Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:3
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use mellameth/my-embedding-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mellameth/my-embedding-gemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mellameth/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:
- a81fa217b67ef4a1992b48a47651c27a2a19df419eafd1aad9c0bbd5ff49bde3
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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