jxliu2/idiom
Updated
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/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
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.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.
idr_datasets/training_sequences: idr_datasets/generated_sequences: hf download jxliu2/idiom-datasets \
--repo-type=dataset --local-dir ./datasets
hf download jxliu2/idiom-datasets \
idr_datasets/training_sequences/AFDB_IDR_90_FIM_512_full.fasta \
--repo-type=dataset \
--local-dir ./datasets
hf download jxliu2/idiom-datasets \
idr_datasets/training_sequences/AFDB_IDR_90_FIM_512_idrs.fasta \
--repo-type=dataset \
--local-dir ./datasets
idr_datasets/training_sequences:
AFDB_IDR_90_reps.fasta: contains the 53M cluster representatives after the initial 214M full
length AFDB protein sequences are clustered at 90% identity, 80% coverage.AFDB_IDR_90_alldata.h5: contains 73M IDRs as extracted from the AlphaFold Database according to
the Tesei logic (see paper Methods), and after filtering for IDRs belonging to the 53M cluster
representatives identified in AFDB_IDR_90_reps.fasta. This HDF5 file contains the following
keys: <KeysViewHDF5 ['accession_ids', 'full_avg_plddt', 'full_length', 'full_-
seq', 'idr_end', 'idr_length', 'idr_plddt', 'idr_start', 'idrs']>.AFDB_IDR_90_FIM_512.h5: is created from AFDB_IDR_90_alldata.h5 by filtering out IDRs
whose full length sequences are longer than 512 residues. We also find that ∼ 1/3 of records in
AFDB_IDR_90_alldata.h5 are fully low-pLDDT sequences, and we filter out those sequences
because we find that they are not representative of intrinsically disordered proteins.
For the remaining 37M IDRs, we apply the fill-in-the-middle (FIM)
transformation as well as IDP data augmentation as mentioned in the Methods, and place
those records into AFDB_IDR_90_FIM_512.h5. We note that we represent the <N>, <C>, and
<I> tokens with 1, 2, and 3, respectively, in this HDF5 file as well as in the codebase. This
is the final file used for the precompute and pre-training steps.AFDB_IDR_90_FIM_512_full.fasta: contains the 37M full length sequences (in correct order,
not FIM-transformed) contained in AFDB_IDR_90_FIM_512.h5. The fasta header contains
_IDR_X-Y where X and Y are the 1-indexed indices of the start and end (inclusive) of the
intrinsically disordered region.AFDB_IDR_90_FIM_512_idrs.fasta: contains only the sequences of the 37M intrinsically dis-
ordered regions in AFDB_IDR_90_FIM_512_full.fasta, without their surrounding context.idr_datasets/generated_sequences:
Generated IDPs: 100,000 unprompted intrinsically disordered proteins.Generated IDRs: 101,700 intrinsically disordered regions generated using 1,017 DisProt flank-
ing contexts prompts (100 generated IDRs per prompt).Generated NPM1 IDRs: 100,000 sequences generated using the NPM1 flanking context as
the prompt (UniProt: P06748).Generated ProtGPS Sequences: 10,000 IDPs generated from post-trained checkpoints. Post-
training was done to optimize ProtGPS localization scores for the four target compartments:
chromosome, nucleolus, P-body, and stress granule.