How to use tomaarsen/sparse-mpnet-base-nq-fresh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/sparse-mpnet-base-nq-fresh") sentences = [ "what is the difference between uae and saudi arabia", "Monopoly Junior Players take turns in order, with the initial player determined by age before the game: the youngest player goes first. Players are dealt an initial amount Monopoly money depending on the total number of players playing: 20 in a two-player game, 18 in a three-player game or 16 in a four-player game. A typical turn begins with the rolling of the die and the player advancing their token clockwise around the board the corresponding number of spaces. When the player lands on an unowned space they must purchase the space from the bank for the amount indicated on the board, and places a sold sign on the coloured band at the top of the space to denote ownership. If a player lands on a space owned by an opponent the player pays the opponent rent in the amount written on the board. If the opponent owns both properties of the same colour the rent is doubled.", "Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4]", "Governors of states of India The governors and lieutenant-governors are appointed by the President for a term of five years." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
