Feature Extraction
sentence-transformers
ONNX
gemma3_text
teradata
byom
embeddings
gemma
gemma3
text-embeddings-inference
Instructions to use Teradata/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Teradata/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Teradata/embeddinggemma-300m") 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
File size: 133 Bytes
ac61f3b | 1 2 3 4 5 6 7 8 | {
"cache_implementation": "hybrid",
"do_sample": true,
"top_k": 64,
"top_p": 0.95,
"transformers_version": "4.57.0.dev0"
}
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