How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("webmp3/Sakura-EmbeddingGemma-2-AutoRound") 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]
How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="webmp3/Sakura-EmbeddingGemma-2-AutoRound")
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound") model = AutoModel.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound", device_map="auto")
https://huggingface.co/webmp3/Sakura-EmbeddingGemma-2-AutoRound/resolve/main/1_Pooling/config.json
hf download hf://webmp3/Sakura-EmbeddingGemma-2-AutoRound/1_Pooling/config.json
curl -L -o config.json https://huggingface.co/webmp3/Sakura-EmbeddingGemma-2-AutoRound/resolve/main/1_Pooling/config.json
