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f440f03 | 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 | """Modeļa novērtēšana."""
from __future__ import annotations
import argparse
import json
import logging
from maris_core.training.config import load_training_config
from maris_core.training.train import evaluate_with_config
def evaluate(
config_path: str | None = None,
model_path: str | None = None,
dataset_repo: str | None = None,
eval_dataset_repo: str | None = None,
benchmark_dataset_path: str | None = None,
) -> dict[str, float]:
"""Novērtē modeļa kvalitāti."""
config = load_training_config(
config_path,
overrides={
"dataset_repo": dataset_repo,
"eval_dataset_repo": eval_dataset_repo,
"benchmark_dataset_path": benchmark_dataset_path,
"output_dir": model_path,
},
)
return evaluate_with_config(config, model_path=model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Novērtē Maris AI modeli")
parser.add_argument("--config", help="JSON konfigurācijas fails")
parser.add_argument("--model-path", help="Modeļa ceļš vai HF repo")
parser.add_argument("--dataset-repo", help="HF dataset repo ID")
parser.add_argument("--eval-dataset-repo", help="Atsevišķs HF eval dataset repo ID")
parser.add_argument("--benchmark-dataset-path", help="Lokāls JSON benchmark datasets")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
results = evaluate(
args.config,
args.model_path,
args.dataset_repo,
args.eval_dataset_repo,
args.benchmark_dataset_path,
)
print(json.dumps(results, indent=2, ensure_ascii=False))
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