---
# pretty_name: "" # Example: "MS MARCO Terrier Index"
tags:
- pyterrier
- pyterrier-artifact
- pyterrier-artifact.sparse_index
- pyterrier-artifact.sparse_index.terrier
task_categories:
- text-retrieval
viewer: false
---

# ragwiki-terrier

## Description

This is the sparse (Terrier) index for the Wikipedia 2018 corpus used by the Natural Questions and Textbook Question Answering datasets. By making this available as a Huggingface dataset, experiments can be performed using the existing index.

## Usage

```python
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('pyterrier/ragwiki-terrier')
bm25 = artifact.bm25()
bm25.search("what are chemical reactions")
```

## Benchmarks

*TODO: Provide benchmarks for the artifact.*

## Reproduction

This index was created uing PyTerrier's sparse (Terrier) indexer.

```python
import pyterrier as pt, pyterrier_rag
ref = pt.IterDictIndexer(
        index_dir, 
        text_attrs=['title', 'text'], 
        meta={'docno' : 20, 'text' : 1750, 'title' : 125}
    ).index(pt.get_dataset('rag:nq_wiki').get_corpus_iter())
index = pt.terrier.TerrierIndex(ref)
index.to_hf('pyterrier/ragwiki-terrier')
```

Full code to reproduce this index is included in [ragwiki_indexing-terrier.ipynb](https://huggingface.co/datasets/pyterrier/ragwiki-terrier/blob/main/ragwiki_indexing-terrier.ipynb)

## Metadata

```
{
  "type": "sparse_index",
  "format": "terrier",
  "package_hint": "python-terrier"
}
```