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Datasets:
lexuanbach
/
vebench

Tasks:
Other
Tags:
databases
query-equivalence
feature-engineering
rewrite-mining
SMT
License:
Dataset card Data Studio Files
xet
 Community
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
template: string
path: string
src: string
id: int64
code: string
to
{'id': Value('int64'), 'code': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              template: string
              path: string
              src: string
              id: int64
              code: string
              to
              {'id': Value('int64'), 'code': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1348, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 890, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 951, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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id
int64
code
string
0
df.groupby('g').agg(n=('a', 'sum'))
1
df.assign(t = df.a + df.b)[df.a > 51]
2
df.assign(t = df.a + df.b)[df.a > 45]
3
df[df.a > 64][['a', 'g']]
4
df[(df.a > 79) & (df.g == 32)]
5
df[(df.a > 39) & (df.b < 12)]
6
df.merge(u, on='g')[(df.a > 60) & (df.g == 71)]
7
df[df.a > 40].apply(lambda r: r.a * 2, axis=1)
8
df[df.a > 70][['a', 'g']]
9
df.merge(u, on='g')[df.a > 7]
10
df[(df.a > 92) & (df.g == 51)]
11
df[df.a > 78]
12
df.merge(u, on='g')[(df.a > 31) & (df.g == 93)]
13
df[(df.a > 24) & (df.g == 72)]
14
df[(df.a > 69) & (df.b < 57)]
15
df.sort_values('a').head(65)
16
df.sort_values('a').head(90)
17
df[df.a > 77][['a', 'g']]
18
df.assign(t = df.a + df.b)[df.a > 11]
19
df.merge(u, on='g')[(df.a > 73) & (df.g == 30)]
20
df[df.a > 23][['a', 'g']]
21
df.merge(u, on='g')[df.a > 60]
22
df.groupby('g').rolling(19).mean()
23
df.groupby('g').agg(n=('a', 'sum'))
24
df[df.a > 27][['a', 'g']]
25
df.groupby('g').agg(n=('a', 'sum'))
26
df.merge(u, on='g')[(df.a > 10) & (df.g == 41)]
27
df.assign(t = df.a + df.b)[df.a > 75]
28
df[df.a > 31][['a', 'g']]
29
df[(df.a > 90) & (df.g == 28)]
30
df[(df.a > 42) & (df.b < 54)]
31
df[(df.a > 100) & (df.g == 18)]
32
df[df.a > 5][['a', 'g']]
33
df[(df.a > 3) & (df.g == 15)]
34
df[(df.a > 50) & (df.g == 11)]
35
df[(df.a > 4) & (df.g == 77)]
36
df.groupby('g').rolling(91).mean()
37
df[df.a > 86]
38
df[(df.a > 33) & (df.g == 8)]
39
df.merge(u, on='g')[df.a > 44]
40
df[df.a > 5].apply(lambda r: r.a * 2, axis=1)
41
df[df.a > 25].apply(lambda r: r.a * 2, axis=1)
42
df.assign(t = df.a + df.b)[df.a > 72]
43
df[df.a > 98][['a', 'g']]
44
df[(df.a > 20) & (df.b < 43)]
45
df[(df.a > 76) & (df.g == 56)]
46
df[df.a > 60]
47
df.merge(u, on='g')[df.a > 83]
48
df.merge(u, on='g')[df.a > 19]
49
df.assign(t = df.a + df.b)[df.a > 94]
50
df[df.a > 69]
51
df[df.a > 45].apply(lambda r: r.a * 2, axis=1)
52
df.merge(u, on='g')[(df.a > 75) & (df.g == 81)]
53
df[(df.a > 91) & (df.b < 39)]
54
df.groupby('g').agg(n=('a', 'sum'))
55
df.groupby('g').rolling(89).mean()
56
df.groupby('g').rolling(81).mean()
57
df.groupby('g').agg(n=('a', 'sum'))
58
df.groupby('g').agg(n=('a', 'sum'))
59
df[(df.a > 57) & (df.b < 8)]
60
df[(df.a > 57) & (df.b < 67)]
61
df[df.a > 4]
62
df.assign(t = df.a + df.b)[df.a > 6]
63
df.pivot_table(index='g', values='a')
64
df.groupby('g').agg(n=('a', 'sum'))
65
df[df.a > 1][['a', 'g']]
66
df[(df.a > 24) & (df.g == 15)]
67
df[df.a > 38][['a', 'g']]
68
df[(df.a > 12) & (df.b < 60)]
69
df.groupby('g').agg(n=('a', 'sum'))
70
df[df.a > 14].apply(lambda r: r.a * 2, axis=1)
71
df.sort_values('a').head(14)
72
df[df.a > 5]
73
df.merge(u, on='g')[df.a > 71]
74
df[df.a > 95]
75
df.assign(t = df.a + df.b)[df.a > 91]
76
df.groupby('g').agg(n=('a', 'sum'))
77
df[df.a > 75].apply(lambda r: r.a * 2, axis=1)
78
df.groupby('g').rolling(34).mean()
79
df.merge(u, on='g')[(df.a > 91) & (df.g == 11)]
80
df[df.a > 5]
81
df.sort_values('a').head(46)
82
df[(df.a > 37) & (df.b < 14)]
83
df[df.a > 6][['a', 'g']]
84
df.pivot_table(index='g', values='a')
85
df.merge(u, on='g')[df.a > 53]
86
df[df.a > 60].apply(lambda r: r.a * 2, axis=1)
87
df.merge(u, on='g')[(df.a > 15) & (df.g == 61)]
88
df[df.a > 54].apply(lambda r: r.a * 2, axis=1)
89
df[(df.a > 21) & (df.b < 80)]
90
df[(df.a > 8) & (df.g == 10)]
91
df.groupby('g').rolling(7).mean()
92
df[df.a > 71].apply(lambda r: r.a * 2, axis=1)
93
df.assign(t = df.a + df.b)[df.a > 100]
94
df[df.a > 54][['a', 'g']]
95
df.merge(u, on='g')[df.a > 74]
96
df.groupby('g').rolling(45).mean()
97
df.pivot_table(index='g', values='a')
98
df[df.a > 15][['a', 'g']]
99
df[df.a > 82][['a', 'g']]
End of preview.

License: mixed permissive. The real-code components carry their original SPDX licenses (predominantly MIT, Apache-2.0, BSD-3-Clause); per-item license tags ship with each record.

VeBench

The labeled benchmark for VeRA: Mining and Verifying Feature-Pipeline Rewrites with an MLIR-Style IR and SMT (ICDE 2026): nine components for verified feature-pipeline rewrite mining. Generated components regenerate from fixed seeds via the artifact (scripts/export_bench.py); real-code components ship as license-stamped samples.

File Component Size Origin
sql_mining_corpus.jsonl SQL mining corpus 300 generated
dataframe_mining_corpus.jsonl dataframe mining corpus 300 generated
soundness_rules.jsonl soundness rule set 31 hand-built
mutation_rules.jsonl mutation rule set 400 mechanical
hf_jupyter_sample.jsonl real-notebook scan 1500 public GitHub
feast_sample.jsonl Feast feature definitions 20 Feast templates
job_sample.jsonl JOB coverage suite 113 IMDb (JOB)
tpch_coverage.jsonl TPC-H coverage suite 22 TPC-H
tpcds_coverage.jsonl TPC-DS coverage suite 99 TPC-DS

See the artifact README.md and REPRODUCE.md for provenance, licenses, labels (lowering status, admission tier, guard, verdict), and regeneration.
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