Datasets:

Factiverse
/

averitec-repaired-dev-splade

pretty_name: AVeriTeC repaired dev, SPLADE index
language:
  - en
license: other
license_name: averitec-derived
license_link: https://fever.ai/dataset/averitec.html
task_categories:
  - text-retrieval
tags:
  - fact-checking
  - evidence-retrieval
  - averitec
  - splade
size_categories:
  - 10M<n<100M
extra_gated_prompt: >-
  This index is derived from text extracted from third-party web pages,
  including news articles. It is released for research on evidence retrieval for
  fact-checking. By requesting access you agree to use it for research purposes,
  to respect the rights of the original publishers, and not to redistribute the
  extracted text.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text

AVeriTeC repaired dev, SPLADE index

A prebuilt SPLADE index over Factiverse/averitec-repaired-dev, so the corpus can be searched without a GPU.

Encoder: naver/splade-cocondenser-ensembledistil, the model the AVeriTeC shared task used, so scores are comparable with its baselines.

Two differences from the shipped store

It indexes the repaired text, in which 63.9% of the empty documents have been recovered (21.5% of documents empty, down to 7.8%).

It does not drop text. The shared task's builder flushes its buffer before appending the current sentence and never flushes after the loop, so every document loses its final sentence and a single-sentence document yields one empty chunk and no text. This index chunks at 600 words instead, losing nothing. Chunk size was chosen from a sweep over 59 claims where 600 and 300 words gave identical gold recall — paired difference 0.0000, 3 wins, 3 losses, 53 ties — at half the chunks.

Layout

dev/full/<claim_id>/documents.pkl     chunks, with url metadata
dev/full/<claim_id>/embeddings.npz    scipy CSR, (n_chunks, 30522)

Row i of the matrix is chunk i of the pickle.

import pickle, scipy.sparse as sp
docs = pickle.load(open("dev/full/2/documents.pkl", "rb"))
emb = sp.load_npz("dev/full/2/embeddings.npz")
scores = emb @ query_vector          # query_vector: SPLADE-encoded, (30522,)

Reproducing it

The index is derived, not primary. It is published to save ~3 GPU-hours and to pin the exact chunker, but everything needed to rebuild it is public: the repaired corpus above and scripts/build_repaired_splade_store.py from the BEACON repository.

Provenance

URLs, document lists and structure come from the AVeriTeC shared task, whose terms govern that material. The indexed text was fetched from publishers' own pages and ordinary copyright applies to it. Access is gated for that reason. Please cite AVeriTeC.