Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE
Paper • 2607.07740 • Published • 22
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
tokenizer: string
min_tokens: int64
target_total: int64
actual_total: int64
n_from_test: int64
n_from_train: int64
seed: int64
books: list<item: struct<idx: int64, source_split: string, source_index: int64, n_tokens: int64>>
child 0, item: struct<idx: int64, source_split: string, source_index: int64, n_tokens: int64>
child 0, idx: int64
child 1, source_split: string
child 2, source_index: int64
child 3, n_tokens: int64
source_split: string
source_index: int64
n_tokens: int64
idx: int64
text: string
to
{'idx': Value('int64'), 'source_split': Value('string'), 'source_index': Value('int64'), 'n_tokens': Value('int64'), 'text': 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
tokenizer: string
min_tokens: int64
target_total: int64
actual_total: int64
n_from_test: int64
n_from_train: int64
seed: int64
books: list<item: struct<idx: int64, source_split: string, source_index: int64, n_tokens: int64>>
child 0, item: struct<idx: int64, source_split: string, source_index: int64, n_tokens: int64>
child 0, idx: int64
child 1, source_split: string
child 2, source_index: int64
child 3, n_tokens: int64
source_split: string
source_index: int64
n_tokens: int64
idx: int64
text: string
to
{'idx': Value('int64'), 'source_split': Value('string'), 'source_index': Value('int64'), 'n_tokens': Value('int64'), 'text': 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.
Jet-Long Evaluation Datasets
This repository contains the evaluation data used in the paper Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE.
- Code: GitHub Repository
Dataset Description
These datasets are processed versions of standard benchmarks used to evaluate the long-context capabilities of Jet-Long:
- RULER-500: A dataset for evaluating long-context understanding and retrieval up to 128K context.
- PG-19-subsample: A subsampled version of the PG-19 dataset used for evaluating long-context language modeling and perplexity.
Citation
If you find Jet-Long or these datasets useful, please cite:
@misc{jetlong2026,
title={Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE},
author={Haozhan Tang and Zerui Wang and Yuxian Gu and Song Han and Han Cai},
year={2026},
eprint={2607.07740},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2607.07740},
}
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