Polish LongContext Benchmark
Collection
Polish long-context benchmark with five tasks covering legal, human-rights, and literary document understanding. • 5 items • Updated
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
text: string
art_2: int64
art_3: int64
art_4: int64
art_5: int64
art_6: int64
art_7: int64
art_8: int64
art_9: int64
art_10: int64
art_11: int64
art_12: int64
art_13: int64
art_14: int64
art_15: int64
art_16: int64
art_17: int64
art_18: int64
art_34: int64
art_38: int64
art_39: int64
art_46: int64
art_P1-1: int64
art_P1-2: int64
art_P1-3: int64
art_P3-1: int64
art_P4-1: int64
art_P4-2: int64
art_P4-3: int64
art_P4-4: int64
art_P6-1: int64
art_P6-2: int64
art_P6-3: int64
art_P7-1: int64
art_P7-2: int64
art_P7-3: int64
art_P7-4: int64
art_P7-5: int64
art_P12-1: int64
art_P13-1: int64
art_P13-2: int64
art_P13-3: int64
labels: list<item: int64>
child 0, item: int64
to
{'text': Value('string'), 'labels': List(Value('int64'))}
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
text: string
art_2: int64
art_3: int64
art_4: int64
art_5: int64
art_6: int64
art_7: int64
art_8: int64
art_9: int64
art_10: int64
art_11: int64
art_12: int64
art_13: int64
art_14: int64
art_15: int64
art_16: int64
art_17: int64
art_18: int64
art_34: int64
art_38: int64
art_39: int64
art_46: int64
art_P1-1: int64
art_P1-2: int64
art_P1-3: int64
art_P3-1: int64
art_P4-1: int64
art_P4-2: int64
art_P4-3: int64
art_P4-4: int64
art_P6-1: int64
art_P6-2: int64
art_P6-3: int64
art_P7-1: int64
art_P7-2: int64
art_P7-3: int64
art_P7-4: int64
art_P7-5: int64
art_P12-1: int64
art_P13-1: int64
art_P13-2: int64
art_P13-3: int64
labels: list<item: int64>
child 0, item: int64
to
{'text': Value('string'), 'labels': List(Value('int64'))}
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.
ECtHR-PL-AVA
ECtHR-PL-AVA is a Polish long-document multi-label classification dataset derived from the publicly available ECtHR Cases dataset.
The dataset contains Polish translations of factual descriptions from cases heard by the European Court of Human Rights (ECtHR). The task is to predict the articles of the European Convention on Human Rights alleged to have been violated in a given case.
ECtHR-PL-AVA is part of the LongContext benchmark introduced with Polish ModernBERT.
Dataset statistics
| Split | Examples |
|---|---|
| Train | 7,367 |
| Validation | 834 |
| Test | 819 |
| Total | 9,020 |
The dataset contains predominantly long documents: 83.4% of test examples exceed 512 tokens, 58.6% exceed 1,024 tokens, and 23.3% exceed 2,048 tokens when tokenized with the Polish ModernBERT Base tokenizer.
Dataset construction
For each case, sentences and paragraphs describing the case facts were concatenated into a single document. Cases with factual descriptions longer than 32,000 characters were removed to exclude extreme-length outliers.
The remaining documents were translated from English into Polish using GLM-4.6, while the original dataset splits and labels were retained.
The task is evaluated using weighted F1.
Citation
If you use this dataset, please cite:
[CITATION]
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