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---
# 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
---
# dbpedia-entity.terrier
## Description
Terrier index for DBPedia-Entity
## Usage
```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/dbpedia-entity.terrier')
index.bm25()
```
## Benchmarks
`dbpedia-entity/dev`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.3627 | 0.7487 |
| dph | 0.3617 | 0.7461 |
`dbpedia-entity/test`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.3039 | 0.6555 |
| dph | 0.305 | 0.6518 |
## Reproduction
```python
import pyterrier as pt
from tqdm import tqdm
import ir_datasets
dataset = ir_datasets.load('beir/dbpedia-entity')
meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len']
indexer = pt.IterDictIndexer("./dbpedia-entity.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"
}
```
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