Datasets:
File size: 3,776 Bytes
a42d019 | 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 | #!/usr/bin/env python3
"""Honest input-only baselines. The symbolic baseline recognizes authored templates."""
import argparse
import collections
import json
import pathlib
from model_input import model_input
from verify import evaluate, read_lines
def symbolic(item):
question, cells = item["question_ar"], item["table"]["cells"]
if question.startswith("هل يمكن جمع"):
op = "incompatible_sum"
elif "اقسم إجمالي الناتج المحلي" in question:
op = "gdp_per_capita_join"
elif question.startswith("بعد ترتيب القيم المختلفة"):
op = "second_highest_years"
elif "القيمة الأعلى" in question:
op = "maximum_years"
elif "القيمة الأدنى" in question:
op = "minimum_years"
elif "نسبة التغير" in question:
op = "relative_change"
elif "التغير بالنقاط المئوية" in question:
op = "percentage_point_difference"
elif "الفرق بالوحدة المبينة" in question:
op = "difference"
elif "أكبر من" in question:
op = "greater_than"
elif "ما القيمة المسجلة" in question:
op = "lookup"
else:
raise ValueError("Unsupported authored template")
return evaluate(op, cells)
def first_cell(item):
row = item["table"]["cells"][0]
return {"status": "missing" if row["value"] is None else "ok", "value": row["value"], "unit": row["unit_code"]}
def run(release):
questions = sum([read_lines(release / "jsonl" / "questions" / (split + ".jsonl")) for split in ("train", "validation", "test")], [])
report = {"learned_model_evaluation": False,
"scope": "Full released rows, using only model_input(question). The symbolic method recognizes every declared authored Arabic template and is a programmed format ceiling, not general Arabic understanding, training benefit or model performance.", "baselines": {}}
for name, method in [("first_visible_cell", first_cell), ("template_aware_symbolic", symbolic)]:
stats = collections.defaultdict(lambda: {"rows": 0, "exact_answer": 0, "status_exact": 0})
predictions = []
for q in questions:
prediction = method(model_input(q))
gold = json.loads(q["gold_answer"])
for key in ("all", "split:" + q["split"], "operation:" + q["operation"], "diagnostic" if q["structure_holdout_diagnostic"] else "shared_operators"):
stats[key]["rows"] += 1
stats[key]["exact_answer"] += prediction == gold
stats[key]["status_exact"] += prediction["status"] == gold["status"]
evidence = [row["cell_id"] for row in model_input(q)["table"]["cells"]]
predictions.append({"id": q["id"], "answer": prediction,
"evidence_ids": evidence if name == "template_aware_symbolic" else evidence[:1]})
for item in stats.values():
item["exact_answer_rate"] = item["exact_answer"] / item["rows"]
item["status_exact_rate"] = item["status_exact"] / item["rows"]
report["baselines"][name] = dict(stats)
dest = release / "reports" / (name + ".predictions.jsonl")
dest.parent.mkdir(parents=True, exist_ok=True)
dest.write_text("".join(json.dumps(x, ensure_ascii=False, sort_keys=True) + "\n" for x in predictions))
return report
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--release", type=pathlib.Path, required=True)
args = parser.parse_args()
report = run(args.release)
(args.release / "reports" / "baselines.json").write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2))
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