Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
shape: list<item: int64>
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string, clevel: int64, shuffle: string, blocksize: int64>>>
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
vs
attributes: struct<metadata: string, description: string, info: string>
zarr_format: int64
consolidated_metadata: null
node_type: string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
shape: list<item: int64>
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string, clevel: int64, shuffle: string, blocksize: int64>>>
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
vs
attributes: struct<metadata: string, description: string, info: string>
zarr_format: int64
consolidated_metadata: null
node_type: stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
EEG Dataset
This dataset was created using braindecode, a library for deep learning with EEG/MEG/ECoG signals.
Dataset Information
| Property | Value |
|---|---|
| Number of recordings | 1 |
| Dataset type | Windowed (from Epochs object) |
| Number of channels | 26 |
| Sampling frequency | 250 Hz |
| Number of windows / samples | 48 |
| Total size | 0.03 MB |
| Storage format | zarr |
Usage
To load this dataset::
.. code-block:: python
from braindecode.datasets import BaseConcatDataset
# Load dataset from Hugging Face Hub
dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")
# Access data
X, y, metainfo = dataset[0]
# X: EEG data (n_channels, n_times)
# y: label/target
# metainfo: window indices
Using with PyTorch DataLoader
::
from torch.utils.data import DataLoader
# Create DataLoader for training
train_loader = DataLoader(
dataset,
batch_size=32,
shuffle=True,
num_workers=4
)
# Training loop
for X, y, metainfo in train_loader:
# X shape: [batch_size, n_channels, n_times]
# y shape: [batch_size]
# metainfo shape: [batch_size, 2] (start and end indices)
# Process your batch...
Dataset Format
This dataset is stored in Zarr format, optimized for:
- Fast random access during training (critical for PyTorch DataLoader)
- Efficient compression with blosc
- Cloud-native storage compatibility
For more information about braindecode, visit: https://braindecode.org
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