---
dataset_info:
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
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  download_size: 1266719
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- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 6041
    num_examples: 50
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  dataset_size: 6041
- config_name: relevance
  features:
  - name: query-id
    dtype: string
  - name: positive-corpus-ids
    sequence: string
  - name: bm25-ranked-ids
    sequence: string
  splits:
  - name: train
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    num_examples: 50
  download_size: 262930
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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-*
---

# NanoBEIR SCIDOCS with BM25 rankings
This dataset is an updated variant of [NanoSCIDOCS](https://huggingface.co/datasets/zeta-alpha-ai/NanoSCIDOCS), which is a subset of the SCIDOCS dataset from the Benchmark for Information Retrieval (BEIR).
SCIDOCS 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.