Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-Embedding-4B
+library_name: sentence-transformers
+---
+## Description
+This is one [CSRv2](https://arxiv.org/abs/2602.05735) model finetuned on [MTEB](https://huggingface.co/mteb) 
+reranking datasets with [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) as backbone.
+
+For more details, including benchmark evaluation, hardware requirements, and inference performance, please 
+refer to our [Github](https://github.com/Y-Research-SBU/CSRv2).
+
+## Sentence Transformer Usage
+You can evaluate this model loaded by Sentence Transformers with the following code snippet (take SciDocsRR as one example):
+```python
+import mteb
+from sentence_transformers import SparseEncoder
+model = SparseEncoder(
+    "Y-Research-Group/CSRv2-reranking",  
+    trust_remote_code=True
+)
+model.prompts = {
+    "SciDocsRR": "Instruct: Given a title of a scientific paper, retrieve the titles of other relevant papers\n Query:"  
+}
+task = mteb.get_tasks(tasks=["SciDocsRR"])
+evaluation = mteb.MTEB(tasks=task)
+evaluation.run(
+    model,
+    eval_splits=["test"],
+    output_folder="./results/SciDocsRR",
+    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](https://github.com/Y-Research-SBU/CSRv2/blob/main/text/dataset_to_prompt.json)
+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` parameter` in the file `3_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](https://arxiv.org/abs/2602.05735) models finetuned on common tasks in
+[MTEB](https://huggingface.co/mteb) with [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) 
+as backbone. These tasks are:  
+
+- **[Classification](https://huggingface.co/Y-Research-Group/CSRv2-classification)**
+- **[Clustering](https://huggingface.co/Y-Research-Group/CSRv2-clustering)**
+- **[Retrieval](https://huggingface.co/Y-Research-Group/CSRv2-retrieval)**
+- **[STS](https://huggingface.co/Y-Research-Group/CSRv2-sts)**
+- **[Pair_classification](https://huggingface.co/Y-Research-Group/CSRv2-pair_classification)**
+- **[Reranking](https://huggingface.co/Y-Research-Group/CSRv2-reranking)**
+
+## 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}
+}
+```