Datasets:

sxiong
/

DHSA_RULER

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
index: int64
input: string
outputs: list<item: string>
  child 0, item: string
length: int64
length_w_model_temp: int64
answer_prefix: string
to
{'index': Value('int64'), 'input': Value('string'), 'outputs': List(Value('string')), 'length': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              index: int64
              input: string
              outputs: list<item: string>
                child 0, item: string
              length: int64
              length_w_model_temp: int64
              answer_prefix: string
              to
              {'index': Value('int64'), 'input': Value('string'), 'outputs': List(Value('string')), 'length': Value('int64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, 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 948, 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 1683, 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 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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index int64	input string	outputs list	length int64
0	Below is a numbered list of words. In these words, some appear more often than others. Memorize the ones that appear most often. 1. mineshaft 2. fee 3. mineshaft 4. internet 5. abuse 6. go 7. backdrop 8. utensil 9. subsidence 10. subsidence 11. alteration 12. sidecar 13. economic 14. subsidence 15. gaiters 16. fee 17. ...	[ "whiskey", "growth", "jewelry", "position", "gong", "fur", "consciousness", "altered", "circle", "attempt" ]	16,331
1	Below is a numbered list of words. In these words, some appear more often than others. Memorize the ones that appear most often. 1. hunt 2. cathedral 3. hunt 4. roundabout 5. hardhat 6. siege 7. ossified 8. cap 9. brassiere 10. brassiere 11. attendance 12. picture 13. emergent 14. brassiere 15. ejector 16. cathedral 17...	[ "retrieve", "artificer", "chalice", "wraparound", "valuable", "trainer", "overload", "aquatic", "being", "dollar" ]	16,295
2	Below is a numbered list of words. In these words, some appear more often than others. Memorize the ones that appear most often. 1. footrest 2. sloppy 3. footrest 4. waiver 5. fortune 6. hood 7. ruckus 8. abortion 9. slip 10. slip 11. agonizing 12. deployment 13. sculptural 14. slip 15. eggnog 16. sloppy 17. bolt 18. f...	[ "willing", "honor", "verify", "atom", "buggy", "tangible", "recognize", "diving", "tickle", "research" ]	16,268
3	Below is a numbered list of words. In these words, some appear more often than others. Memorize the ones that appear most often. 1. accordance 2. derivation 3. accordance 4. puggle 5. ambulance 6. mantua 7. notepad 8. cutting 9. variant 10. variant 11. macadamia 12. cloud 13. body 14. variant 15. patient 16. derivation...	[ "systemize", "worried", "prophet", "historian", "rough", "snobbish", "mute", "psychology", "ruddy", "experiment" ]	16,262
4	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	["affidavit","step-grandmother","tender","chafe","piety","severity","split","gopher","fantastic","ti(...TRUNCATED)	16,272
5	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	["relish","official","representative","platform","model","derivation","emerald","washer","insect","w(...TRUNCATED)	16,187
6	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	[ "welfare", "squeeze", "crab", "treatment", "cloudy", "racist", "grass", "hardhat", "affair", "fairy" ]	16,326
7	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	["needle","cloakroom","coinsurance","octopus","position","determined","cloth","fine","psychology","y(...TRUNCATED)	16,307
8	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	["leap","sound","pupa","disengagement","undress","spade","everyone","confirmation","prestige","taste(...TRUNCATED)	16,286
9	"Below is a numbered list of words. In these words, some appear more often than others. Memorize the(...TRUNCATED)	["achiever","craftsman","search","floor","quartz","rinse","refuse","pocketbook","apricot","external"(...TRUNCATED)	16,369

End of preview.

RULER Evaluation Data

This dataset contains pre-generated JSONL files for the RULER long-context evaluation benchmark. RULER is designed to evaluate effective context length and long-context behavior beyond simple retrieval, covering retrieval, multi-hop tracing, aggregation, and question answering style tasks.

The files are organized by target context length:

.
├── 4096/
├── 8192/
├── 16384/
├── 32768/
└── 49152/

Each directory contains one JSONL file per RULER task. The 4096, 8192, 16384, and 32768 directories contain 16 task files each. The 49152 directory contains 13 task files and does not include scat_arith_1.jsonl, scat_arith_2.jsonl, or scat_arith_3.jsonl.

Dataset Details

Total examples: 34,900
Context lengths: 4,096, 8,192, 16,384, 32,768, and 49,152 tokens

Available task files:

cwe.jsonl
fwe.jsonl
niah_multikey_1.jsonl
niah_multikey_2.jsonl
niah_multikey_3.jsonl
niah_multiquery.jsonl
niah_multivalue.jsonl
niah_single_1.jsonl
niah_single_2.jsonl
niah_single_3.jsonl
qa_1.jsonl
qa_2.jsonl
scat_arith_1.jsonl
scat_arith_2.jsonl
scat_arith_3.jsonl
vt.jsonl

Schema

Each row is a JSON object. Common fields are:

index: integer example index within the file
input: prompt text to pass to the model
outputs: list of accepted answer strings
length: measured sequence length

Some files also include:

length_w_model_temp: measured sequence length including model-specific template text
answer_prefix: prefix used for constrained or formatted answer generation

Example:

{
  "index": 0,
  "input": "...",
  "outputs": ["answer"],
  "length": 32760,
  "length_w_model_temp": 32760,
  "answer_prefix": " The answer is"
}

Loading

Load one task file directly:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="32768/niah_single_1.jsonl",
    split="train",
)

Load multiple context lengths or tasks:

from datasets import load_dataset

data_files = {
    "4096_niah_single_1": "4096/niah_single_1.jsonl",
    "8192_niah_single_1": "8192/niah_single_1.jsonl",
    "16384_niah_single_1": "16384/niah_single_1.jsonl",
    "32768_niah_single_1": "32768/niah_single_1.jsonl",
    "49152_niah_single_1": "49152/niah_single_1.jsonl",
}

dataset = load_dataset("json", data_files=data_files)

Intended Use

This dataset is intended for evaluating long-context language models with RULER-compatible evaluation scripts. It is not intended for model training, user profiling, or decisions affecting people.

RULER benchmark:

Repository: https://github.com/NVIDIA/RULER
Paper: https://arxiv.org/abs/2404.06654

Citation

@article{xiong2025long,
  title={Long-Context Modeling with Dynamic Hierarchical Sparse Attention for On-Device LLMs},
  author={Xiong, Siheng and Zou, Joe and Fekri, Faramarz and Cho, Yae Jee},
  journal={arXiv preprint arXiv:2510.24606},
  year={2025}
}

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Total file size:

3.04 GB

Papers for sxiong/DHSA_RULER

Long-Context Modeling with Dynamic Hierarchical Sparse Attention for On-Device LLMs

Paper • 2510.24606 • Published Oct 28, 2025

RULER: What's the Real Context Size of Your Long-Context Language Models?

Paper • 2404.06654 • Published Apr 9, 2024 • 42