task_categories:text-retrieval region:us pyterrier pyterrier-artifact pyterrier-artifact.sparse_index pyterrier-artifact.sparse_index.terrier
---
# 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
---

# quora.terrier

## Description

Terrier index for Quora

## Usage

```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/quora.terrier')
index.bm25()
```

## Benchmarks

`quora/dev`

| name   |   nDCG@10 |   R@1000 |
|:-------|----------:|---------:|
| bm25   |    0.7712 |   0.9908 |
| dph    |    0.4529 |   0.9005 |

`quora/test`

| name   |   nDCG@10 |   R@1000 |
|:-------|----------:|---------:|
| bm25   |    0.7676 |   0.9926 |
| dph    |    0.4429 |   0.9026 |

## Reproduction

```python
import pyterrier as pt
from tqdm import tqdm
import ir_datasets
dataset = ir_datasets.load('beir/quora')
meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len']
indexer = pt.IterDictIndexer("./quora.terrier", meta={'docno': meta_docno_len, 'text': 4096})
docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs))
indexer.index(docs)
```

## Metadata

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