Datasets:
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Minimal example: compute accuracy on ChartReal using normalize_answer.
Usage:
python3 eval/eval_example.py path/to/predictions.jsonl path/to/data.jsonl
"""
import json
import sys
from normalize import normalize_answer
def main():
if len(sys.argv) != 3:
print("Usage: python3 eval_example.py <predictions.jsonl> <data.jsonl>")
sys.exit(1)
preds_path, data_path = sys.argv[1], sys.argv[2]
# Index gold answers by example_id
gold = {}
with open(data_path, encoding="utf-8") as f:
for line in f:
d = json.loads(line)
gold[d["example_id"]] = d
# Score predictions
n = c = 0
with open(preds_path, encoding="utf-8") as f:
for line in f:
p = json.loads(line)
ex = gold.get(p["example_id"])
if not ex:
continue
n += 1
if normalize_answer(
p["prediction_raw"], ex["answer"], ex["answer_type"],
gold_numeric=ex.get("answer_numeric"),
):
c += 1
print(f"Accuracy: {c}/{n} = {c/n*100:.2f}%")
if __name__ == "__main__":
main()
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