Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
structured-decisions
calibration
probabilistic-classification
system-one
workflow-evaluation
Synthetic
License:
File size: 3,154 Bytes
e49e13d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | """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()
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