Dataset Viewer
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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
source_dataset: string
revision: string
model_name: string
languages: list<item: string>
child 0, item: string
skip_tokens_per_language: int64
kept_tokens_per_language: int64
context_size: int64
total_tokens: int64
language_manifests: list<item: struct<source_dataset: string, revision: string, model_name: string, language: string, sk (... 177 chars omitted)
child 0, item: struct<source_dataset: string, revision: string, model_name: string, language: string, skip_tokens: (... 165 chars omitted)
child 0, source_dataset: string
child 1, revision: string
child 2, model_name: string
child 3, language: string
child 4, skip_tokens: int64
child 5, kept_tokens: int64
child 6, context_size: int64
child 7, sequences: int64
child 8, raw_tokens_seen: int64
child 9, documents_seen: int64
child 10, documents_contributing: int64
child 11, parquet_file: string
documents_seen: int64
kept_tokens: int64
raw_tokens_seen: int64
documents_contributing: int64
language: string
parquet_file: string
sequences: int64
skip_tokens: int64
to
{'source_dataset': Value('string'), 'revision': Value('string'), 'model_name': Value('string'), 'language': Value('string'), 'skip_tokens': Value('int64'), 'kept_tokens': Value('int64'), 'context_size': Value('int64'), 'sequences': Value('int64'), 'raw_tokens_seen': Value('int64'), 'documents_seen': Value('int64'), 'documents_contributing': Value('int64'), 'parquet_file': Value('string')}
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 478, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
source_dataset: string
revision: string
model_name: string
languages: list<item: string>
child 0, item: string
skip_tokens_per_language: int64
kept_tokens_per_language: int64
context_size: int64
total_tokens: int64
language_manifests: list<item: struct<source_dataset: string, revision: string, model_name: string, language: string, sk (... 177 chars omitted)
child 0, item: struct<source_dataset: string, revision: string, model_name: string, language: string, skip_tokens: (... 165 chars omitted)
child 0, source_dataset: string
child 1, revision: string
child 2, model_name: string
child 3, language: string
child 4, skip_tokens: int64
child 5, kept_tokens: int64
child 6, context_size: int64
child 7, sequences: int64
child 8, raw_tokens_seen: int64
child 9, documents_seen: int64
child 10, documents_contributing: int64
child 11, parquet_file: string
documents_seen: int64
kept_tokens: int64
raw_tokens_seen: int64
documents_contributing: int64
language: string
parquet_file: string
sequences: int64
skip_tokens: int64
to
{'source_dataset': Value('string'), 'revision': Value('string'), 'model_name': Value('string'), 'language': Value('string'), 'skip_tokens': Value('int64'), 'kept_tokens': Value('int64'), 'context_size': Value('int64'), 'sequences': Value('int64'), 'raw_tokens_seen': Value('int64'), 'documents_seen': Value('int64'), 'documents_contributing': Value('int64'), 'parquet_file': Value('string')}
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.
FineWeb2 multilingual CLT entropy evaluation data
This dataset contains held-out token sequences used to measure multilingual
feature entropy for CausalNLP/multilingual_gpt2-clt-ar-zh-ko-ja.
Construction
- Source:
HuggingFaceFW/fineweb-2 - Source revision:
af9c13333eb981300149d5ca60a8e9d659b276b9 - Tokenizer:
CausalNLP/gpt2-ar-zh-ko-ja-120k - Languages/configurations:
arb_Arab,cmn_Hani,jpn_Jpan,kor_Hang - The first 200,000,000 tokenizer tokens of each language were skipped.
- The following 1,000,000 tokens per language were retained.
- Each row contains one complete 16-token sequence.
- There are 62,500 rows per language and 4,000,000 retained tokens in total.
The Parquet files contain input_ids, language, source_id, and
document_token_start. See manifest.json and the per-language manifests for
the exact construction parameters and source-stream counts.
This dataset preserves token IDs rather than decoded text so that the entropy evaluation is reproducible with the exact tokenizer used for the CLT.
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