Datasets:

makneeee
/

bioasq_large_10m

Parquet error: Scan size limit exceeded: attempted to read 1928096480 bytes, limit is 300000000 bytes Make sure that 1. the Parquet files contain a page index to enable random access without loading entire row groups2. otherwise use smaller row-group sizes when serializing the Parquet files

Error code:   TooBigContentError

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BioASQ Large 10M - Sharded DiskANN Indices

Pre-built DiskANN indices for the BioASQ Large 10M dataset from VectorDBBench, sharded for distributed vector search.

Dataset Info

Source: VectorDBBench (BioASQ)
Vectors: 10,000,000
Dimensions: 1024
Data type: float32
Queries: 10,000
Distance: L2

DiskANN Parameters

R (graph degree): 16, 32, 64
L (build beam width): 100
PQ bytes: 256

Shard Configurations

shard_3: 3 shards x ~3,333,333 vectors
shard_5: 5 shards x ~2,000,000 vectors
shard_7: 7 shards x ~1,428,571 vectors
shard_10: 10 shards x ~1,000,000 vectors

Index Variants (per shard directory)

R=16: bioasq_large_10m_16_100_256.shard*_disk.index
R=32: bioasq_large_10m_32_100_256.shard*_disk.index
R=64: bioasq_large_10m_64_100_256.shard*_disk.index

File Structure

fbin/
  base.fbin                                    # Base vectors (float32)
  queries.fbin                                 # Query vectors (float32)
parquet/
  train_*.parquet                              # Original VectorDBBench parquet
  test.parquet                                 # Original queries parquet
diskann/
  gt_100.fbin                                  # Ground truth (100-NN)
  shard_N/                                     # N-shard configuration
    bioasq_large_10m_base.shardX.fbin            # Shard base data
    bioasq_large_10m_R_100_256.shardX_disk.index                    # DiskANN disk index
    bioasq_large_10m_R_100_256.shardX_disk.index_512_none.indices   # MinIO graph indices
    bioasq_large_10m_R_100_256.shardX_disk.index_base_none.vectors  # MinIO vector data
    bioasq_large_10m_R_100_256.shardX_pq_pivots.bin                 # PQ pivot data
    bioasq_large_10m_R_100_256.shardX_pq_compressed.bin             # PQ compressed data
    bioasq_large_10m_R_100_256.shardX_sample_data.bin               # Sample data
    bioasq_large_10m_R_100_256.shardX_sample_ids.bin                # Sample IDs

Where R is one of 16, 32, 64 and X is the shard index.

Chunked Files

Files larger than 5 GB are split into chunks for upload:

*.part0000, *.part0001, etc.

To reassemble: cat file.part0000 file.part0001 ... > file

Usage

Download with huggingface_hub

from huggingface_hub import hf_hub_download

# Download a specific shard file
index = hf_hub_download(
    repo_id="makneeee/bioasq_large_10m",
    filename="diskann/shard_10/bioasq_large_10m_64_100_256.shard0_disk.index",
    repo_type="dataset"
)

Download with git-lfs

git lfs install
git clone https://huggingface.co/datasets/makneeee/bioasq_large_10m

License

Same as source dataset (VectorDBBench).

Downloads last month: 42

Size of downloaded dataset files:

19.3 GB

Size of the auto-converted Parquet files:

19.3 GB

Number of rows:

10,003,106