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/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/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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.
TSRouter-VLDB Artifacts
Release: v1.0
This dataset contains the reproduction artifacts used with the TSRouter-VLDB code repository.
Figure 1: Why quality-efficiency routing is needed in a growing TSFM zoo. A multi-tenant forecasting service receives heterogeneous time-series requests while the candidate TSFM repository expands over time. Release-ordered winner shares show that request ownership is distributed across models and shifts after new arrivals. Full-zoo forwarding and selector retraining become increasingly costly as the zoo grows, motivating adaptive routing that balances forecasting accuracy, serving latency, and INSERT maintenance time.
TSRouter uses three operations to maintain a growing model zoo: PROFILE builds capability representations, ROUTE selects models for requests, and INSERT updates the index for a newly available model or result.
Contents
- Released TSFM, TSRouter, and baseline result records.
- Capability representations and request-sample caches for rerunning TSRouter routing.
- Profile inputs for rebuilding the TSRouter capability profile.
- Table inputs used by the reproduction commands.
Use
Install the dependencies from the GitHub repository, then select a reproduction level:
results: validate released results and preview tables.route: rerun main and fast routing from the released representations and request-sample caches.core: rebuild capability representations and rerun routing with GIFT-Eval and official model checkpoints.
--stage N selects a model-arrival stage for index construction, routing, and INSERT. PROFILE source preparation is shared across stages. INSERT also accepts --start-stage A --end-stage B for a stage range.
git clone https://github.com/fireball0213/TSRouter-VLDB.git
cd TSRouter-VLDB
export TSROUTER_VLDB_HF_REPO="LAMDA-shihn/tsrouter-v1-artifacts"
bash scripts/run_public_reproduction.sh --root "$PWD" --python-bin "$(which python)" --reuse results --pull --repo-id "$TSROUTER_VLDB_HF_REPO"
GIFT-Eval is available from its official Hugging Face repository. Model checkpoints are retrieved from their original public sources by the GitHub reproduction tools.
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