Add task categories and paper references to dataset card

README.md

@@ -1,48 +1,49 @@
 ---
-pretty_name: "Galaxy10 AION-1 Benchmark"
 license: mit
-
-tags:
-  - astronomy
-  - galaxy-morphology
-  - aion
-  - galaxy-zoo
-
+task_categories:
+- feature-extraction
 size_categories:
-  - 1K<n<10K
-
+- 1K<n<10K
+pretty_name: Galaxy10 AION-1 Benchmark
+tags:
+- astronomy
+- galaxy-morphology
+- aion
+- galaxy-zoo
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: data/train-*.parquet
-      - split: test
-        path: data/test-*.parquet
-
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+  - split: test
+    path: data/test-*.parquet
 dataset_info:
   features:
-    - name: image_rgb
-      dtype: image
-    - name: ra
-      dtype: float64
-    - name: dec
-      dtype: float64
-    - name: Galaxy10_DECals_index
-      dtype: int64
-    - name: label
-      dtype: int64
-    - name: label_name
-      dtype: string
-    - name: image_bands
+  - name: image_rgb
+    dtype: image
+  - name: ra
+    dtype: float64
+  - name: dec  
+    dtype: float64
+  - name: Galaxy10_DECals_index
+    dtype: int64
+  - name: label
+    dtype: int64
+  - name: label_name
+    dtype: string
+  - name: image_bands
+    list:
       list:
         list:
-          list:
-            dtype: float32
+          dtype: float32
 ---
 
 # Galaxy10 AION-1 Benchmark
 
-This dataset provides the exact train/test split used to produce the Galaxy Morphology Classification results in the [AION-1 paper](https://arxiv.org/abs/2510.17960) (Table 2, Section 7.2.2) and used in the [AION-Search paper](https://arxiv.org/abs/2512.11982).
+This dataset provides the exact train/test split used to produce the Galaxy Morphology Classification results in the [AION-1 paper](https://arxiv.org/abs/2510.17960) (Table 2, Section 7.2.2) and used in the paper [Semantic search for 100M+ galaxy images using AI-generated captions](https://huggingface.co/papers/2512.11982).
+
+- **Project Page:** [https://aion-search.github.io](https://aion-search.github.io)
+- **GitHub Repository:** [https://github.com/NolanKoblischke/AION-Search](https://github.com/NolanKoblischke/AION-Search)
 
 ## Task
 
@@ -92,6 +93,28 @@ cutout = np.array(row["image_bands"], dtype=np.float32)  # (4, 96, 96)
 # cutout[0] = g-band, cutout[1] = r-band, cutout[2] = i-band, cutout[3] = z-band
 ```
 
+### Sample Usage: Semantic Search
+
+To perform semantic search as described in the AION-Search paper:
+
+```python
+from aionsearch import AIONSearchClipModel
+
+# Load pretrained model from HuggingFace
+model = AIONSearchClipModel.from_pretrained()
+
+# Project AION image embeddings into shared space
+aion_embedding = # Embedding of an image using github.com/PolymathicAI/AION
+projected_image = model.image_projector(aion_embedding)  # (batch, 768) -> (batch, 1024)
+
+# Project OpenAI text embeddings into shared space  
+text_embedding = # Embedding of text using text-embedding-3-large
+projected_text = model.text_projector(text_embedding)    # (batch, 3072) -> (batch, 1024)
+
+# Compute similarity for semantic search
+similarity = projected_image @ projected_text.T
+```
+
 ### Using with AION-1
 
 To tokenize with the AION codec and compute embeddings:
@@ -123,7 +146,6 @@ mean_embedding = embeddings.mean(dim=1)  # (1, 768)
 | Oquab et al. (2023) | 71.4 |
 | EfficientNet | 80.0 |
 | Walmsley et al. (2022) | 89.6 |
-| AION-Search | X |
 
 ## Data Sources