Feature Extraction
sentence-transformers
ONNX
modernbert
teradata
byom
embeddings
long-context
text-embeddings-inference
Instructions to use Teradata/granite-embedding-97m-multilingual-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Teradata/granite-embedding-97m-multilingual-r2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Teradata/granite-embedding-97m-multilingual-r2") 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
- Xet hash:
- 27e665ae4aa3e3ceb968eb347d8ea4d24f6c6a6b6453dca15e4c54052feade30
- Size of remote file:
- 25.3 MB
- SHA256:
- 51947676cae1f991fa51c6b9a24e14ee5460e5f0b9f692f13bb3159829d1592a
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