Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
run_name: string
adapter: null
adapter_revision: null
base_model: string
load_in_4bit: bool
protocol: struct<decoding: string, batch_size: int64, max_new_tokens: int64, image_resize: list<item: int64>, (... 70 chars omitted)
child 0, decoding: string
child 1, batch_size: int64
child 2, max_new_tokens: int64
child 3, image_resize: list<item: int64>
child 0, item: int64
child 4, seed: int64
child 5, limit: null
child 6, prompt_template: string
child 7, prompt_sha: string
scoring_version: string
datasets: struct<vqa_rad: struct<repo: string, revision: string>, slake: struct<repo: string, revision: string (... 2 chars omitted)
child 0, vqa_rad: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
child 1, slake: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
env: struct<python: string, gpu: string, n_gpus: int64, torch: string, transformers: string, unsloth: str (... 85 chars omitted)
child 0, python: string
child 1, gpu: string
child 2, n_gpus: int64
child 3, torch: string
child 4, transformers: string
child 5, unsloth: string
child 6, trl: string
child 7, peft: string
child 8, bitsandbytes: string
child 9, vllm: string
child 10, datasets: string
determinism_check: struct<vqa_rad: struct<checked: int64, mismatches: int64>, slake: struct<checked: int64, mismatches: (... 8 chars omitted)
child 0, vqa_rad: struct<checked: int64, mismat
...
truncated_rate: double
child 9, mean_pred_words: double
child 1, closed: struct<n: int64, correct: double, correct_lenient: double, correct_strict: double, format_ok: double (... 124 chars omitted)
child 0, n: int64
child 1, correct: double
child 2, correct_lenient: double
child 3, correct_strict: double
child 4, format_ok: double
child 5, token_recall: double
child 6, token_f1: double
child 7, correct_ci95: list<item: double>
child 0, item: double
child 8, truncated_rate: double
child 9, mean_pred_words: double
child 2, open: struct<n: int64, correct: double, correct_lenient: double, correct_strict: double, format_ok: double (... 124 chars omitted)
child 0, n: int64
child 1, correct: double
child 2, correct_lenient: double
child 3, correct_strict: double
child 4, format_ok: double
child 5, token_recall: double
child 6, token_f1: double
child 7, correct_ci95: list<item: double>
child 0, item: double
child 8, truncated_rate: double
child 9, mean_pred_words: double
child 3, closed_polarity: struct<gt_yes_rate: double, pred_yes_rate: double, pred_no_rate: double, unparseable_rate: double, a (... 43 chars omitted)
child 0, gt_yes_rate: double
child 1, pred_yes_rate: double
child 2, pred_no_rate: double
child 3, unparseable_rate: double
child 4, acc_on_gt_yes: double
child 5, acc_on_gt_no: double
to
{'vqa_rad': {'overall': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'open': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed_polarity': {'gt_yes_rate': Value('float64'), 'pred_yes_rate': Value('float64'), 'pred_no_rate': Value('float64'), 'unparseable_rate': Value('float64'), 'acc_on_gt_yes': Value('float64'), 'acc_on_gt_no': Value('float64')}}, 'slake': {'overall': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'open': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed_polarity': {'gt_yes_rate': Value('float64'), 'pred_yes_rate': Value('float64'), 'pred_no_rate': Value('float64'), 'unparseable_rate': Value('float64'), 'acc_on_gt_yes': Value('float64'), 'acc_on_gt_no': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
run_name: string
adapter: null
adapter_revision: null
base_model: string
load_in_4bit: bool
protocol: struct<decoding: string, batch_size: int64, max_new_tokens: int64, image_resize: list<item: int64>, (... 70 chars omitted)
child 0, decoding: string
child 1, batch_size: int64
child 2, max_new_tokens: int64
child 3, image_resize: list<item: int64>
child 0, item: int64
child 4, seed: int64
child 5, limit: null
child 6, prompt_template: string
child 7, prompt_sha: string
scoring_version: string
datasets: struct<vqa_rad: struct<repo: string, revision: string>, slake: struct<repo: string, revision: string (... 2 chars omitted)
child 0, vqa_rad: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
child 1, slake: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
env: struct<python: string, gpu: string, n_gpus: int64, torch: string, transformers: string, unsloth: str (... 85 chars omitted)
child 0, python: string
child 1, gpu: string
child 2, n_gpus: int64
child 3, torch: string
child 4, transformers: string
child 5, unsloth: string
child 6, trl: string
child 7, peft: string
child 8, bitsandbytes: string
child 9, vllm: string
child 10, datasets: string
determinism_check: struct<vqa_rad: struct<checked: int64, mismatches: int64>, slake: struct<checked: int64, mismatches: (... 8 chars omitted)
child 0, vqa_rad: struct<checked: int64, mismat
...
truncated_rate: double
child 9, mean_pred_words: double
child 1, closed: struct<n: int64, correct: double, correct_lenient: double, correct_strict: double, format_ok: double (... 124 chars omitted)
child 0, n: int64
child 1, correct: double
child 2, correct_lenient: double
child 3, correct_strict: double
child 4, format_ok: double
child 5, token_recall: double
child 6, token_f1: double
child 7, correct_ci95: list<item: double>
child 0, item: double
child 8, truncated_rate: double
child 9, mean_pred_words: double
child 2, open: struct<n: int64, correct: double, correct_lenient: double, correct_strict: double, format_ok: double (... 124 chars omitted)
child 0, n: int64
child 1, correct: double
child 2, correct_lenient: double
child 3, correct_strict: double
child 4, format_ok: double
child 5, token_recall: double
child 6, token_f1: double
child 7, correct_ci95: list<item: double>
child 0, item: double
child 8, truncated_rate: double
child 9, mean_pred_words: double
child 3, closed_polarity: struct<gt_yes_rate: double, pred_yes_rate: double, pred_no_rate: double, unparseable_rate: double, a (... 43 chars omitted)
child 0, gt_yes_rate: double
child 1, pred_yes_rate: double
child 2, pred_no_rate: double
child 3, unparseable_rate: double
child 4, acc_on_gt_yes: double
child 5, acc_on_gt_no: double
to
{'vqa_rad': {'overall': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'open': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed_polarity': {'gt_yes_rate': Value('float64'), 'pred_yes_rate': Value('float64'), 'pred_no_rate': Value('float64'), 'unparseable_rate': Value('float64'), 'acc_on_gt_yes': Value('float64'), 'acc_on_gt_no': Value('float64')}}, 'slake': {'overall': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'open': {'n': Value('int64'), 'correct': Value('float64'), 'correct_lenient': Value('float64'), 'correct_strict': Value('float64'), 'format_ok': Value('float64'), 'token_recall': Value('float64'), 'token_f1': Value('float64'), 'correct_ci95': List(Value('float64')), 'truncated_rate': Value('float64'), 'mean_pred_words': Value('float64')}, 'closed_polarity': {'gt_yes_rate': Value('float64'), 'pred_yes_rate': Value('float64'), 'pred_no_rate': Value('float64'), 'unparseable_rate': Value('float64'), 'acc_on_gt_yes': Value('float64'), 'acc_on_gt_no': Value('float64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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