How to use minishlab/M2V_base_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_base_output") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape)
How to use minishlab/M2V_base_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_base_output") 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/minishlab/M2V_base_output/resolve/main/special_tokens_map.json
hf download hf://minishlab/M2V_base_output/special_tokens_map.json
curl -L -o special_tokens_map.json https://huggingface.co/minishlab/M2V_base_output/resolve/main/special_tokens_map.json
