README.md

---
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}
}
```