Sentence Similarity
sentence-transformers
Safetensors
gemma
feature-extraction
mteb
Eval Results (legacy)
Instructions to use Jaume/gemma-2b-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Jaume/gemma-2b-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jaume/gemma-2b-embeddings") 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
Download tokenizer.model from Jaume/gemma-2b-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/Jaume/gemma-2b-embeddings/resolve/main/tokenizer.model
- Command line
-
hf download hf://Jaume/gemma-2b-embeddings/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Jaume/gemma-2b-embeddings/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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