How to use mikeee/m2v-gemma-embedding-300m with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("mikeee/m2v-gemma-embedding-300m") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape)
How to use mikeee/m2v-gemma-embedding-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mikeee/m2v-gemma-embedding-300m") 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]
https://huggingface.co/mikeee/m2v-gemma-embedding-300m/resolve/main/tokenizer.json
hf download hf://mikeee/m2v-gemma-embedding-300m/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/mikeee/m2v-gemma-embedding-300m/resolve/main/tokenizer.json
