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: TypeError
Message: Couldn't cast array of type struct<flat: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>, ivf1024_flat: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>, ivf1024_pq: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>> to null
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 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type struct<flat: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>, ivf1024_flat: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>, ivf1024_pq: struct<MAP@10: double, MRR: double, NDCG@10: double, R@1: double, R@10: double, R@100: double, R@50: double, avg_rank: double, index_size_bytes: int64, mMG: double, median_rank: double, search_latency_ms_per_query: double>> to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NL-Lean CSN-v2 demo: data bundle
The data needed to run the interactive demo of
padieul/nl-lean (DEMO_README.md) outside the
machine it was built on. Do not download files by hand; the fetch script picks the files,
places them and checks every sha256:
python scripts/demo/fetch_data.py snapshot # recorded answers only, no GPU
python scripts/demo/fetch_data.py full # everything for the live demo
| tier | files | size |
|---|---|---|
| snapshot | 843 | 24.0 MB |
| data | 123 | 1.4 GB |
| index | 33 | 20.6 GB |
Paths are relative to the demo data root; bundle-manifest.json lists every file with its
tier, size and sha256. The base models are not in this repository; fetch_data.py full
downloads them from their own repositories at these revisions:
Qwen/Qwen3-Embedding-0.6B@97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3(for A-42, B-42, C-42, D-42, zs-0.6b)Qwen/Qwen3-Embedding-8B@1d8ad4ca9b3dd8059ad90a75d4983776a23d44af(for zs-8b)Qwen/Qwen3-Reranker-0.6B@e61197ed45024b0ed8a2d74b80b4d909f1255473(for two-stage)
Provenance. Staged 2026-09-27T00:53:21+00:00 from nl-lean commit
33c9a0586c1af635f0c563bc5b5c152cc7d3eedd-dirty.
Held-out data. This bundle contains the sealed held-out CSN-v2 panel and its certified near misses and views.
Licences. The bundle redistributes data derived from Herald (FrenzyMath) and adapters
trained on Qwen3 models. The upstream licences apply; set license above accordingly.
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