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
list<item: struct<model: string, provider: string, tier: string, date: timestamp[s], methodology_version: string, git_commit: string, benchmarks: struct<creative_technical: struct<score: double, status: string, error: string, details: struct<budget-haiku: double, recursive-story: double, regex-poetry: double>>, practical_knowledge: struct<score: double, details: struct<taxes: double, regulations: double, practical_finance: double, consumer_rights: double>>, gsm8k: struct<score: double, accuracy: double>, ifeval: struct<score: double, strict: double, loose: int64>>>>
to
{'together/mistral-7b-instruct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'groq/llama-3.1-8b-instant': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('float64'), 'creative_technical': Value('float64'), 'gsm8k': Value('float64'), 'ifeval': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('float64'), 'creative_technical': Value('float64'), 'gsm8k': Value('float64'), 'ifeval': Value('float64')})}, 'cohere/command-light': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'huggingface/mistral-7b-instruct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'google/gemini-1.5-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('null'), 'creative_technical': Value('null')}), 'rolling': List({'date': Valu
...
truct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64')})}, 'cohere/command-r-08-2024': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'ifeval': Value('float64'), 'gsm8k': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'ifeval': Value('float64'), 'gsm8k': Value('float64')})}, 'google/gemini-2.0-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('null'), 'practical_knowledge': Value('null')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'google/gemini-2.5-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64')})}}
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 2303, 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 1852, 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 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
list<item: struct<model: string, provider: string, tier: string, date: timestamp[s], methodology_version: string, git_commit: string, benchmarks: struct<creative_technical: struct<score: double, status: string, error: string, details: struct<budget-haiku: double, recursive-story: double, regex-poetry: double>>, practical_knowledge: struct<score: double, details: struct<taxes: double, regulations: double, practical_finance: double, consumer_rights: double>>, gsm8k: struct<score: double, accuracy: double>, ifeval: struct<score: double, strict: double, loose: int64>>>>
to
{'together/mistral-7b-instruct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'groq/llama-3.1-8b-instant': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('float64'), 'creative_technical': Value('float64'), 'gsm8k': Value('float64'), 'ifeval': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('float64'), 'creative_technical': Value('float64'), 'gsm8k': Value('float64'), 'ifeval': Value('float64')})}, 'cohere/command-light': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'huggingface/mistral-7b-instruct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'google/gemini-1.5-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'practical_knowledge': Value('null'), 'creative_technical': Value('null')}), 'rolling': List({'date': Valu
...
truct': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64')})}, 'cohere/command-r-08-2024': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'ifeval': Value('float64'), 'gsm8k': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'ifeval': Value('float64'), 'gsm8k': Value('float64')})}, 'google/gemini-2.0-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('null'), 'practical_knowledge': Value('null')}), 'rolling': List({'date': Value('timestamp[s]')})}, 'google/gemini-2.5-flash': {'provider': Value('string'), 'tier': Value('string'), 'scores': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64'), 'methodology_version': Value('string')}), 'rolling': List({'date': Value('timestamp[s]'), 'creative_technical': Value('float64'), 'practical_knowledge': Value('float64')})}}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AI Egg Index — results
Weekly benchmark of free-tier LLMs on everyday tasks (practical knowledge, instruction following, math, coding).
- Live leaderboard: https://ceaustin117.github.io/ai-egg-index/
- Code: https://github.com/Ceaustin117/ai-egg-index
Files
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Scores are small-sample / directional — see the repo's LIMITATIONS.md. Data is CC BY 4.0; some result text is model-generated and belongs to the respective providers.
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