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/folder_based_builder/folder_based_builder.py", line 246, in _split_generators
raise ValueError(
"`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
)
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
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 68, 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.
HFTrainer · crema_d_mini
A reproducible public dataset for HFTrainer training examples.
| Property | Value |
|---|---|
| Source | https://github.com/CheyneyComputerScience/CREMA-D |
| Revision | 1658cd342dff90010aa843eaeebd53610a08b1dc |
| Splits | {"train": 24, "validation": 8} |
| License | odbl-1.0 — retain the upstream notices |
Preparation
Actor-disjoint subset: actors 1001–1004 train; 1005–1006 validation. Candidates ordered by SHA256(path), retain first clips at least 2.5 seconds with audio. First 2.5 seconds transcoded to H.264/24fps and AAC/stereo/32kHz; source resolution preserved. Captions describe the annotated emotion, not a verbatim transcript. Database: ODbL 1.0; individual contents: Database Contents License 1.0 (see LICENSE.txt).
See provenance.json, SHA256SUMS.json and the retained upstream notices. This is a short training-validation dataset, not evidence of benchmark convergence. No private HFTrainer development data is included.
Download
from huggingface_hub import snapshot_download
snapshot_download("ZeyuLing/hftrainer_crema_d_mini", repo_type="dataset", local_dir="data/hftrainer_crema_d_mini")
Citation and attribution
Cao et al. CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset, IEEE Transactions on Affective Computing 5(4), 377–390, 2014. doi:10.1109/TAFFC.2014.2336244.
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