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README.md

@@ -3,6 +3,8 @@
 tags:
 - pyterrier
 - pyterrier-artifact
+- pyterrier-artifact.sparse_index
+- pyterrier-artifact.sparse_index.terrier
 task_categories:
 - text-retrieval
 viewer: false
@@ -12,43 +14,33 @@ viewer: false
 
 ## Description
 
-Terrier index for Arguana
+*TODO: What is the artifact?*
 
 ## Usage
 
 ```python
 # Load the artifact
 import pyterrier as pt
-index = pt.Artifact.from_hf('pyterrier/arguana.terrier')
-index.bm25()
+artifact = pt.Artifact.from_hf('pyterrier/arguana.terrier')
+# TODO: Show how you use the artifact
 ```
 
 ## Benchmarks
 
-| name   |   nDCG@10 |   R@1000 |
-|:-------|----------:|---------:|
-| bm25   |    0.4914 |   0.9922 |
-| dph    |    0.4319 |   0.99   |
+*TODO: Provide benchmarks for the artifact.*
 
 ## Reproduction
 
 ```python
-import pyterrier as pt
-from tqdm import tqdm
-import pandas as pd
-import ir_datasets
-from pyterrier_pisa import PisaIndex
-dataset = ir_datasets.load('beir/arguana')
-meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len']
-indexer = pt.IterDictIndexer("./arguana.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)
+# TODO: Show how you constructed the artifact.
 ```
 
 ## Metadata
 
 ```
 {
-  "package_hint": "pyterrier"
+  "type": "sparse_index",
+  "format": "terrier",
+  "package_hint": "python-terrier"
 }
 ```

artifact.tar.lz4

@@ -1,3 +1,3 @@
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-size 8014232
+oid sha256:95b4e93b28502f44293703192acd3d54b3057d89a3606af5174ef2f3f4c84ed8
+size 8014275

artifact.tar.lz4.json

@@ -1,3 +1,3 @@
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 size 853