File size: 1,168 Bytes
10104df 84c276e 10104df 84c276e 10104df 84c276e 10104df 84c276e 10104df |
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 |
---
# 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
---
# webis-touche2020.terrier
## Description
Terrier index for Touche2020
## Usage
```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/webis-touche2020.terrier')
index.bm25()
```
## Benchmarks
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.594 | 0.7307 |
| dph | 0.6756 | 0.7244 |
## Reproduction
```python
import pyterrier as pt
from tqdm import tqdm
import ir_datasets
dataset = ir_datasets.load('beir/webis-touche2020/v2')
meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len']
indexer = pt.IterDictIndexer("./webis-touche2020/v2.terrier", meta={'docno': meta_docno_len, 'text': 4096})
docs = ({'docno': d.doc_id, 'text': '{title}\n{text}'.format(**d._asdict())} for d in tqdm(dataset.docs))
indexer.index(docs)
```
## Metadata
```
{
"type": "sparse_index",
"format": "terrier",
"package_hint": "python-terrier"
}
```
|