File size: 1,237 Bytes
a6e05bc cc0cf09 a6e05bc cc0cf09 a6e05bc cc0cf09 a6e05bc cc0cf09 a6e05bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
# 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
---
# hotpotqa.pisa
## Description
A PISA index for the Hotpot QA dataset
## Usage
```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/hotpotqa.pisa')
index.bm25() # returns a BM25 retriever
```
## Benchmarks
`hotpotqa/dev`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.6525 | 0.8909 |
| dph | 0.6445 | 0.8888 |
`hotpotqa/test`
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.6318 | 0.8851 |
| dph | 0.6246 | 0.8837 |
## Reproduction
```python
import pyterrier as pt
from tqdm import tqdm
import ir_datasets
from pyterrier_pisa import PisaIndex
index = PisaIndex("hotpotqa.pisa", threads=16)
dataset = ir_datasets.load('beir/hotpotqa')
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"
}
```
|