pyterrier/dbpedia-entity.pisa
收藏Hugging Face2024-10-08 更新2025-04-26 收录
下载链接:
https://hf-mirror.com/datasets/pyterrier/dbpedia-entity.pisa
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资源简介:
---
# pretty_name: "" # Example: "MS MARCO Terrier Index"
tags:
- pyterrier
- pyterrier-artifact
- pyterrier-artifact.sparse_index
- pyterrier-artifact.sparse_index.pisa
task_categories:
- text-retrieval
viewer: false
---
# dbpedia-entity.pisa
## Description
A PISA index for the DBPedia-Entity Dataset
## Usage
```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/dbpedia-entity.pisa')
index.bm25() # returns a BM25 retriever
```
## Benchmarks
`dbpedia-entity/dev`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.3916 | 0.7824 |
| dph | 0.3961 | 0.7735 |
`dbpedia-entity/test`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.3271 | 0.6861 |
| dph | 0.3237 | 0.6794 |
## Reproduction
```python
import pyterrier as pt
from tqdm import tqdm
import ir_datasets
from pyterrier_pisa import PisaIndex
index = PisaIndex("dbpedia-entity.pisa", threads=16)
dataset = ir_datasets.load('beir/dbpedia-entity')
docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs))
index.index(docs)
```
## Metadata
```
{
"type": "sparse_index",
"format": "pisa",
"package_hint": "pyterrier-pisa",
"stemmer": "porter2"
}
```
提供机构:
pyterrier



