Text Classification
sentence-transformers
Safetensors
Transformers
English
nvembed
feature-extraction
mteb
text
text-embeddings-inference
sparse-encoder
sparse
csr
custom_code
Eval Results (legacy)
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
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README.md
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output_folder="./results/MTOPIntentClassification",
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show_progress_bar=True
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encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8},
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) # MTEB don't support sparse tensors yet, so we need to convert to dense tensors
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```
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## Citation
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output_folder="./results/MTOPIntentClassification",
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show_progress_bar=True
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encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8},
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) # MTEB don't support sparse tensors yet, so we need to convert to dense tensors
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```
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## Citation
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