Update README.md

README.md

@@ -1,23 +1,31 @@
 ---
-# pretty_name: "" # Example: "MS MARCO Terrier Index"
 tags:
 - pyterrier
 - pyterrier-artifact
-- pyterrier-artifact.dense_index
-- pyterrier-artifact.dense_index.flex
+- pyterrier-dr
 task_categories:
 - text-retrieval
 viewer: false
+language:
+- en
+pretty_name: Dense index of the BRIGHT Sustainable Living collection for PyTerrier
 ---
 
-# bright.sustainable.mini.dense
+# Dense index of the BRIGHT Sustainable Living collection for PyTerrier
 
 ## Description
 
-*TODO: What is the artifact?*
+A dense index of the sustainable-living subset of the BRIGHT dataset build with PyTerrier's DR plugin.
 
 ## Usage
 
+You will need to install PyTerrier, and its DR engine:
+
+```python
+%pip install pyterrier
+%pip install pyterrier-dr
+```
+
 ```python
 # Load the artifact
 import pyterrier as pt
@@ -25,14 +33,28 @@ artifact = pt.Artifact.from_hf('pyterrier-tutorial/bright.sustainable.mini.dense
 # TODO: Show how you use the artifact
 ```
 
-## Benchmarks
-
-*TODO: Provide benchmarks for the artifact.*
-
 ## Reproduction
 
 ```python
-# TODO: Show how you constructed the artifact.
+import pyterrier as pt
+from pyterrier_dr import SBertBiEncoder, FlexIndex
+
+dataset = pt.get_dataset('irds:bright/sustainable-living')
+
+model_mini = SBertBiEncoder(
+    "sentence-transformers/all-MiniLM-L6-v2",
+    batch_size=32,
+    text_field="text"
+)
+
+dense_path_mini = "./bright-mini.flex"
+
+if os.path.exists(dense_path_mini):
+    shutil.rmtree(dense_path_mini)
+
+dense_index_mini = FlexIndex(dense_path_mini)
+dense_indexer_mini = model_mini.doc_encoder() >> dense_index_mini.indexer(mode="overwrite")
+dense_indexer_mini.index(dataset.get_corpus_iter())
 ```
 
 ## Metadata
@@ -44,4 +66,4 @@ artifact = pt.Artifact.from_hf('pyterrier-tutorial/bright.sustainable.mini.dense
   "vec_size": 384,
   "doc_count": 60792
 }
-```
+```