Datasets:
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Exact-substring scoring.
A response is correct if the whole golden answer appears in it as one
contiguous, case-insensitive substring. No tokenisation, no word boundaries,
no stemming.
is_correct = answer.lower() in response.lower()
See scripts/README.md for what this measures, where it gives false positives
and false negatives, and how it compares with word_overlap.py.
Usage:
python exact_substring.py --responses my_model_english.csv
python exact_substring.py --responses my_model_english.csv --out scored.csv
Input CSV: the language file from this dataset (columns `question`, `answer`,
`Domain`) with a `response` column added holding the model's raw output.
"""
import argparse
import sys
import pandas as pd
REQUIRED = ["question", "answer", "Domain", "response"]
def is_correct(answer, response):
"""The golden answer must appear as one contiguous run of characters."""
return str(answer).lower() in str(response).lower()
def report(df, label):
"""Print per-domain and combined accuracy."""
per_domain = df.groupby("Domain")["is_correct"].agg(["sum", "size"])
print(f"\n{label}\n")
print(f"{'Domain':<20} {'Correct':>8} {'Total':>7} {'Accuracy':>10}")
print("-" * 48)
for domain, row in per_domain.iterrows():
acc = row["sum"] / row["size"] * 100
print(f"{domain:<20} {int(row['sum']):>8} {int(row['size']):>7} {acc:>9.2f}%")
correct, total = int(df["is_correct"].sum()), len(df)
blank = int((df["response"].fillna("").astype(str).str.strip() == "").sum())
print("-" * 48)
print(f"{'COMBINED':<20} {correct:>8} {total:>7} {correct / total * 100:>9.2f}%")
print("\nCombined is the micro-average over all pooled questions, which is")
print("identical to weighting each domain by its size.")
if blank:
print(f"Blank responses: {blank} (scored incorrect)")
def main():
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--responses", required=True,
help="CSV with columns: question, answer, Domain, response")
ap.add_argument("--out", help="Optional path to write per-question verdicts")
args = ap.parse_args()
df = pd.read_csv(args.responses)
missing = [c for c in REQUIRED if c not in df.columns]
if missing:
sys.exit(f"Error: {args.responses} is missing column(s): {', '.join(missing)}\n"
f"Found: {', '.join(df.columns)}")
# A blank answer would match every response vacuously; flag rather than
# silently inflate the score.
blank_answers = int((df["answer"].fillna("").astype(str).str.strip() == "").sum())
if blank_answers:
print(f"Warning: {blank_answers} row(s) have an empty golden answer. "
f"These match any response and will score correct.", file=sys.stderr)
df["is_correct"] = [is_correct(a, r) for a, r in zip(df["answer"], df["response"])]
report(df, f"Exact substring — {args.responses}")
if args.out:
df.to_csv(args.out, index=False)
print(f"\nPer-question verdicts written to {args.out}")
if __name__ == "__main__":
main()
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