---
license: mit
datasets:
- mteb/banking77
language:
- en
pipeline_tag: text-classification
library_name: sentence-transformers
tags:
- mteb
- text
- transformers
- text-embeddings-inference
- sparse-encoder
- sparse
- csr

model-index:
- name: CSR
  results:
  - dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
    metrics:
    - type: accuracy
      value: 0.899545
    - type: f1
      value: 0.899018
    - type: f1_weighted
      value: 0.899018
    - type: main_score
      value: 0.899545
    task:
      type: Classification
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 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-Classification-Banking77",
    trust_remote_code=True
)
model.prompts = {
    "Banking77Classification": "Instruct: Given a online banking query, find the corresponding intents\nQuery:"
}
task = mteb.get_tasks(tasks=["Banking77Classification"])
evaluation = mteb.MTEB(tasks=task)
evaluation.run(
    model,
    eval_splits=["test"],
    output_folder="./results/Banking77Classification",
    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
@inproceedings{wenbeyond,
  title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
  author={Wen, Tiansheng and Wang, Yifei and Zeng, Zequn and Peng, Zhong and Su, Yudi and Liu, Xinyang and Chen, Bo and Liu, Hongwei and Jegelka, Stefanie and You, Chenyu},
  booktitle={Forty-second International Conference on Machine Learning}
}
```