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crosslingual-rule-following
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crv-analysis

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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'band', 'judge'}) and 3 missing columns ({'rater', 'level', 'factor'}).

This happened while the csv dataset builder was generating data using

hf://datasets/crosslingual-rule-following/crv-analysis/native-validation-yo-2026-10-30/tables/agreement_by_judge_band.csv (at revision 0e44287c58c603b5754eebdb50380b74b84a2156), ['hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_factor.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_judge_band.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_note.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/band_populations.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/disagreement_patterns.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/disagreements.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/judge_pair_agreement.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/judge_verdict_by_native.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/label_audit_echo_counts.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/label_audit_echo_rows.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/native_vs_llm.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/score_ranking.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_bounds.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_note_reasons.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_rate.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              judge: string
              band: string
              n: int64
              prompts: int64
              native_held_n: int64
              native_violated_n: int64
              both_held: int64
              false_violations: int64
              false_helds: int64
              both_violated: int64
              effective_n: double
              stat: string
              value: double
              ci_low: double
              ci_high: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2016
              to
              {'factor': Value('string'), 'level': Value('string'), 'rater': Value('string'), 'n': Value('int64'), 'prompts': Value('int64'), 'native_held_n': Value('int64'), 'native_violated_n': Value('int64'), 'both_held': Value('int64'), 'false_violations': Value('int64'), 'false_helds': Value('int64'), 'both_violated': Value('int64'), 'effective_n': Value('float64'), 'stat': Value('string'), 'value': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'band', 'judge'}) and 3 missing columns ({'rater', 'level', 'factor'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/crosslingual-rule-following/crv-analysis/native-validation-yo-2026-10-30/tables/agreement_by_judge_band.csv (at revision 0e44287c58c603b5754eebdb50380b74b84a2156), ['hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_factor.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_judge_band.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/agreement_by_note.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/band_populations.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/disagreement_patterns.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/disagreements.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/judge_pair_agreement.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/judge_verdict_by_native.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/label_audit_echo_counts.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/label_audit_echo_rows.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/native_vs_llm.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/score_ranking.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_bounds.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_note_reasons.csv', 'hf://datasets/crosslingual-rule-following/crv-analysis@0e44287c58c603b5754eebdb50380b74b84a2156/native-validation-yo-2026-10-30/tables/unsure_rate.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

factor
string
level
string
rater
string
n
int64
prompts
int64
native_held_n
int64
native_violated_n
int64
both_held
int64
false_violations
int64
false_helds
int64
both_violated
int64
effective_n
float64
stat
string
value
float64
ci_low
float64
ci_high
float64
model
llama
gpt
69
69
39
30
33
6
4
26
69
agreement
0.855072
0.768116
0.942029
model
llama
gpt
69
69
39
30
33
6
4
26
69
kappa
0.707379
0.528608
0.863317
model
llama
gpt
69
69
39
30
33
6
4
26
69
false_violation
0.153846
0.05
0.277778
model
llama
gpt
69
69
39
30
33
6
4
26
69
false_held
0.133333
0.027027
0.269231
model
llama
gpt
69
69
39
30
33
6
4
26
69
native_held_given_judge_violated
0.1875
0.064466
0.341479
model
llama
gpt
69
69
39
30
33
6
4
26
69
native_violated_given_judge_held
0.108108
0.023256
0.212175
model
llama
gpt
69
69
39
30
33
6
4
26
69
pabak
0.710145
0.536232
0.884058
model
llama
gpt
69
69
39
30
33
6
4
26
69
gwet_ac1
0.713098
0.538654
0.884082
model
llama
gemini
68
68
39
29
34
5
7
22
68
agreement
0.823529
0.735294
0.911765
model
llama
gemini
68
68
39
29
34
5
7
22
68
kappa
0.636039
0.438835
0.806823
model
llama
gemini
68
68
39
29
34
5
7
22
68
false_violation
0.128205
0.028571
0.243902
model
llama
gemini
68
68
39
29
34
5
7
22
68
false_held
0.241379
0.096774
0.4
model
llama
gemini
68
68
39
29
34
5
7
22
68
native_held_given_judge_violated
0.185185
0.043478
0.346196
model
llama
gemini
68
68
39
29
34
5
7
22
68
native_violated_given_judge_held
0.170732
0.068182
0.288904
model
llama
gemini
68
68
39
29
34
5
7
22
68
pabak
0.647059
0.470588
0.823529
model
llama
gemini
68
68
39
29
34
5
7
22
68
gwet_ac1
0.657718
0.474048
0.825939
model
llama
deepseek
69
69
40
29
39
1
23
6
69
agreement
0.652174
0.536232
0.753623
model
llama
deepseek
69
69
40
29
39
1
23
6
69
kappa
0.20308
0.03646
0.378378
model
llama
deepseek
69
69
40
29
39
1
23
6
69
false_violation
0.025
0
0.081081
model
llama
deepseek
69
69
40
29
39
1
23
6
69
false_held
0.793103
0.633333
0.933333
model
llama
deepseek
69
69
40
29
39
1
23
6
69
native_held_given_judge_violated
0.142857
0
0.5
model
llama
deepseek
69
69
40
29
39
1
23
6
69
native_violated_given_judge_held
0.370968
0.253962
0.4925
model
llama
deepseek
69
69
40
29
39
1
23
6
69
pabak
0.304348
0.072464
0.507246
model
llama
deepseek
69
69
40
29
39
1
23
6
69
gwet_ac1
0.433846
0.184366
0.640503
model
llama
mean
66
66
38
28
35
3
5
23
66
agreement
0.878788
0.787879
0.954545
model
llama
mean
66
66
38
28
35
3
5
23
66
kappa
0.749526
0.570018
0.89661
model
llama
mean
66
66
38
28
35
3
5
23
66
false_violation
0.078947
0
0.176503
model
llama
mean
66
66
38
28
35
3
5
23
66
false_held
0.178571
0.045405
0.333333
model
llama
mean
66
66
38
28
35
3
5
23
66
native_held_given_judge_violated
0.115385
0
0.259259
model
llama
mean
66
66
38
28
35
3
5
23
66
native_violated_given_judge_held
0.125
0.027778
0.236873
model
llama
mean
66
66
38
28
35
3
5
23
66
pabak
0.757576
0.575758
0.909091
model
llama
mean
66
66
38
28
35
3
5
23
66
gwet_ac1
0.765333
0.599306
0.910102
model
qwen
gpt
25
25
14
11
8
6
0
11
25
agreement
0.76
0.6
0.92
model
qwen
gpt
25
25
14
11
8
6
0
11
25
kappa
0.539877
0.260355
0.834437
model
qwen
gpt
25
25
14
11
8
6
0
11
25
false_violation
0.428571
0.181818
0.7
model
qwen
gpt
25
25
14
11
8
6
0
11
25
false_held
0
0
0
model
qwen
gpt
25
25
14
11
8
6
0
11
25
native_held_given_judge_violated
0.352941
0.125
0.571429
model
qwen
gpt
25
25
14
11
8
6
0
11
25
native_violated_given_judge_held
0
0
0
model
qwen
gpt
25
25
14
11
8
6
0
11
25
pabak
0.52
0.2
0.84
model
qwen
gpt
25
25
14
11
8
6
0
11
25
gwet_ac1
0.526814
0.20173
0.846154
model
qwen
gemini
25
25
14
11
9
5
1
10
25
agreement
0.76
0.599
0.92
model
qwen
gemini
25
25
14
11
9
5
1
10
25
kappa
0.53125
0.193548
0.83871
model
qwen
gemini
25
25
14
11
9
5
1
10
25
false_violation
0.357143
0.117647
0.625
model
qwen
gemini
25
25
14
11
9
5
1
10
25
false_held
0.090909
0
0.3
model
qwen
gemini
25
25
14
11
9
5
1
10
25
native_held_given_judge_violated
0.333333
0.111111
0.571429
model
qwen
gemini
25
25
14
11
9
5
1
10
25
native_violated_given_judge_held
0.1
0
0.333333
model
qwen
gemini
25
25
14
11
9
5
1
10
25
pabak
0.52
0.198
0.84
model
qwen
gemini
25
25
14
11
9
5
1
10
25
gwet_ac1
0.520767
0.201278
0.846154
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
agreement
0.695652
0.521739
0.869565
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
kappa
0.290749
-0.082353
0.646221
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
false_violation
0.071429
0
0.230769
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
false_held
0.666667
0.333333
1
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
native_held_given_judge_violated
0.25
0
1
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
native_violated_given_judge_held
0.315789
0.125
0.526393
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
pabak
0.391304
0.043478
0.73913
model
qwen
deepseek
23
23
14
9
13
1
6
3
23
gwet_ac1
0.488076
0.104425
0.809655
model
qwen
mean
23
23
14
9
9
5
1
8
23
agreement
0.73913
0.565217
0.913043
model
qwen
mean
23
23
14
9
9
5
1
8
23
kappa
0.492647
0.141791
0.817496
model
qwen
mean
23
23
14
9
9
5
1
8
23
false_violation
0.357143
0.110833
0.615385
model
qwen
mean
23
23
14
9
9
5
1
8
23
false_held
0.111111
0
0.375
model
qwen
mean
23
23
14
9
9
5
1
8
23
native_held_given_judge_violated
0.384615
0.117647
0.666667
model
qwen
mean
23
23
14
9
9
5
1
8
23
native_violated_given_judge_held
0.1
0
0.333333
model
qwen
mean
23
23
14
9
9
5
1
8
23
pabak
0.478261
0.130435
0.826087
model
qwen
mean
23
23
14
9
9
5
1
8
23
gwet_ac1
0.479245
0.132075
0.828996
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
agreement
0.882353
0.705882
1
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
kappa
null
null
null
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
false_violation
0.5
0
1
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
false_held
0.066667
0
0.214286
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
native_held_given_judge_violated
0.066667
0
0.214286
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
native_violated_given_judge_held
0.5
0
1
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
pabak
null
null
null
category
mandatory_referral
gpt
17
17
2
15
1
1
1
14
17
gwet_ac1
null
null
null
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
agreement
0.705882
0.470588
0.882353
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
kappa
null
null
null
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
false_violation
0.5
0
1
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
false_held
0.266667
0.066667
0.5
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
native_held_given_judge_violated
0.083333
0
0.272727
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
native_violated_given_judge_held
0.8
0.333333
1
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
pabak
null
null
null
category
mandatory_referral
gemini
17
17
2
15
1
1
4
11
17
gwet_ac1
null
null
null
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
agreement
0.533333
0.266667
0.733333
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
kappa
null
null
null
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
false_violation
0.5
0
1
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
false_held
0.461538
0.214286
0.733333
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
native_held_given_judge_violated
0.125
0
0.4
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
native_violated_given_judge_held
0.857143
0.571032
1
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
pabak
null
null
null
category
mandatory_referral
deepseek
15
15
2
13
1
1
6
7
15
gwet_ac1
null
null
null
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
agreement
0.733333
0.466667
0.933333
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
kappa
null
null
null
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
false_violation
0.5
0
1
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
false_held
0.230769
0
0.466667
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
native_held_given_judge_violated
0.090909
0
0.3
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
native_violated_given_judge_held
0.75
0.2325
1
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
pabak
null
null
null
category
mandatory_referral
mean
15
15
2
13
1
1
3
10
15
gwet_ac1
null
null
null
category
no_dosage
gpt
13
13
10
3
9
1
1
2
13
agreement
0.846154
0.615385
1
category
no_dosage
gpt
13
13
10
3
9
1
1
2
13
kappa
null
null
null
category
no_dosage
gpt
13
13
10
3
9
1
1
2
13
false_violation
0.1
0
0.333333
category
no_dosage
gpt
13
13
10
3
9
1
1
2
13
false_held
0.333333
0
1
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