README.md

---
# 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
---

# MS MARCO PISA Index

## Description

This is an index of the MS MARCO passage (v1) dataset with PISA. It can be used for passage retrieval using lexical methods.

## Usage

```python
>>> from pyterrier_pisa import PisaIndex
>>> index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
>>> bm25 = index.bm25()
>>> bm25.search('terrier breeds')
 qid query docno score rank
0 1 terrier breeds 1406578 22.686367 0
1 1 terrier breeds 5785957 22.611134 1
2 1 terrier breeds 7455374 22.592781 2
3 1 terrier breeds 3984886 22.242958 3
4 1 terrier breeds 3984893 22.009525 4
...
```

## Benchmarks

**TREC DL 2019**

<details>
 <summary>Code</summary>

```python
from ir_measures import nDCG, RR, MAP, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019/judged')
pt.Experiment(
 [index.bm25(), index.qld(), index.dph(), index.pl2()],
 dataset.get_topics(),
 dataset.get_qrels(),
 [nDCG@10, nDCG, RR(rel=2), MAP(rel=2), R(rel=2)@1000],
 ['BM25', 'QLD', 'DPH', 'PL2'],
 round=4,
)
```
</details>

| | name | nDCG@10 | nDCG | RR(rel=2) | AP(rel=2) | R(rel=2)@1000 |
|---:|:-------|----------:|-------:|------------:|------------:|----------------:|
| 0 | BM25 | 0.4989 | 0.6023 | 0.6804 | 0.3031 | 0.7555 |
| 1 | QLD | 0.468 | 0.5984 | 0.6047 | 0.3037 | 0.7601 |
| 2 | DPH | 0.4975 | 0.5907 | 0.6674 | 0.3009 | 0.7436 |
| 3 | PL2 | 0.4503 | 0.5681 | 0.6495 | 0.2679 | 0.7304 |

**TREC DL 2020**

<details>
 <summary>Code</summary>

```python
from ir_measures import nDCG, RR, MAP, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020/judged')
pt.Experiment(
 [index.bm25(), index.qld(), index.dph(), index.pl2()],
 dataset.get_topics(),
 dataset.get_qrels(),
 [nDCG@10, nDCG, RR(rel=2), MAP(rel=2), R(rel=2)@1000],
 ['BM25', 'QLD', 'DPH', 'PL2'],
 round=4,
)
```
</details>

| | name | nDCG@10 | nDCG | RR(rel=2) | AP(rel=2) | R(rel=2)@1000 |
|---:|:-------|----------:|-------:|------------:|------------:|----------------:|
| 0 | BM25 | 0.4793 | 0.5963 | 0.6529 | 0.2974 | 0.8048 |
| 1 | QLD | 0.4511 | 0.587 | 0.5812 | 0.2879 | 0.8125 |
| 2 | DPH | 0.4586 | 0.5704 | 0.6123 | 0.2779 | 0.798 |
| 3 | PL2 | 0.4552 | 0.5609 | 0.5788 | 0.2666 | 0.7772 |

**MS MARCO Dev (small)**

<details>
 <summary>Code</summary>

```python
from ir_measures import RR, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/dev/small')
pt.Experiment(
 [index.bm25(), index.qld(), index.dph(), index.pl2()],
 dataset.get_topics(),
 dataset.get_qrels(),
 [RR@10, R@1000],
 ['BM25', 'QLD', 'DPH', 'PL2'],
 round=4,
)
```
</details>

| | name | RR@10 | R@1000 |
|---:|:-------|--------:|---------:|
| 0 | BM25 | 0.185 | 0.8677 |
| 1 | QLD | 0.1683 | 0.8542 |
| 2 | DPH | 0.1782 | 0.8605 |
| 3 | PL2 | 0.1741 | 0.8607 |

## Reproduction

```python
>>> import pyterrier_pisa
>>> import pyterrier as pt
>>> idx = pyterrier_pisa.PisaIndex('msmarco-passage.pisa')
>>> idx.indexer().index(pt.get_dataset('irds:msmarco-passage').get_corpus_iter())
```

## Metadata

```
{
 "type": "sparse_index",
 "format": "pisa",
 "package_hint": "pyterrier-pisa",
 "stemmer": "porter2"
}
```