Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
dataset_id: string
format: string
metadata: string
records: int64
residues: int64
sequence_dir: string
shards: int64
source_file: string
source_slug: string
source_count: int64
sources: list<item: struct<dataset_id: string, format: string, metadata: string, records: int64, residues: in (... 84 chars omitted)
child 0, item: struct<dataset_id: string, format: string, metadata: string, records: int64, residues: int64, sequen (... 72 chars omitted)
child 0, dataset_id: string
child 1, format: string
child 2, metadata: string
child 3, records: int64
child 4, residues: int64
child 5, sequence_dir: string
child 6, shards: int64
child 7, source_file: string
child 8, source_slug: string
total_records: int64
total_shards: int64
total_residues: int64
to
{'dataset_id': Value('string'), 'format': Value('string'), 'source_count': Value('int64'), 'sources': List({'dataset_id': Value('string'), 'format': Value('string'), 'metadata': Value('string'), 'records': Value('int64'), 'residues': Value('int64'), 'sequence_dir': Value('string'), 'shards': Value('int64'), 'source_file': Value('string'), 'source_slug': Value('string')}), 'total_records': Value('int64'), 'total_residues': Value('int64'), 'total_shards': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
dataset_id: string
format: string
metadata: string
records: int64
residues: int64
sequence_dir: string
shards: int64
source_file: string
source_slug: string
source_count: int64
sources: list<item: struct<dataset_id: string, format: string, metadata: string, records: int64, residues: in (... 84 chars omitted)
child 0, item: struct<dataset_id: string, format: string, metadata: string, records: int64, residues: int64, sequen (... 72 chars omitted)
child 0, dataset_id: string
child 1, format: string
child 2, metadata: string
child 3, records: int64
child 4, residues: int64
child 5, sequence_dir: string
child 6, shards: int64
child 7, source_file: string
child 8, source_slug: string
total_records: int64
total_shards: int64
total_residues: int64
to
{'dataset_id': Value('string'), 'format': Value('string'), 'source_count': Value('int64'), 'sources': List({'dataset_id': Value('string'), 'format': Value('string'), 'metadata': Value('string'), 'records': Value('int64'), 'residues': Value('int64'), 'sequence_dir': Value('string'), 'shards': Value('int64'), 'source_file': Value('string'), 'source_slug': Value('string')}), 'total_records': Value('int64'), 'total_residues': Value('int64'), 'total_shards': Value('int64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
UniRef90
Normalized FASTA shards of UniRef90 cluster representative sequences (90% sequence identity threshold).
Processed and uploaded by the MegaData post-download pipeline (internal repo). Original source: https://www.uniprot.org/help/uniref.
Statistics
| Source files | 1 |
| Shards | 189 |
| Compressed shard bytes | 38.65 GiB (41,498,595,315) |
| Records (per-source manifest sum) | 188,848,220 |
| Residues (per-source manifest sum) | 66,359,825,357 |
Aggregate manifest total_records |
188848220 |
Layout
.
├── _MANIFEST.json # aggregate manifest written by the pipeline
├── manifests/<source_slug>.json # per-source manifest (records, residues, shards)
├── metadata/<source_slug>.records.jsonl # per-record provenance
└── sequences/<source_slug>/shard-NNNNNN.fasta.zst
<source_slug> corresponds 1:1 with an upstream source archive; e.g.
sequence_uniprotkb_uniprot_sprot.fasta.gz.
Loading
Stream every shard of one source (replace <source_slug> with the directory of
interest under sequences/):
hf download LiteFold/UniRef90 --repo-type dataset \
--include 'sequences/<source_slug>/shard-*.fasta.zst' \
--local-dir ./uniref90
zstd -dc ./uniref90/sequences/<source_slug>/shard-*.fasta.zst | head
Programmatic streaming with zstandard:
from huggingface_hub import snapshot_download
from pathlib import Path
import zstandard as zstd
local = snapshot_download(
repo_id="LiteFold/UniRef90",
repo_type="dataset",
allow_patterns=["sequences/*/shard-*.fasta.zst"],
)
dctx = zstd.ZstdDecompressor()
for shard in sorted(Path(local).rglob("shard-*.fasta.zst")):
with shard.open("rb") as f, dctx.stream_reader(f) as reader:
buf = b""
while chunk := reader.read(1 << 20):
buf += chunk
*lines, buf = buf.split(b"\n")
for line in lines:
... # naive splitter; swap in your FASTA parser
License
CC BY 4.0 (UniProt Consortium).
Citation
Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH; UniProt Consortium. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 31(6):926-32, 2015.
Provenance
Built from the local manifest entry uniref90 of manifests/atlas_download_plan.json.
Pipeline source: megadata-post normalize --dataset uniref90.
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