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Teradata
/
granite-embedding-97m-multilingual-r2

Feature Extraction
sentence-transformers
ONNX
modernbert
teradata
byom
embeddings
long-context
text-embeddings-inference
Model card Files Files and versions
xet
Community

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
granite-embedding-97m-multilingual-r2
577 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
sasha-smirnov's picture
sasha-smirnov
Initial publish via td-embeddings
1c1264e verified 1 day ago
  • onnx
    Initial publish via td-embeddings 1 day ago
  • .gitattributes
    1.57 kB
    Initial publish via td-embeddings 1 day ago
  • README.md
    8.78 kB
    Initial publish via td-embeddings 1 day ago
  • config.json
    1.22 kB
    Initial publish via td-embeddings 1 day ago
  • special_tokens_map.json
    871 Bytes
    Initial publish via td-embeddings 1 day ago
  • tokenizer.json
    25.3 MB
    xet
    Initial publish via td-embeddings 1 day ago
  • tokenizer_config.json
    12.9 kB
    Initial publish via td-embeddings 1 day ago