sentence-transformers How to use danthepol/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("danthepol/MNLP_M3_document_encoder")
sentences = [
"A certain junior class has 1000 students and a certain senior class has 900 students. Among these students, there are 60 siblings pairs each consisting of 1 junior and 1 senior. If 1 student is to be selected at random from each class, what is the probability that the 2 students selected will be a sibling pair?",
"Let's see Pick 60/1000 first Then we can only pick 1 other pair from the 800 So total will be 60 / 900 *1000 Simplify and you get 2/30000",
"To maximize number of hot dogs with 300$ Total number of hot dogs bought in 250-pack = 22.95*13 =298.35$ Amount remaining = 300 - 298.35 = 1.65$ This amount is too less to buy any 8- pack . Greatest number of hot dogs one can buy with 300 $ = 250*13 = 3250",
"artificial leg"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
