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license: mit
task_categories:
- feature-extraction
- text-retrieval
tags:
- embeddings
- vector-database
- retrieval
- beir
- la2m
pretty_name: LA2M Embeddings
---
# LA2M_emb
Embedding vectors used in the paper
**[Integrating Vector Databases across Embedding Models](https://dl.acm.org/doi/10.1145/3769803)**
(Beining Yang, Yang Cao, Yang Ren. *Proc. ACM Manag. Data* 3(6) (SIGMOD), Article 338, 2025).
This is a **curated subset of [DB-Edinburgh/VectorBenchmark](https://huggingface.co/datasets/DB-Edinburgh/VectorBenchmark)**,
restricted to exactly the 6 embedding models (Table 3) and 5 benchmarks (Table 2) reported in the paper,
so that the experiments can be reproduced without pulling the full ~6k-file benchmark.
Code: [VectorMerge](https://github.com/Suchun-sv/VectorMerge) (`reproduce` branch has a Docker setup).
## Contents
`embeddings/{corpus,query}_embeddings_{model}_{dataset}.npy` — plain `numpy` arrays, one row per document / query,
row order matches the BEIR `corpus.jsonl` / `queries.jsonl` of the corresponding dataset.
| paper model (Table 3) | file key | dim in these files | dim in Table 3 |
|---|---|---|---|
| GloVe | `glove` | 300 | 300 |
| FastText | `fast-text` | 300 | 300 |
| Mistral | `mistral` | 1024 | 1024 |
| NV-Embed-V2 | `nv-embed` | 4096 | 1024 |
| GTE-Qwen2 | `gte` | 3584 | 1024 |
| OpenAI-Ada | `openai` | 1536 | 1536 |
| dataset | corpus rows | query rows |
|---|---|---|
| `scifact` | 5,183 | 300 |
| `nfcorpus` | 3,633 | 323 |
| `arguana` | 8,674 | 1,406 |
| `scidocs` | 25,657 | 1,000 |
| `fiqa` | 57,638 | 648 |
Corpus row counts match Table 2 of the paper exactly.
## Known quirks
These are inherited from the source benchmark and are documented rather than silently fixed:
- **`nv-embed` vs `nv-embed-v2`.** The paper's NV-Embed-V2 vectors are stored under the `nv-embed` key,
which is also the name the `vectormerge` code expects. `nv-embed-v2`-keyed files exist only for
`scidocs` and `fiqa`; they are included here for completeness and have identical shapes.
- **`query_embeddings_mistral_scifact.npy` has 320 rows**, not the 300 that every other model has for
SciFact (and that Table 2 reports). Index-aligned evaluation on that one file needs care.
- **Mixed dtypes.** Most files are `float32`; `openai`, `mistral`, and `glove` (arguana, fiqa) are `float64`.
- **Reported vs stored dimension.** Table 3 lists 1024 for NV-Embed-V2 and GTE-Qwen2, while the stored
vectors are at native dimension (4096 / 3584). The `vectormerge` pipeline aligns dimensions at load time
(`align_dimension`), so the 1024 in the table refers to the model family rather than these files.
## Usage
```python
from huggingface_hub import hf_hub_download
import numpy as np
path = hf_hub_download(
"DB-Edinburgh/LA2M_emb",
"embeddings/corpus_embeddings_mistral_scifact.npy",
repo_type="dataset",
)
X = np.load(path) # (5183, 1024)
```
## Citation
```bibtex
@article{Yang2025integrating,
author = {Beining Yang and Yang Cao and Yang Ren},
title = {Integrating Vector Databases across Embedding Models},
journal = {Proc. {ACM} Manag. Data},
volume = {3},
number = {6},
pages = {1--28},
year = {2025},
doi = {10.1145/3769803}
}
```
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