How to use SeppeV/roberta_TSDAE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SeppeV/roberta_TSDAE") sentences = [ "This . A engineer and go a trip walking the when a The physicist the distance of the the drop bullet his rifle fires the deer to . engineer his . to account for he rifle licks finger the speed and of fires deer 5 right . statistician \"got!\"", "This is a mean joke.\nA physicist, an engineer, and a statistician go on a hunting trip, they are walking through the woods when they spot a deer in a clearing. The physicist calculates the distance of the target, the velocity and drop of the bullet, adjusts his rifle and fires, missing the deer 5 feet to the left. The engineer rolls his eyes. 'You forgot to account for wind. Give it here', he snatches the rifle, licks his finger and estimates the speed and direction of the wind and fires, missing the deer 5 feet to the right. Suddenly, the statistician claps his hands and yells \"We got him!\"", "While driving to work, robbers jumped into my car and stole everything.\nThey were pirates of the car I be in.", "Driving and trying to read twitter, I just ran over a poodle. Unfortunately I drive a Yaris. My car got a dent and the poodle got annoyed." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
