Text Retrieval
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-Retrieval-NFcorpus 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-Retrieval-NFcorpus with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Y-Research-Group/CSR-NV_Embed_v2-Retrieval-NFcorpus", 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-Retrieval-NFcorpus with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Y-Research-Group/CSR-NV_Embed_v2-Retrieval-NFcorpus", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): cf41c3b
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README.md
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- text
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- transformers
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model-index:
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- name: NV-Embed-v2
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results:
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value: 0.39624
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task:
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type: Retrieval
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---
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- text
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- transformers
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- text-embeddings-inference
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- sparse-encoder
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- sparse
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- csr
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model-index:
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- name: NV-Embed-v2
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results:
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value: 0.39624
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task:
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type: Retrieval
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base_model:
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- nvidia/NV-Embed-v2
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