| --- |
| 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} |
| } |
| ``` |
|
|
