Error code:   ConfigNamesError
Exception:    TypeError
Message:      list_() takes at least 1 positional argument (0 given)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 612, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 396, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2138, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2134, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                                ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2123, in from_yaml_inner
                  Value(obj["dtype"])
                File "<string>", line 5, in __init__
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 552, in __post_init__
                  self.pa_type = string_to_arrow(self.dtype)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 156, in string_to_arrow
                  return pa.__dict__[datasets_dtype + "_"]()
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/types.pxi", line 4942, in pyarrow.lib.list_
              TypeError: list_() takes at least 1 positional argument (0 given)

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AI Text Outline Benchmark

Benchmark dataset for evaluating ai-text-outline on Tibetan Buddhist texts.

Documents: 124
Source: BDRC/OpenPecha catalog transcriptions with human-confirmed section breakpoints
Evaluation repo: OpenPecha/ai-text-outline-benchmark
Package: ai-text-outline on PyPI

What this dataset is for

Each row is one complete text with:

breakpoints — character indices where a new section begins in content
titles — the section title at each breakpoint (from catalog annotation)

Researchers and engineers use this set to measure how well an automatic ToC/outline pipeline recovers the same structure (breakpoint F1, segment count, title match, Pk, WindowDiff). See the GitHub repo for metric definitions and the full evaluation pipeline.

Baseline results (ai-text-outline v0.8.0)

Evaluated on all 124 documents (106 succeeded; 18 failed with Gemini image errors):

Metric	Mean
Breakpoint F1@100	0.43
Segment count MAE	17.7
Title F1	0.54
Pk	0.21
WindowDiff	0.26
Success rate (≤5% segment-count error)	85.5%

Full per-document reports: data/results/ in the GitHub repository.

Usage

from datasets import load_dataset

ds = load_dataset("openpecha/ai-text-outline-benchmark", split="train")
print(ds)
# Dataset with 124 rows; columns id, filename, content, breakpoints, titles, ...

row = ds[0]
text = row["content"]
starts = row["breakpoints"]
titles = row["titles"]

Install the evaluator:

git clone https://github.com/OpenPecha/ai-text-outline-benchmark.git
cd ai-text-outline-benchmark
pip install -e .
pip install ai-text-outline
export GEMINI_API_KEY=...  # required to run predictions
python -m benchmark.run_pipeline

Schema

Field	Type	Description
`id`	string	Document UUID
`filename`	string	BDRC filename
`content`	string	Full Tibetan Unicode text
`content_length`	int64	Length of `content` in characters
`num_segments`	int32	Number of sections
`breakpoints`	list[int]	Section start indices in `content`
`titles`	list[string]	Section titles

Data collection

Samples were extracted from the OpenPecha catalog database: documents with at least one confirmed breakpoint annotation. Texts are stored as .txt files in the GitHub repo (Git LFS); this Hub dataset mirrors data/samples/ + data/ground_truth.json.

Citation

If you use this benchmark, please cite the OpenPecha project and link to this dataset:

@misc{{openpecha_ai_text_outline_benchmark,
  title = {{AI Text Outline Benchmark}},
  author = {{OpenPecha}},
  year = {{2026}},
  howpublished = {{\url{{https://huggingface.co/datasets/openpecha/ai-text-outline-benchmark}}}}
}}

License

Dataset annotations and text transcriptions are released under CC BY 4.0. Check individual BDRC source records for underlying scan rights.

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