---
dataset_info:
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1745074
    num_examples: 5043
  download_size: 1110075
  dataset_size: 1745074
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 2328
    num_examples: 50
  download_size: 3032
  dataset_size: 2328
- config_name: relevance
  features:
  - name: query-id
    dtype: string
  - name: positive-corpus-ids
    sequence: string
  - name: bm25-ranked-ids
    sequence: string
  splits:
  - name: train
    num_bytes: 2745765
    num_examples: 50
  download_size: 445450
  dataset_size: 2745765
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: queries
  data_files:
  - split: train
    path: queries/train-*
- config_name: relevance
  data_files:
  - split: train
    path: relevance/train-*
language:
- en
tags:
- sentence-transformers
size_categories:
- 1K<n<10K
---

# NanoBEIR MS MARCO with BM25 rankings
This dataset is an updated variant of [NanoMSMARCO](https://huggingface.co/datasets/zeta-alpha-ai/NanoMSMARCO), which is a subset of the MS MARCO dataset from the Benchmark for Information Retrieval (BEIR).
MS MARCO was created as a subset of the rather large BEIR, designed to be more efficient to run. This dataset adds a `bm25-ranked-ids` column to the `relevance` subset, which contains a ranking of every single passage in the corpus to the query.

This dataset is used in Sentence Transformers for evaluating CrossEncoder (i.e. reranker) models on NanoBEIR by reranking the top *k* results from BM25.