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"
}
```