Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:3
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use jonc/my-embedding-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jonc/my-embedding-gemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jonc/my-embedding-gemma") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68450b3c6f440ac6ae208ce4ee880fdbf9c31d66f53aa33289296f69e952c47c
- Size of remote file:
- 33.4 MB
- SHA256:
- 216e2a79606fe879c9f17c529c71cd241338407fd5646b595ffd3c4b9ea1d503
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