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---
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](https://huggingface.co/datasets/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.
```python
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](https://fever.ai/dataset/averitec.html) 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.