The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: Column 2: In chunk 0: Invalid: Struct child array #2 invalid: Invalid: Struct child array #1 invalid: Invalid: Length spanned by list offsets (5) larger than values array (length 1)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 322, in _generate_tables
examples = [ujson_loads(line) for line in 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
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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/json/json.py", line 329, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 294, in _generate_tables
pa_table.validate()
~~~~~~~~~~~~~~~~~^^
File "pyarrow/table.pxi", line 4237, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 2: In chunk 0: Invalid: Struct child array #2 invalid: Invalid: Struct child array #1 invalid: Invalid: Length spanned by list offsets (5) larger than values array (length 1)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.
E5+C3 original training code and evaluation documentation
Public release, 2026-10-25 (Asia/Singapore). This Hugging Face dataset hosts a Git source
repository, cloneable with git clone https://huggingface.co/datasets/ar-mine/e5-c3-code.
It is not a training dataset or a generic AutoModel implementation.
Epoch2 weights · Epoch6 weights · Training method and reproduction · Evaluation method · Hardware · Full results.
| Model | Clean / seen | Randomized / unseen | Overall |
|---|---|---|---|
| ep2 | 381/500 (76.2%) | 204/500 (40.8%) | 585/1000 (58.5%) |
| ep6 | 416/500 (83.2%) | 178/500 (35.6%) | 594/1000 (59.4%) |
These are completed historical evaluations; the release task ran no new rollouts or training. Epoch2 and epoch6 differ in global batch (64/192), LR (1e-4/1.5e-4), GPU count (4/8) and epoch count. They are not a pure epoch ablation. Clean/seen versus randomized/unseen also changes the instruction pool, so it is not a pure visual randomization effect. All 50 tasks appear in training; seen/unseen describes instruction vocabulary/template pools, not unseen tasks. These are the original fixed-table runs, not later low-table adaptation or interpolation experiments.
The actual original training archives supply the shared runtime, training entry, E5 history
logic, C3 augmentation, normalization and configs. Selected shared files are byte-identical
between endpoints before the documented portable path changes. SOURCE_MANIFEST.json
records original/export SHA256 values, original archive SHA256 values and portable edits.
Original archive contents, machine-specific mappings and records remain local: 原始记录保存在本地。
Upstream licenses are preserved in third_party/lingbot-vla-v2/LICENSE and nested dependency
LICENSE files. Original upstream illustrations/loss plots are omitted; they are not these runs'
curves. Deployment allocation scripts and private machine layouts are not required by the
portable training entry. See the explicit remaining reproduction limits in the method documents.
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