lightonai/fiqa-decontaminated

fiqa (Decontaminated)

A decontaminated version of the fiqa dataset from the BEIR benchmark, with samples found in the mgte-en pre-training dataset removed.

Decontamination methodology

Contamination was detected using a two-pass approach against the full mgte-en dataset (484 GB, 1,235 parquet files):

Pass 1: Exact hash matching

All texts (queries and corpus documents) were normalized (lowercased, unicode NFKD, whitespace collapsed) and hashed with xxHash-64. The same normalization + hashing was applied to every query and document field in mgte-en. Any sample whose hash appeared in mgte-en was flagged as contaminated.

Pass 2: 13-gram containment (GPT-3 style)

Following the methodology introduced in the GPT-3 paper (Brown et al., 2020), word-level 13-grams were extracted from all remaining samples. For each sample, containment was computed as:

containment = |ngrams_in_sample ∩ ngrams_in_mgte| / |ngrams_in_sample|

Samples with containment >= 0.5 were flagged as near-duplicates.

Qrels filtering

Relevance judgments (qrels) referencing any removed query or corpus document were also removed.

Decontamination results

Qrels per split

Usage

from datasets import load_dataset

corpus = load_dataset("lightonai/fiqa-decontaminated", "corpus", split="corpus")
queries = load_dataset("lightonai/fiqa-decontaminated", "queries", split="queries")

Citation

Please cite the original BEIR benchmark:

@inproceedings{thakur2021beir,
  title={BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
  author={Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Irena},
  booktitle={NeurIPS Datasets and Benchmarks},
  year={2021}
}

License

MIT (same as original BEIR)