How to use NilsML/fine_tuned_miniLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NilsML/fine_tuned_miniLM") sentences = [ "Structurally, diplomonads have two equal-sized what and multiple flagella?", "deciding when to buy or sell a stock is not an easy task because the market is hard to predict, being influenced by political and economic factors. thus, methodologies based on computational intelligence have been applied to this challenging problem. in this work, every day the stocks are ranked by technique for order preference by similarity to ideal solution ( topsis ) using technical analysis criteria, and the most suitable stock is selected for purchase. even so, it may occur that the market is not favorable to purchase on certain days, or even, the topsis make an incorrect selection. to improve the selection, another method should be used. so, a hybrid", "we present the analysis of the brightest flare that was recorded in the \\ emph { insight } - hmxt data set, in a broad energy range ( 2 $ - $ 200 kev ) from the microquasar grs ~ 1915 + 105 during an unusual low - luminosity state. this flare was detected by \\ emph { insight } - hxmt among a series of flares during 2 june 2019 utc 16 : 37 : 06 to 20 : 11 : 36, with a 2 - 200 kev luminosity of 3. 4 $ - $ 7. 27 $ \\ times10 ^ { 38 } $ er", "nuclei" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
