Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:3
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use zbahhah/my-embedding-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zbahhah/my-embedding-gemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zbahhah/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:
- 0fa8ff705c25e5a9099aac79e90151124edd76728b9b5294722340faa0cc20ca
- Size of remote file:
- 1.21 GB
- SHA256:
- 420b9bbc655f1280af350cb05ce7eb779af19c78c2d1644a49c862be192689c5
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