add-dataset-v1

README.md

@@ -1,7 +1,6 @@
 ---
 task_categories:
 - text-retrieval
-- image-to-text
 - sentence-similarity
 language:
 - en
@@ -25,12 +24,8 @@ It is specifically designed for integration with VectorDBBench.
 
 ### Dataset Summary
 
-- **Source Data**: 
-  - Wikipedia (Public Dataset)
-  - LAION0400M (Public Dataset)
-- **Embedding Model**: 
-  - google/embeddinggemma-300m
-  - sentence-transformers/clip-ViT-B-32
+- **Source Data**: Wikipedia (Public Dataset)
+- **Embedding Model**: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)
 
 ## Dataset Structure
 
@@ -39,6 +34,7 @@ For each embedding model, the directory contains two key file:
 | Data | Description |
 |-------|-------------|
 | `centroids.npy` | centroids as followed IVF |
+| `tree_info.pkl` | tree metadata with parent and leaf info |
 
 ## Data Fields
 
@@ -46,19 +42,35 @@ For each embedding model, the directory contains two key file:
 
 - **Purpose**: Finding the nearest clusters for IVF (Inverted File Index)
 - **Type**: NumPy array (`np.ndarray`)
-- **Shape**: `[32768, 768]` or `[1024, 512]`
-- **Description**: 768-dimensional vectors representing 32,768 cluster centroids, or 512-dimensional vectors representing 1,024 cluster centroids.
+- **Shape**: `[32768, 768]`
+- **Description**: 768-dimensional vectors representing 32,768 cluster centroids
 - **Normalization**: L2-normalized (unit norm)
 - **Format**: float32
 
+### Tree Metadata: `tree_info.pkl`
+
+- **Purpose**: Finding virtual clusters following hierarchical tree structure for efficient GAS search
+- **Type**: Python dictionary (pickle)
+- **Keys**:
+  - `node_parents`: Dictionary mapping each node ID to its parent node ID
+    - Format: `{node_id: parent_node_id, ...}`
+    - Contains parent-child relationships for all nodes in the tree
+  
+  - `leaf_ids`: List of leaf node IDs
+    - Format: `[leaf_id_1, leaf_id_2, ..., leaf_id_32768]`
+    - Total 32,768 leaf nodes (corresponding to 32,768 centroids)
+  
+  - `leaf_to_centroid_idx`: Mapping from leaf node IDs to centroid indices in `centroids.npy`
+    - Format: `{leaf_node_id: centroid_index, ...}`
+    - Maps each leaf node to its corresponding row index in `centroids.npy`
+    - Important: Leaf IDs in `leaf_ids` are ordered sequentially, so the i-th leaf corresponds to the i-th centroid
+
 
 ## Dataset Creation
 
 ### Source Data
 
-Source dataset is a large public dataset: 
-- Wikipedia: [mixedbread-ai/wikipedia-data-en-2023-11](https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11)
-- LAION: [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/).
+Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-data-en-2023-11](https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11).
 
 ### Preprocessing
 
@@ -66,7 +78,7 @@ Source dataset is a large public dataset:
 
    Description TBD
 
-2. Chunking (for text): For texts exceeding 2048 tokens:
+2. Chunking: For texts exceeding 2048 tokens:
 
     - Split into chunks with ~100 token overlap
     - Embedded each chunk separately
@@ -76,16 +88,10 @@ Source dataset is a large public dataset:
 
 ### Embedding Generation
 
-- Text:
-  - Model: google/embeddinggemma-300m
-  - Dimension: 768
-  - Max Token Length: 2048
-  - Normalization: L2-normalized
-
-- Multi-Modal:
-  - Model: sentence-transformers/clip-ViT-B-32
-  - Dimension: 512
-  - Normalization: L2-normalized
+- Model: google/embeddinggemma-300m
+- Dimension: 768
+- Max Token Length: 2048
+- Normalization: L2-normalized
 
 ## Usage
 
@@ -93,9 +99,10 @@ Source dataset is a large public dataset:
 import wget
 
 def download_centroids(embedding_model: str, dataset_dir: str) -> None:
-    """Download pre-computed centroids for IVF_GAS."""
-    dataset_link = f"https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/{embedding_model}"
+    """Download pre-computed centroids and tree info for GAS."""
+    dataset_link = "https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/embeddinggemma-300m"
     wget.download(f"{dataset_link}/centroids.npy", out="centroids.npy")
+    wget.download(f"{dataset_link}/tree_info.pkl", out="tree_info.pkl")
 ```
 
 ## License
@@ -128,16 +135,6 @@ If you use this dataset, please cite:
 }
 ```
 
-```bibtex
-@dataset{laion400m,
-  author = {Schuhmann, Christoph and others},
-  title = {LAION-AI},
-  year = {2021},
-  publisher = {LAION},
-  url = {https://laion.ai/blog/laion-400-open-dataset}
-}
-```
-
 ### Embedding Model Citation
 
 ```bibtex
@@ -149,21 +146,8 @@ If you use this dataset, please cite:
 }
 ```
 
-```bibtex
-@misc{clipvitb32,
-  title={CLIP ViT-B/32},
-  author={Open AI},
-  year={2021},
-  url={https://huggingface.co/sentence-transformers/clip-ViT-B-32}
-}
-```
-
 ### Acknowledgments
 
-- Original dataset: 
-  - mixedbread-ai/wikipedia-data-en-2023-11
-  - LAION-400M
-- Embedding model: 
-  - google/embeddinggemma-300m
-  - sentence-transformers/clip-ViT-B-32
+- Original dataset: mixedbread-ai/wikipedia-data-en-2023-11
+- Embedding model: google/embeddinggemma-300m
 - Benchmark framework: VectorDBBench

clip-vit-b-32/centroids.npy → embeddinggemma-300m/tree_info.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1feead304605e263b9d0f3e35b8e10ced82a938cec222ba36f65c7e8d7e7b954
-size 2097280
+oid sha256:e0aff444db4a474220e24c5ab243f4f1bfc0c56d972333c0b9c3b1422ca3e552
+size 687518