Update README.md

README.md

@@ -3,36 +3,90 @@
 tags:
 - pyterrier
 - pyterrier-artifact
-- pyterrier-artifact.dense_index
-- pyterrier-artifact.dense_index.flex
+- pyterrier-dr
+- pyterrier-flex
 task_categories:
 - text-retrieval
 viewer: false
 ---
 
-# msmarco-passage.tasb.flex
+# Flex Index of MSMarco Passage using TASB
 
 ## Description
 
-*TODO: What is the artifact?*
+This artifact represents the retrieval index of the MSMarco Passage collection produced by the TaASB model (more info [here](https://pyterrier.readthedocs.io/en/latest/_modules/pyterrier_dr/hgf_models.html#TasB)).
 
 ## Usage
 
+The following code snippete shows how to use this artifact to perform a dense retrieval experiment:
 ```python
-# Load the artifact
+
 import pyterrier as pt
-artifact = pt.Artifact.from_hf('ntonellotto/msmarco-passage.tasb.flex')
-# TODO: Show how you use the artifact
+import pyterrier_dr
+from pyterrier.measures import *
+
+# Load the artifact
+index = pt.Artifact.from_hf('ntonellotto/msmarco-passage.tasb.flex')
+# Load the query encoder (must be the same used to create the artifact)
+model = pyterrier_dr.TasB()
+# Load the dataset
+dataset = pt.get_dataset('msmarco_passage')
+# Run the PyTerrier experiment
+pt.Experiment(
+    [model >> index],
+    dataset.get_topics('test-2019'),
+    dataset.get_qrels('test-2019'),
+    eval_metrics=[RR@10, Recall(rel=2)@100, Recall@100, nDCG@10, "mrt"],
+    names=["Dense Retrieval"]
+)
 ```
 
-## Benchmarks
+You should get something like:
+| name             | RR@10    | R(rel=2)@100 | R@100    | nDCG@10  | mrt        |
+|------------------|----------|---------------|----------|----------|------------|
+| Dense Retrieval  | 0.976744 | 0.607269      | 0.514744 | 0.718238 | 280.014161 |
 
-*TODO: Provide benchmarks for the artifact.*
 
 ## Reproduction
 
+### Generating the data
+
+The data has been generated using the following code snippet:
+
+```python
+import torch
+import pyterrier as pt
+import pyterrier_dr
+
+# Select the available Torch's backend
+if torch.backends.mps.is_available():
+    device = torch.device("mps")
+elif torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+print(f"Using device: {device}")
+# Create the encoder model
+tct = pyterrier_dr.TasB(device=device, batch_size=256)
+# Create the destination index folder
+index = pyterrier_dr.FlexIndex("./msmarco-passage.tasb.flex")
+# Indexing
+(tct >> index).index(pt.get_dataset("msmarco_passage").get_corpus_iter())
+
+print(f"Indexed {len(index)} documents")
+```
+
+### Uploading the data
+
+To upload the index to Hugging Face, use the following code snippet (the dataset sheet will be created automatically):
+
 ```python
-# TODO: Show how you constructed the artifact.
+import pyterrier as pt
+
+# Load the artifact from disk
+artifact = pt.Artifact.load("./msmarco-passage.tasb.flex")
+# Upload the artifact to HF
+artifact.to_hf('ntonellotto/msmarco-passage.tasb.flex')
 ```
 
 ## Metadata