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Y-Research-Group
/
CSR-NV_Embed_v2-Classification-MTOPIntent

Text Classification
sentence-transformers
Safetensors
Transformers
English
nvembed
feature-extraction
mteb
text
text-embeddings-inference
sparse-encoder
sparse
csr
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", trust_remote_code=True)
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Transformers

    How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
CSR-NV_Embed_v2-Classification-MTOPIntent / 3_SparseAutoEncoder
269 MB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 1 commit
Veritas2025's picture
Veritas2025
Sparse Encoder Update
4579def 10 months ago
  • config.json
    131 Bytes
    Sparse Encoder Update 10 months ago
  • model.safetensors
    269 MB
    xet
    Sparse Encoder Update 10 months ago