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msmarco

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metadata
license: apache-2.0
language:
  - en
pretty_name: HSEB MSMARCO benchmarking dataset
dataset_info:
  features:
    - name: text
      dtype: string
    - name: id
      dtype: int64
    - name: embedding
      sequence: float32
    - name: results_10_docs
      sequence: int64
    - name: results_10_scores
      sequence: float32
    - name: results_90_docs
      sequence: int64
    - name: results_90_scores
      sequence: float32
    - name: results_100_docs
      sequence: int64
    - name: results_100_scores
      sequence: float32
    - name: tag
      sequence: int64
configs:
  - config_name: query-all-MiniLM-L6-v2-1K
    data_files: data/all-MiniLM-L6-v2/1K/queries.jsonl.gz
    default: true
  - config_name: corpus-all-MiniLM-L6-v2-1K
    data_files: data/all-MiniLM-L6-v2/1K/corpus.jsonl.gz
  - config_name: query-all-MiniLM-L6-v2-100K
    data_files: data/all-MiniLM-L6-v2/100K/queries.jsonl.gz
  - config_name: corpus-all-MiniLM-L6-v2-100K
    data_files: data/all-MiniLM-L6-v2/100K/corpus.jsonl.gz
  - config_name: query-all-MiniLM-L6-v2-1M
    data_files: data/all-MiniLM-L6-v2/1M/queries.jsonl.gz
  - config_name: corpus-all-MiniLM-L6-v2-1M
    data_files: data/all-MiniLM-L6-v2/1M/corpus.jsonl.gz
  - config_name: query-e5-base-v2-1K
    data_files: data/e5-base-v2/1K/queries.jsonl.gz
  - config_name: corpus-e5-base-v2-1K
    data_files: data/e5-base-v2/1K/corpus.jsonl.gz
  - config_name: query-e5-base-v2-100K
    data_files: data/e5-base-v2/100K/queries.jsonl.gz
  - config_name: corpus-e5-base-v2-100K
    data_files: data/e5-base-v2/100K/corpus.jsonl.gz
  - config_name: query-e5-base-v2-1M
    data_files: data/e5-base-v2/1M/queries.jsonl.gz
  - config_name: corpus-e5-base-v2-1M
    data_files: data/e5-base-v2/1M/corpus.jsonl.gz
  - config_name: query-Qwen3-Embedding-4B-1K
    data_files: data/Qwen3-Embedding-4B/1K/queries.jsonl.gz
  - config_name: corpus-Qwen3-Embedding-4B-1K
    data_files: data/Qwen3-Embedding-4B/1K/corpus.jsonl.gz
  - config_name: query-Qwen3-Embedding-4B-100K
    data_files: data/Qwen3-Embedding-4B/100K/queries.jsonl.gz
  - config_name: corpus-Qwen3-Embedding-4B-100K
    data_files: data/Qwen3-Embedding-4B/100K/corpus.jsonl.gz
  - config_name: query-Qwen3-Embedding-4B-1M
    data_files: data/Qwen3-Embedding-4B/1M/queries.jsonl.gz
  - config_name: corpus-Qwen3-Embedding-4B-1M
    data_files: data/Qwen3-Embedding-4B/1M/corpus.jsonl.gz

HSEB MSMARCO benchmarking dataset

This collection is based on MSMARCO dataset:

  • Embedding models:
  • Splits:
    • 1K: 1K documents, 10K queries
    • 100K: 100K documents, 10K queries
    • 1M: 1M documents, 10K queries
  • Filter selectivity:
    • 10% for high selectivity, 90% for low selectivity, 100% for no filters at all
    • each document has a tag based on sampled selectivity, so 10% of docs have a tag 10, and 50% of docs have tag 50
  • Exact match results:
    • for each selectivity level for each query there are precomputed exact k-NN search results for top-100 documents.

Dataset structure

The dataset can be loaded with the Huggingface datasets library:

from datasets import load_dataset

query = load_dataset("hseb-benchmark/msmarco", "query-all-MiniLM-L6-v2-1M")
corpus = load_dataset("hseb-benchmark/msmarco", "corpus-all-MiniLM-L6-v2-1M")

where 2nd argument to load_dataset is a config name, consisting of the following parts:

<query|corpus>-<model>-<size>
  1. query | corpus - which side of the dataset you're loading
  2. model: embedding model, all-MiniLM-L6-v2 (384 dims), e5-base-v2 (768 dims) or Qwen3-Embedding-4B (2560 dims)
  3. size: sample size, 1K, 100K, 1M

License

Apache 2.0