| def main(): | |
| ap = argparse.ArgumentParser(description=__doc__) | |
| ap.add_argument("model_id", help="model repo the results belong to, e.g. org/model") | |
| ap.add_argument("results", nargs="?", default="results.json", help="summary JSON from score_with_jobs.py") | |
| ap.add_argument("--mode", choices=["general", "specialist"], default="general", | |
| help="specialist = fitted on the benchmark's train split") | |
| ap.add_argument("--dry-run", action="store_true", help="print the YAML instead of opening a PR") | |
| args = ap.parse_args() | |
| with open(args.results) as f: | |
| summary = json.load(f) | |
| missing = [t for t in TASKS if t not in summary] | |
| if missing: | |
| raise SystemExit(f"results file is missing task keys: {missing}") | |
| date = datetime.date.today().isoformat() | |
| yaml_text = build_yaml(summary, args.mode, date) | |
| file_name = args.model_id.replace("/", "__") + ".yaml" | |
| if args.dry_run: | |
| print(yaml_text) | |
| return | |
| from huggingface_hub import HfApi | |
| api = HfApi() | |
| commit = api.upload_file( | |
| path_or_fileobj=yaml_text.encode(), | |
| path_in_repo=f".eval_results/{file_name}", | |
| repo_id=args.model_id, | |
| repo_type="model", | |
| create_pr=True, | |
| commit_message="Add Typed Decisions eval results", | |
| commit_description=( | |
| f"{args.mode} run on {date}: accuracy {summary['accuracy']}, " | |
| f"KL {summary['kl_from_gold']}, Brier {summary['brier']}, ECE {summary['ece']} " | |
| f"({summary['n_decisions']} decisions, one request per case)." | |
| ), | |
| ) | |
| pr_url = getattr(commit, "pr_url", None) or getattr(commit, "pull_request_url", "") | |
| print(f"[submit] eval YAML opened as a PR on {args.model_id}: {pr_url}", flush=True) | |
| if __name__ == "__main__": | |
| main() | |