Sentence Similarity
Model2Vec
Safetensors
sentence-transformers
Sanskrit
English
embeddings
static-embeddings
sanskrit
multilingual
Instructions to use karthikrajgopal/distilled-embeddinggemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use karthikrajgopal/distilled-embeddinggemma with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("karthikrajgopal/distilled-embeddinggemma") - sentence-transformers
How to use karthikrajgopal/distilled-embeddinggemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("karthikrajgopal/distilled-embeddinggemma") 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
- Xet hash:
- a60229c6849e9a10169e9dd754c5f7e61f76ed58f9f33856d3e3b61f64b25d22
- Size of remote file:
- 13.4 MB
- SHA256:
- f8a5d8279dc7348bb3000f0b695dabd43c7fb503892799b57e346244b0341f75
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