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/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/checkpoints/[]/checkpoint) changed from string to number in row 0
During handling of the above exception, another exception occurred:
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/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_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
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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
bigann-100m-batch-update-eval
Batch-update evaluation workload package generated from bigann-100m-static-search-eval.
Dataset
- Source static dataset:
bigann-100m-static-search-eval - Vector count:
100,000,000 - Dimension:
128 - Dtype:
uint8 - Metric:
l2 - Initial update index:
80,000,000vectors with external labels equal toA = P[0:80M] - Update order:
update_order.u32, a seed-42permutation of source IDs[0, 100M) - Insert vector source:
base_permuted.u8bin, where rowjequalsbase.u8bin[P[j]]
Traces
insert-20: starts fromA, then insertsP[80M:100M]in twenty1,000,000-vector batches.delete-20: starts from the static100Mstate, then deletesP[80M:100M]in twenty1,000,000-vector batches.mixed-replace-100: keeps80Mlive vectors for 100 rounds; each round deletes a cyclic1,000,000source-ID slice and insertsbase_permuted.u8binrow ranges: batches 1-20 use rows80M:100M, then batches 21-100 use rows0:80M. Insert external IDs use existingP[80M:100M]labels for the first20rounds, then new labels in[100,000,000, 180,000,000).
Batch JSON files use compact descriptors (range, u32_slice, and u32_cyclic_slice) instead of inline million-ID arrays. Insert external_ids continue to express user-visible source IDs. Insert vector_refs are row ranges in the reordered insert source and must be read from base_permuted.u8bin.
Ground Truth
Checkpoint ground truth is produced by filtering the static source ground truth in source-distance order through the checkpoint owner map. Files are exact top-10 only when every query retains at least 10 active candidates from the static source GT depth. If a checkpoint cannot provide top-10 for every query, the package writes a matching .invalid.json marker instead of padding.
Files
workload.json: workload contract.static-workload-reference.json: immutable static-search-eval references.source_manifest.json: generation manifest.update_order.u32: seed-42 source-ID permutation.initial/index_80m_m32_efc500: HNSW index built frombase[A]with labelsA.groundtruth/active_80m.bin: initial80Mcheckpoint GT, oractive_80m.invalid.json.initial/layout-sidecar/index_80m_m32_efc500.*: optional runtime layout sidecar for the initial HNSW index.initial/pq/pq_m<M>.*: initial80MPQ artifacts reordered forinitial/index_80m_m32_efc500internal IDs.initial_pq_manifest.json: source static PQ files and validation samples for the reordered initial PQ artifacts.base_permuted.u8bin: reordered insert vector source, present when insertvector_refsare row ranges.reordered_insert_manifest.json: source, formula, size, and sample-check manifest forbase_permuted.u8bin.traces/*/trace.json: trace metadata.traces/*/batches/*.json: compact batch descriptors.traces/*/groundtruth/*: checkpoint GT or invalid markers.checksums.sha256: checksums for generated package files.
Static PQ codebooks and metadata are reused. initial/pq/pq_m<M>.pqcodes contains only 80,000,000 rows and is ordered by the initial HNSW internal ID, so it can be used directly with initial/index_80m_m32_efc500.
PQ row-order compatibility
The initial 80M PQ payloads were already generated in HNSW internal-ID order;
they do not inherit the former base-row ordering of the static dataset and were
not rewritten during the static PQ correction. For internal row i,
initial/pq/pq_m<M>.pqcodes[i] encodes the external label stored in
initial/index_80m_m32_efc500 row i.
initial_pq_manifest.json records code_order: hnsw_internal_id and validation
samples. tools/reorder_pq_codes_for_index.py is included to reproduce or
validate the same transformation when a new initial HNSW index is built.
Attribution
The original BIGANN (SIFT) vectors are credited to the original BIGANN dataset contributors and were obtained from the Big ANN 2021 benchmark collection.
The original source data is subject to CC0 1.0 Universal; the original terms are reproduced in LICENSE.
We generated the indexes ourselves.
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