Feature Extraction
sentence-transformers
PyTorch
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use odunola/e5-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use odunola/e5-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("odunola/e5-base-v2") 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] - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d0d559c47d5f71b1d280b13b62a2657f3e3bc70c0786f9ab91a36545e6a8f693
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size 437955512
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