import mteb
from sentence_transformers import SparseEncoder
model = SparseEncoder(
    "Y-Research-Group/CSRv2-classification",  
    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

It is suggested that you use our default prompts in evaluation.

Multi-TopK Support

Our model supports different sparsity levels due to the utilization of Multi-TopK loss in training. You can change sparsity model by adjusting the k parameterin the file3_SparseAutoEncoder/config.json`. We set sparsity level to 2 by default.

For instance, if you want to evaluate with sparsity level $K=8$ (which means there are 8 activated neurons in each embedding vector), the 3_SparseAutoEncoder/config.json should look like this:

json

{
    "input_dim": 2560,
    "hidden_dim": 10240,
    "k": 8,
    "k_aux": 1024,
    "normalize": false,
    "dead_threshold": 30
}

CSRv2 Qwen Series

We will release a series of CSRv2 models finetuned on common tasks in MTEB with Qwen3-Embedding-4B as backbone. These tasks are:

Citation

bibtex

@inproceedings{guo2026csrv2,
    title={{CSR}v2: Unlocking Ultra-sparse Embeddings},
    author={Guo, Lixuan and Wang, Yifei and Wen, Tiansheng and Wang, Yifan and Feng, Aosong and Chen, Bo and Jegelka, Stefanie and You, Chenyu},
    booktitle={International Conference on Learning Representations (ICLR)},
    year={2026}
}