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Add SETA v3: 497 newly solved tasks with Qwen3.8-27B evaluation traces
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"""Rebuild SETA v3 tasks.parquet and archive.parquet from the pinned v1 Parquet and metadata/selection.json.
pip install pyarrow huggingface_hub
python patch.py --out rebuilt
Optional --source is a local copy of seta_v1/tasks.parquet at the pinned revision.
"""
import argparse, hashlib, json
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
# Thinking-off columns (qwen38_*), kept_from_v2, then thinking-on columns (qwen38_thinking_*).
OFF = ['qwen38_graded_attempts', 'qwen38_successes', 'qwen38_missing_attempts', 'qwen38_same_payload', 'kept_from_v2']
ON = ['qwen38_thinking_graded_attempts', 'qwen38_thinking_successes', 'qwen38_thinking_verifier_graded_attempts',
'qwen38_thinking_retry_attempts', 'qwen38_thinking_retry_successes', 'qwen38_thinking_config']
EXTRA = [pa.field('qwen38_graded_attempts', pa.int64()), pa.field('qwen38_successes', pa.int64()),
pa.field('qwen38_missing_attempts', pa.int64()), pa.field('qwen38_same_payload', pa.bool_()),
pa.field('kept_from_v2', pa.bool_()),
*[pa.field(c, pa.int64()) for c in ON[:-1]], pa.field('qwen38_thinking_config', pa.string())]
def build(out, source=None):
m = json.loads((Path(__file__).parent / 'metadata/selection.json').read_text())
if source is None:
from huggingface_hub import hf_hub_download
source = Path(hf_hub_download(m['repo_id'], m['source_folder'] + '/tasks.parquet', repo_type='dataset',
revision=m['source_revision'], cache_dir=out / '.source-cache'))
with open(source, 'rb') as f:
assert hashlib.file_digest(f, 'sha256').hexdigest() == m['source_sha256']
out.mkdir(parents=True, exist_ok=True)
assert not any((out / n).exists() for n in ['tasks.parquet', 'archive.parquet'])
tasks, cols, s = m['tasks'], m['result_columns'], m['summary']
v1 = pq.ParquetFile(source)
base = v1.schema_arrow.remove_metadata()
def qcols(t):
e = tasks[t]
r = {'qwen38_graded_attempts': 0, 'qwen38_successes': None, 'qwen38_missing_attempts': None,
'qwen38_same_payload': None, 'kept_from_v2': e['kept_from_v2'], 'qwen38_thinking_graded_attempts': 0,
**{c: None for c in ON[1:]}}
r.update({k: e[k] for k in OFF + ON if k in e})
return r
def images(names):
im = json.loads(v1.schema_arrow.metadata[b'ot.images.v1'])
im['tasks'] = {k: v for k, v in im['tasks'].items() if k in names}
used = {e['bundle'] for v in im['tasks'].values() for e in v['environments']}
im['bundles'] = {k: v for k, v in im['bundles'].items() if k in used}
if 'missing' in im:
im['missing'] = [x for x in im['missing'] if x.get('task') in names]
if 'uncached_task_ids' in im:
im['uncached_task_ids'] = sorted(set(names) - set(im['tasks']))
if 'coverage' in im:
im['coverage'] = {'cached_tasks': len(im['tasks']), 'total_tasks': len(names)}
return json.dumps(im).encode()
release = json.dumps(s['release']).encode()
v2kept = {t for t, e in tasks.items() if e['kept_from_v2']}
off_new = {t for t, e in tasks.items() if e['kept_thinking_off'] and not e['kept_from_v2']}
on_new = {t for t, e in tasks.items() if e['kept'] and not e['kept_thinking_off']}
excluded = sorted(t for t, e in tasks.items() if not e['kept'])
kept = off_new | on_new
tschema = pa.schema([*base, *[pa.field(c, pa.int64()) for c in cols], *EXTRA])
tschema = tschema.with_metadata({b'ot.images.v1': images(kept), b'ot.release.v3': release})
aschema = tschema
for name, kind in [('archive_stage', pa.int64()), ('archive_reason', pa.string()), ('archive_labels', pa.list_(pa.string()))]:
aschema = aschema.set(aschema.get_field_index(name), pa.field(name, kind))
aschema = aschema.with_metadata({b'ot.images.v1': images(excluded), b'ot.release.v3': release})
def rows(select):
for batch in v1.iter_batches(batch_size=16):
chosen = [r for r in batch.to_pylist() if select(r['path'])]
for r in chosen:
assert r['content_sha256'] == tasks[r['path']]['content_sha256']
r.update({c: tasks[r['path']].get(c) for c in cols})
r.update(qcols(r['path']))
yield chosen
n = 0
with pq.ParquetWriter(out / 'tasks.parquet', tschema, compression='zstd') as w:
# Only new tasks: thinking-off successes, then additional thinking-on successes
for group in (off_new, on_new):
for chosen in rows(group.__contains__):
if chosen:
w.write_table(pa.Table.from_pylist(chosen, schema=tschema))
n += len(chosen)
assert n == s['kept']
n = 0
with pq.ParquetWriter(out / 'archive.parquet', aschema, compression='zstd') as w:
for chosen in rows(lambda t: not tasks[t]['kept']):
for r in chosen:
e = tasks[r['path']]
assert 'qwen38_thinking_graded_attempts' in e
reason = ('zero_success_v2_and_qwen38_both_configs' if 'qwen38_graded_attempts' in e
else 'zero_success_v2_and_qwen38_thinking_on')
r.update(archive_stage=6, archive_reason=reason, archive_labels=[reason])
if chosen:
w.write_table(pa.Table.from_pylist(chosen, schema=aschema))
n += len(chosen)
assert n == s['excluded']
print(json.dumps({'kept': s['kept'], 'excluded': n}))
if __name__ == '__main__':
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--out', type=Path, required=True)
p.add_argument('--source', type=Path)
a = p.parse_args()
build(a.out, a.source)