Datasets:

Mo-ZheHan
/

VA-data

License:

Dataset Viewer

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Cannot get the split names for the config 'default' of the dataset.

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 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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 71, 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.

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VA-data — lingbot-va research corpus + measurement records

A machine-handover bundle: everything outside the git repo that the AgiBot-rehearsal line needs, tarred (no compression) so the Hub sees ~280 large files instead of millions of small ones.

Restore with RESTORE.sh (downloads, reassembles split units, untars to the ORIGINAL absolute paths, recreates the eval symlinks and the export→base-model symlinks):

export HF_ENDPOINT=https://hf-mirror.com
hf download Mo-ZheHan/VA-data --repo-type dataset --include "RESTORE.sh" --local-dir .
bash RESTORE.sh 01 02 03        # records + eval instrument + r7b continuity first
bash RESTORE.sh all             # everything

Groups, in restore-priority order

group	what	size	replaceable?
`01-records`	eval cell cache (1471 cells = every offline TF/vdrift number), SR closed-loop results, `setup_logs` diagnostic toolchain, the full git repo incl. history, other eval-cell groups	~14G	no — re-deriving needs 200G+ of checkpoints and GPU-days
`02-eval-instrument`	the two RoboTwin eval tasks WITH videos (eval decodes mp4; training never does)	3.5G	only by re-collecting
`03-r7b`	r7b resume checkpoint (step 3000) + its step2000 export	69G	yes, by retraining ~11h
`04-clean500`	clean base corpus, 50 task tars (latents + text_emb)	113G	slow re-extraction
`05-geniesim`	filtered GenieSim DR rehearsal, 18 task tars, videos included (the only re-extraction source)	129G	slow
`06-baseline-exports`	r2 + r7 exports at 2k–10k — the baselines any new eval must pair against	108G	re-exportable from full ckpts (not uploaded)
`07-agibot`	AgiBot rehearsal corpus, 188 task tars	242G	slow re-fetch
`08-base-model`	`lingbot-va-base`	23G	yes — `hf download robbyant/lingbot-va-base`

MANIFEST.tsv lists every uploaded path with its byte size; LFS sha256 is checked by hf download itself.

Deliberately NOT here

Mix trees (datasets/mix/*): build_mix_tree trims metadata only, so a tree still hardlinks every episode of every task it includes — copying one moves 220G+ for nothing. Rebuild them (commands in RESTORE.sh's closing notes).
geniesim/lerobot_v21: same inodes as the videos inside 05-geniesim.
Full training checkpoints (21G each): wandb holds the curves, the cells hold the measurements.
.venv: must be rebuilt on local disk, never on a network share.
Credentials (~/.netrc, HF token, ssh keys/config): this repo is public — carry those by hand, out of band.

Downloads last month: -

Total file size:

738 GB