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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
completed: list<item: struct<m: int64, mode: string>>
  child 0, item: struct<m: int64, mode: string>
      child 0, m: int64
      child 1, mode: string
dataset: string
elapsed_seconds: double
end_time: string
faiss_fallback_used: bool
m_values: list<item: int64>
  child 0, item: int64
mode: string
start_time: string
threads: int64
train_fraction: null
M: int64
ef_construction: int64
index_bytes: int64
sidecar: null
index_path: string
dtype: string
space: string
command: list<item: string>
  child 0, item: string
to
{'M': Value('int64'), 'command': List(Value('string')), 'dataset': Value('string'), 'dtype': Value('string'), 'ef_construction': Value('int64'), 'elapsed_seconds': Value('float64'), 'end_time': Value('string'), 'index_bytes': Value('int64'), 'index_path': Value('string'), 'sidecar': Value('null'), 'space': Value('string'), 'start_time': Value('string'), 'threads': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              completed: list<item: struct<m: int64, mode: string>>
                child 0, item: struct<m: int64, mode: string>
                    child 0, m: int64
                    child 1, mode: string
              dataset: string
              elapsed_seconds: double
              end_time: string
              faiss_fallback_used: bool
              m_values: list<item: int64>
                child 0, item: int64
              mode: string
              start_time: string
              threads: int64
              train_fraction: null
              M: int64
              ef_construction: int64
              index_bytes: int64
              sidecar: null
              index_path: string
              dtype: string
              space: string
              command: list<item: string>
                child 0, item: string
              to
              {'M': Value('int64'), 'command': List(Value('string')), 'dataset': Value('string'), 'dtype': Value('string'), 'ef_construction': Value('int64'), 'elapsed_seconds': Value('float64'), 'end_time': Value('string'), 'index_bytes': Value('int64'), 'index_path': Value('string'), 'sidecar': Value('null'), 'space': Value('string'), 'start_time': Value('string'), 'threads': Value('int64')}
              because column names don't match

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tti-100m-static-search-eval

Static 100M search-evaluation dataset with original vectors, original query/ground-truth files, a rebuilt hnswlib HNSW index, and generated PQ artifacts.

This Hugging Face copy provides the static-search-eval package for tti, including static artifacts and PQ artifacts.

Files

  • Base: base.fbin
  • HNSW index: index_m_32_ef_500
  • Query: orig_query_100k.fbin
  • Ground truth: groundtruth.bin
  • PQ artifacts: pq_m50.pqcodes/.pqmeta/.pqcodebook, pq_m100.pqcodes/.pqmeta/.pqcodebook, pq_m200.pqcodes/.pqmeta/.pqcodebook
  • Checksums: checksums.sha256

Dataset Properties

  • Vector count: 100000000
  • Dimension: 200
  • Dtype: float32
  • Metric: ip
  • Query count label: 100k
  • Query count: 100000

HNSW Index

  • Parameters: M=32, ef_construction=500
  • Primary search parameters: ef_search=150, k=10

Source

Original source terms apply. This copy is uploaded as a public Hugging Face dataset from the public Kaggle datasets listed above.

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