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
id: string
source_uri: string
instruction: string
input: string
output: string
chosen: string
rejected: string
label: double
responses: list<item: null>
child 0, item: null
reward_scores: list<item: null>
child 0, item: null
task_type: string
metadata: struct<page: int64, generation_source: string, source_sample_id: string, difficulty: string, parent_ (... 79 chars omitted)
child 0, page: int64
child 1, generation_source: string
child 2, source_sample_id: string
child 3, difficulty: string
child 4, parent_heading: string
child 5, chunk_index: int64
child 6, content_type: string
child 7, source_file: string
provenance_chain: list<item: struct<step_name: string, step_version: string, timestamp: string, config_hash: string, n (... 974 chars omitted)
child 0, item: struct<step_name: string, step_version: string, timestamp: string, config_hash: string, notes: struc (... 962 chars omitted)
child 0, step_name: string
child 1, step_version: string
child 2, timestamp: string
child 3, config_hash: string
child 4, notes: struct<table_extraction: string, reason: string, page: int64, source_file: string, chunk_index: int6 (... 866 chars omitted)
child 0, table_extraction: string
child 1, reason: string
child 2, page: int64
child 3, source_file: string
child 4, chunk_index: int64
child 5, parent_heading: string
child 6, chunk_strategy: string
child 7, overlap_
...
, rejected_count: int64
child 8, MaxSamplesTruncator: struct<input_count: int64, output_count: int64>
child 0, input_count: int64
child 1, output_count: int64
child 9, AlpacaExporter: struct<exported_count: int64>
child 0, exported_count: int64
child 10, ShareGPTExporter: struct<exported_count: int64>
child 0, exported_count: int64
tool_versions: struct<curatorkit: string, python: string>
child 0, curatorkit: string
child 1, python: string
token_stats: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_tokens: int64>
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_tokens: int64
pipeline_config_hash: string
source_files: list<item: struct<path: string>>
child 0, item: struct<path: string>
child 0, path: string
diversity_stats: null
run_timestamp: string
diagnostic_files: list<item: null>
child 0, item: null
dedup_stats: struct<>
minhash_threshold: null
sampler_stats: null
rejected_breakdown: struct<table_stubbed:extract_tables_disabled: int64, hallucination_contract_failed:0.50: int64, hall (... 153 chars omitted)
child 0, table_stubbed:extract_tables_disabled: int64
child 1, hallucination_contract_failed:0.50: int64
child 2, hallucination_contract_failed:0.00: int64
child 3, hallucination_contract_failed:0.60: int64
child 4, below_reward_threshold:0.00: int64
child 5, below_reward_threshold:0.50: int64
wall_clock_seconds: double
diagnostic_stats: null
to
{'pipeline_config_hash': Value('string'), 'run_timestamp': Value('string'), 'source_files': List({'path': Value('string')}), 'stage_counts': {'PDFReader': {'output_count': Value('int64'), 'rejected_count': Value('int64')}, 'SchemaGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'ExactDeduplicator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'MinHashDeduplicator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'TextCleaner': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'QAGenerationTask': {'input_count': Value('int64'), 'output_count': Value('int64'), 'rejected_count': Value('int64')}, 'HallucinationGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'RewardGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'MaxSamplesTruncator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'AlpacaExporter': {'exported_count': Value('int64')}, 'ShareGPTExporter': {'exported_count': Value('int64')}}, 'rejected_breakdown': {'table_stubbed:extract_tables_disabled': Value('int64'), 'hallucination_contract_failed:0.50': Value('int64'), 'hallucination_contract_failed:0.00': Value('int64'), 'hallucination_contract_failed:0.60': Value('int64'), 'below_reward_threshold:0.00': Value('int64'), 'below_reward_threshold:0.50': Value('int64')}, 'dedup_stats': {}, 'minhash_threshold': Value('null'), 'sampler_stats': Value('null'), 'token_stats': {'total_prompt_tokens': Value('int64'), 'total_completion_tokens': Value('int64'), 'total_tokens': Value('int64')}, 'wall_clock_seconds': Value('float64'), 'tool_versions': {'curatorkit': Value('string'), 'python': Value('string')}, 'diversity_stats': Value('null'), 'diagnostic_stats': Value('null'), 'diagnostic_files': List(Value('null'))}
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
id: string
source_uri: string
instruction: string
input: string
output: string
chosen: string
rejected: string
label: double
responses: list<item: null>
child 0, item: null
reward_scores: list<item: null>
child 0, item: null
task_type: string
metadata: struct<page: int64, generation_source: string, source_sample_id: string, difficulty: string, parent_ (... 79 chars omitted)
child 0, page: int64
child 1, generation_source: string
child 2, source_sample_id: string
child 3, difficulty: string
child 4, parent_heading: string
child 5, chunk_index: int64
child 6, content_type: string
child 7, source_file: string
provenance_chain: list<item: struct<step_name: string, step_version: string, timestamp: string, config_hash: string, n (... 974 chars omitted)
child 0, item: struct<step_name: string, step_version: string, timestamp: string, config_hash: string, notes: struc (... 962 chars omitted)
child 0, step_name: string
child 1, step_version: string
child 2, timestamp: string
child 3, config_hash: string
child 4, notes: struct<table_extraction: string, reason: string, page: int64, source_file: string, chunk_index: int6 (... 866 chars omitted)
child 0, table_extraction: string
child 1, reason: string
child 2, page: int64
child 3, source_file: string
child 4, chunk_index: int64
child 5, parent_heading: string
child 6, chunk_strategy: string
child 7, overlap_
...
, rejected_count: int64
child 8, MaxSamplesTruncator: struct<input_count: int64, output_count: int64>
child 0, input_count: int64
child 1, output_count: int64
child 9, AlpacaExporter: struct<exported_count: int64>
child 0, exported_count: int64
child 10, ShareGPTExporter: struct<exported_count: int64>
child 0, exported_count: int64
tool_versions: struct<curatorkit: string, python: string>
child 0, curatorkit: string
child 1, python: string
token_stats: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_tokens: int64>
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_tokens: int64
pipeline_config_hash: string
source_files: list<item: struct<path: string>>
child 0, item: struct<path: string>
child 0, path: string
diversity_stats: null
run_timestamp: string
diagnostic_files: list<item: null>
child 0, item: null
dedup_stats: struct<>
minhash_threshold: null
sampler_stats: null
rejected_breakdown: struct<table_stubbed:extract_tables_disabled: int64, hallucination_contract_failed:0.50: int64, hall (... 153 chars omitted)
child 0, table_stubbed:extract_tables_disabled: int64
child 1, hallucination_contract_failed:0.50: int64
child 2, hallucination_contract_failed:0.00: int64
child 3, hallucination_contract_failed:0.60: int64
child 4, below_reward_threshold:0.00: int64
child 5, below_reward_threshold:0.50: int64
wall_clock_seconds: double
diagnostic_stats: null
to
{'pipeline_config_hash': Value('string'), 'run_timestamp': Value('string'), 'source_files': List({'path': Value('string')}), 'stage_counts': {'PDFReader': {'output_count': Value('int64'), 'rejected_count': Value('int64')}, 'SchemaGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'ExactDeduplicator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'MinHashDeduplicator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'TextCleaner': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'QAGenerationTask': {'input_count': Value('int64'), 'output_count': Value('int64'), 'rejected_count': Value('int64')}, 'HallucinationGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'RewardGate': {'input_count': Value('int64'), 'output_count': Value('int64'), 'probe_recovered': Value('int64'), 'rejected_count': Value('int64')}, 'MaxSamplesTruncator': {'input_count': Value('int64'), 'output_count': Value('int64')}, 'AlpacaExporter': {'exported_count': Value('int64')}, 'ShareGPTExporter': {'exported_count': Value('int64')}}, 'rejected_breakdown': {'table_stubbed:extract_tables_disabled': Value('int64'), 'hallucination_contract_failed:0.50': Value('int64'), 'hallucination_contract_failed:0.00': Value('int64'), 'hallucination_contract_failed:0.60': Value('int64'), 'below_reward_threshold:0.00': Value('int64'), 'below_reward_threshold:0.50': Value('int64')}, 'dedup_stats': {}, 'minhash_threshold': Value('null'), 'sampler_stats': Value('null'), 'token_stats': {'total_prompt_tokens': Value('int64'), 'total_completion_tokens': Value('int64'), 'total_tokens': Value('int64')}, 'wall_clock_seconds': Value('float64'), 'tool_versions': {'curatorkit': Value('string'), 'python': Value('string')}, 'diversity_stats': Value('null'), 'diagnostic_stats': Value('null'), 'diagnostic_files': List(Value('null'))}
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.
|
curatorkit-testrun-OCR
Built using CuratorKIT — provenance-grounded curation and synthesis for LLM post-training.
| Method | qa |
| Backend | litellm |
| Model | openai/Qwen/Qwen2.5-0.5B-Instruct |
| Formats | — |
| Artifact | dataset |
| Published | 2026-09-01 06:26 UTC |
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/curatorkit-testrun-OCR")
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