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
case_id: large_string
dataset_name: large_string
data_path: large_string
reference_config_path: large_string
expected_target: large_string
expected_task_type: large_string
expected_eval_metric: large_string
time_limit_seconds: large_string
sample_weight: large_string
forbidden_features: large_string
notes: large_string
-- schema metadata --
huggingface: '{"info": {"features": {"case_id": {"dtype": "large_string",' + 660
to
{'variant_id': Value('large_string'), 'label': Value('large_string'), 'description': Value('large_string'), 'nodes': Value('large_string'), 'requires_llm': Value('bool'), 'requires_tavily': Value('bool'), 'requires_cloud_submission': Value('large_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/parquet/parquet.py", line 220, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
case_id: large_string
dataset_name: large_string
data_path: large_string
reference_config_path: large_string
expected_target: large_string
expected_task_type: large_string
expected_eval_metric: large_string
time_limit_seconds: large_string
sample_weight: large_string
forbidden_features: large_string
notes: large_string
-- schema metadata --
huggingface: '{"info": {"features": {"case_id": {"dtype": "large_string",' + 660
to
{'variant_id': Value('large_string'), 'label': Value('large_string'), 'description': Value('large_string'), 'nodes': Value('large_string'), 'requires_llm': Value('bool'), 'requires_tavily': Value('bool'), 'requires_cloud_submission': Value('large_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.
AutoML-LLM Agent Module 2 Benchmark
This dataset contains the Module 2 benchmark for evaluating an assistant that converts a Module 1 recipe, a user request, and a processed tabular dataset into an auditable autogluon.cloud.TabularCloudPredictor configuration.
The repository is scoped to Module 2 only.
Tables
module2_cases: one row per Module 2 evaluation case.module2_queries: user requests for each case.module2_reference_configs: legacy reference JSON files used as expected AutoGluon Cloud intent.module2_reference_recipes: Module 1 recipe assets that Module 2 must preserve.module2_ablation_variants: LangGraph node-ablation variants used by Module 2 experiments.module2_invalid_seed_configs: intentionally invalid configs for repair and robustness evaluation.
File Assets
Data/raw/AutoML_LLM_agent/dataset/processed_files/*.csv: canonical processed tabular CSVs referenced bymodule2_cases.data_path.Data/raw/AutoML_LLM_agent/reference_json/json_*_reference.json: legacy reference configs referenced bymodule2_cases.reference_config_path.dataset/module2_reference_recipes/*.json: Module 1 recipe contracts referenced bymodule2_reference_recipes.recipe_path.
Current Coverage
The benchmark currently includes seven reference cases:
calls_for_service_cloudelectric_vehicle_cloudcholesterol_clouddiabetes_cloudproperties_cloudbanking_cloudavocado_cloud
Usage
from datasets import load_dataset
cases = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_cases", split="train")
queries = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_queries", split="train")
variants = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_ablation_variants", split="train")
CSV and JSON assets referenced by the table path columns can be downloaded from the same dataset repository.
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