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--- |
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tags: |
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- pyterrier |
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- pyterrier-artifact |
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- pyterrier-artifact.sparse_index |
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- pyterrier-artifact.sparse_index.terrier |
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task_categories: |
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- text-retrieval |
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viewer: false |
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--- |
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# dbpedia-entity.terrier |
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## Description |
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Terrier index for DBPedia-Entity |
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## Usage |
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```python |
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# Load the artifact |
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import pyterrier as pt |
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index = pt.Artifact.from_hf('pyterrier/dbpedia-entity.terrier') |
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index.bm25() |
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``` |
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## Benchmarks |
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`dbpedia-entity/dev` |
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| name | nDCG@10 | R@1000 | |
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|:-------|----------:|---------:| |
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| bm25 | 0.3627 | 0.7487 | |
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| dph | 0.3617 | 0.7461 | |
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`dbpedia-entity/test` |
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| name | nDCG@10 | R@1000 | |
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|:-------|----------:|---------:| |
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| bm25 | 0.3039 | 0.6555 | |
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| dph | 0.305 | 0.6518 | |
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## Reproduction |
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```python |
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import pyterrier as pt |
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from tqdm import tqdm |
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import ir_datasets |
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dataset = ir_datasets.load('beir/dbpedia-entity') |
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meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len'] |
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indexer = pt.IterDictIndexer("./dbpedia-entity.terrier", meta={'docno': meta_docno_len, 'text': 4096}) |
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docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs)) |
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indexer.index(docs) |
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``` |
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## Metadata |
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``` |
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{ |
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"type": "sparse_index", |
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"format": "terrier", |
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"package_hint": "python-terrier" |
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} |
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``` |
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