---
license: mit
datasets:
- mteb/nfcorpus
language:
- en
pipeline_tag: text-retrieval
library_name: sentence-transformers
tags:
- mteb
- text
- transformers
- text-embeddings-inference
- sparse-encoder
- sparse
- csr
model-index:
- name: NV-Embed-v2
  results:
    - dataset:
        name: MTEB NFCorpus
        type: mteb/nfcorpus
        revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
        config: default
        split: test
        languages:
          - eng-Latn
      metrics:
        - type: ndcg@1
          value: 0.43189
        - type: ndcg@3
          value: 0.41132
        - type: ndcg@5
          value: 0.40406
        - type: ndcg@10
          value: 0.39624
        - type: ndcg@20
          value: 0.38517
        - type: ndcg@100
          value: 0.40068
        - type: ndcg@1000
          value: 0.49126
        - type: map@10
          value: 0.14342
        - type: map@100
          value: 0.21866
        - type: map@1000
          value: 0.2427
        - type: recall@10
          value: 0.1968
        - type: recall@100
          value: 0.45592
        - type: recall@1000
          value: 0.78216
        - type: precision@1
          value: 0.45511
        - type: precision@10
          value: 0.32353
        - type: mrr@10
          value: 0.537792
        - type: main_score
          value: 0.39624
      task:
        type: Retrieval
base_model:
- nvidia/NV-Embed-v2
---


For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [Github](https://github.com/neilwen987/CSR_Adaptive_Rep).


## Usage
📌 **Tip**: For NV-Embed-V2, using Transformers versions **later** than 4.47.0 may lead to performance degradation, as ``model_type=bidir_mistral`` in ``config.json`` is unsupported is no longer supported.

We recommend using ``Transformers 4.47.0.``

### Sentence Transformers Usage
You can evaluate this model loaded by Sentence Transformers with the following code snippet:
```python
import mteb
from sentence_transformers import SparseEncoder

model = SparseEncoder("Y-Research-Group/CSR-NV_Embed_v2-Retrieval-NFcorpus", trust_remote_code=True)
model.prompts = {
    "NFCorpus-query": "Instruct: Given a question, retrieve relevant documents that answer the question\nQuery:"
}

task = mteb.get_tasks(tasks=["NFCorpus"])
evaluation = mteb.MTEB(tasks=task)
evaluation.run(
    model,
    eval_splits=["test"],
    output_folder="./results/NFCorpus",
    show_progress_bar=True,
    encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8},
)  # MTEB don't support sparse tensors yet, so we need to convert to dense tensors
```

## Citation
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
      title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, 
      author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
      year={2025},
      eprint={2503.01776},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.01776}, 
}
```