Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,67 @@
+---
+license: apache-2.0
+task_categories:
+  - feature-extraction
+tags:
+  - ann-benchmark
+  - vector-search
+  - embeddings
+  - cosine-similarity
+size_categories:
+  - 1M<n<10M
+---
+
+# Cohere-1M Wikipedia 768-d Embeddings
+
+Pre-computed 768-dimensional embeddings of 1M English Wikipedia articles, generated using Cohere's `embed-english-v2.0` (multilingual-22-12) model.
+
+This dataset is used for ANN (Approximate Nearest Neighbor) benchmarking in the [QuIVer paper](https://arxiv.org/abs/2605.02171) (PVLDB Vol. 20, 2027).
+
+## Files
+
+| File | Shape | Format | Description |
+|------|-------|--------|-------------|
+| `cohere_train.f32` | 1,000,000 × 768 | float32 raw binary | Base vectors (L2-normalized) |
+| `cohere_test.f32` | 1,000 × 768 | float32 raw binary | Query vectors (L2-normalized) |
+| `cohere_groundtruth.i32` | 1,000 × 1,000 | int32 raw binary | Ground truth top-1000 neighbor IDs (cosine) |
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download files
+train_path = hf_hub_download("YoKONCy/Cohere-1M-wikipedia-768d", "cohere_train.f32")
+test_path = hf_hub_download("YoKONCy/Cohere-1M-wikipedia-768d", "cohere_test.f32")
+gt_path = hf_hub_download("YoKONCy/Cohere-1M-wikipedia-768d", "cohere_groundtruth.i32")
+
+# Load
+train = np.fromfile(train_path, dtype=np.float32).reshape(-1, 768)
+test = np.fromfile(test_path, dtype=np.float32).reshape(-1, 768)
+gt = np.fromfile(gt_path, dtype=np.int32).reshape(1000, -1)
+
+print(f"Train: {train.shape}, Test: {test.shape}, GT: {gt.shape}")
+```
+
+## Data Format
+
+All files use **headerless raw binary format**:
+- `.f32`: contiguous `float32` values, row-major. File size = N × D × 4 bytes.
+- `.i32`: contiguous `int32` values, row-major. File size = Q × K × 4 bytes.
+
+## Source
+
+Originally sampled from [Cohere/wikipedia-22-12-en-embeddings](https://cohere.com/) (now deprecated on HuggingFace). Vectors are L2-normalized for cosine similarity evaluation.
+
+## Citation
+
+```bibtex
+@article{quiver2026,
+  title   = {QuIVer: Rethinking ANN Graph Topology via Training-Free Binary Quantization},
+  author  = {Xiao, Wenxuan and Wang, Zhiyou and Li, Chengcheng},
+  journal = {arXiv preprint arXiv:2605.02171},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2605.02171}
+}
+```