| | --- |
| | |
| | tags: |
| | - pyterrier |
| | - pyterrier-artifact |
| | - pyterrier-artifact.sparse_index |
| | - pyterrier-artifact.sparse_index.pisa |
| | task_categories: |
| | - text-retrieval |
| | viewer: false |
| | --- |
| | |
| | # arguana.pisa |
| |
|
| | ## Description |
| |
|
| | A PISA index for the Arguana dataset |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | # Load the artifact |
| | import pyterrier as pt |
| | index = pt.Artifact.from_hf('pyterrier/arguana.pisa') |
| | index.bm25() # returns a BM25 retriever |
| | ``` |
| |
|
| | ## Benchmarks |
| |
|
| | | name | nDCG@10 | R@1000 | |
| | |:-------|----------:|---------:| |
| | | bm25 | 0.3436 | 0.9808 | |
| | | dph | 0.3502 | 0.9815 | |
| |
|
| | ## Reproduction |
| |
|
| | ```python |
| | import pyterrier as pt |
| | from tqdm import tqdm |
| | import ir_datasets |
| | from pyterrier_pisa import PisaIndex |
| | index = PisaIndex("arguana.pisa", threads=16) |
| | dataset = ir_datasets.load('beir/arguana') |
| | 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" |
| | } |
| | ``` |
| |
|