The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
mdlens-realdocs-v1
A held-out Markdown QA / retrieval eval built entirely from real open-source project documentation. It measures whether an agent can answer documentation questions from the right evidence with fewer irrelevant reads and fewer tokens.
Questions are deliberately low lexical overlap (paraphrased), so they stress retrieval rather than string matching. Every non-abstention question has its answer keywords verified to appear in the cited source file.
Question types
- needle: a single fact stated in one section.
- multi_hop: the answer requires combining facts from more than one place.
- abstention: the answer is not present in the corpus; the correct behavior is to say the documentation does not specify it (this catches models that fabricate when retrieval returns near-but-irrelevant context).
Contents
| File | What |
|---|---|
corpus/<project>/*.md |
the searchable documentation corpus (673 files, ~4.9 MiB) |
questions.jsonl |
full license-clean question set (144 questions) |
questions_eval_subset.jsonl |
the 28-question balanced subset used for the paid harness runs |
manifest.json |
per-source provenance, license, file/byte counts |
LICENSES/<project>/ |
the upstream license text for each source project |
Question schema: question_id, question, expected_answer, expected_answer_keywords, evidence_files, evidence_sections, type, difficulty, source_eval.
Sources, attribution, and licensing
This dataset redistributes documentation verbatim from the projects below,
each under its own permissive license. The upstream license text is included
under LICENSES/. All copyright remains with the respective projects.
| Project | Source | License |
|---|---|---|
| FastAPI | https://github.com/fastapi/fastapi | MIT |
| DuckDB (docs) | https://github.com/duckdb/duckdb-web | MIT |
| Polars | https://github.com/pola-rs/polars | MIT |
| Pydantic | https://github.com/pydantic/pydantic | MIT |
| uv | https://github.com/astral-sh/uv | Apache-2.0 OR MIT |
| TRL | https://github.com/huggingface/trl | Apache-2.0 |
Deliberately excluded to respect licensing: Redis (redis/docs is
non-permissive / NOASSERTION), Prometheus (corpus was empty), and the Polars
polars-cloud / polars-on-premises pages (commercial product docs).
If you are a maintainer of any source project and want a change in how your docs are represented here, please open an issue on the dataset.
Provenance of the questions
Questions were generated with an LLM over sections selected from the real corpus, then filtered: answer keywords must occur in the cited file, and phrasing is paraphrased away from the source wording. Abstention questions ask for facts confirmed absent from the corpus.
Intended use
Built to evaluate mdlens, a token-efficient
Markdown retrieval CLI for agents, against plain shell retrieval (rg/cat).
It is a Markdown QA/retrieval benchmark, not a general coding-agent benchmark.
Baseline results (v0.1.3, 28-question subset)
The runs in eval_results/ cover three harnesses and seven models: Claude Code
(Opus 4.8, Sonnet 4.6), Codex (GPT-5.4, GPT-5.4-mini), and three open-source
models via Pi on OpenRouter (Kimi K2.7, GLM 5.2, DeepSeek V4 Flash). Across all
seven, the mdlens scout workflow beats plain shell retrieval: needle 75% to
86%, multi-hop 51% to 66%, average tool calls 7.8 to 4.9 per question, and lower
cost on every harness that reports dollars (open-source models 33% to 41%
cheaper). See eval_results/report.md for the full cache-aware breakdown.
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