README.md

metadata

license: mit
datasets:
  - mteb/twentynewsgroups-clustering
  - mteb/biorxiv-clustering-s2s
  - mteb/biorxiv-clustering-p2p
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 BiorxivClusteringP2P.v2
          type: mteb/biorxiv_clustering_p2p
          revision: f5dbc242e11dd8e24def4c4268607a49e02946dc
          config: default
          split: test
          languages:
            - eng-Latn
        metrics:
          - type: v_measure
            value: 0.579338
          - type: v_measure_std
            value: 0.00337
          - type: main_score
            value: 0.579338
        task:
          type: Clustering
      - dataset:
          name: MTEB BiorxivClusteringS2S.v2
          type: mteb/biorxiv_clustering_s2s
          revision: eb4edb10386758d274cd161093eb351381a16dbf
          config: default
          split: test
          languages:
            - eng-Latn
        metrics:
          - type: v_measure
            value: 0.540989
          - type: v_measure_std
            value: 0.005707
          - type: main_score
            value: 0.540989
        task:
          type: Clustering
      - dataset:
          name: MTEB TwentyNewsgroupsClustering
          type: mteb/twenty_newsgroups_clustering
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          config: default
          split: test
          languages:
            - eng-Latn
        metrics:
          - type: v_measure
            value: 0.630936
          - type: v_measure_std
            value: 0.007942
          - type: main_score
            value: 0.007942
        task:
          type: Clustering
base_model:
  - nvidia/NV-Embed-v2

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our Github.

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:

import mteb
from sentence_transformers import SparseEncoder
model = SparseEncoder(
    "CSR-NV_Embed_v2-Clustering-Biorxiv_TwentyNews",
    trust_remote_code=True
)
model.prompts = {
    "BiorxivClusteringP2P.v2": "Instruct: Identify the main category of Biorxiv papers based on the titles and abstracts\nQuery:",
    "BiorxivClusteringS2S.v2": "Instruct: Identify the main category of Biorxiv papers based on the titles\nQuery:",
    "TwentyNewsgroupsClustering": "Instruct: Identify the topic or theme of the given news articles\nQuery:"
}
task = mteb.get_tasks(tasks=["BiorxivClusteringP2P.v2", "BiorxivClusteringS2S.v2", "TwentyNewsgroupsClustering"])
evaluation = mteb.MTEB(tasks=task)
evaluation.run(
    model, 
    eval_splits=["test"], 
    output_folder="./results/clustering", 
    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

@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}
}