| --- |
| |
| tags: |
| - pyterrier |
| - pyterrier-artifact |
| - pyterrier-artifact.sparse_index |
| - pyterrier-artifact.sparse_index.terrier |
| task_categories: |
| - text-retrieval |
| viewer: false |
| --- |
| |
| # scifact.terrier |
|
|
| ## Description |
|
|
| Terrier index for SciFact |
|
|
| ## Usage |
|
|
| ```python |
| # Load the artifact |
| import pyterrier as pt |
| index = pt.Artifact.from_hf('pyterrier/scifact.terrier') |
| index.bm25() |
| ``` |
|
|
| ## Benchmarks |
|
|
| | name | nDCG@10 | R@1000 | |
| |:-------|----------:|---------:| |
| | bm25 | 0.6839 | 0.9767 | |
| | dph | 0.6716 | 0.9733 | |
|
|
| ## Reproduction |
|
|
| ```python |
| import pyterrier as pt |
| from tqdm import tqdm |
| import ir_datasets |
| dataset = ir_datasets.load('beir/scifact') |
| meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len'] |
| indexer = pt.IterDictIndexer("./scifact.terrier", meta={'docno': meta_docno_len, 'text': 4096}) |
| docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs)) |
| indexer.index(docs) |
| ``` |
|
|
| ## Metadata |
|
|
| ``` |
| { |
| "type": "sparse_index", |
| "format": "terrier", |
| "package_hint": "python-terrier" |
| } |
| ``` |
|
|
