How to use lucian-li/my_new_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lucian-li/my_new_model", trust_remote_code=True) sentences = [ "Pre-Emphasis (PE)\nA pre-emphasis filter is applied to the framed offset-free input signal:\n\n)1\n(", "Windowing (W)\nA Hamming window of length N is applied to the output of the pre-emphasis block:\n\n(\n)\nN\nn\nn\ns\nN\nn\nn\ns\npe\nw\n≤\n≤\n×\n\n\n\n\n\n\n\n\n\n\n\n\n−\n−\n×\n−\n=", "Group or broadcast call, called mobile stations (GSM only)\nWithin each set of voice group call or voice broadcast call attributes stored in the GCR as defined in 3GPP TS 43.068\nand 3GPP TS 43.069, respectively, a priority level is included if eMLPP is applied. The priority level will be provided\nby the GCR to the MSC together with the call attributes.\nThe priority level shall be indicated together with the related notification messages and treated in the mobile station as\ndefined in 3GPP TS 43.0", "Description of the access technology indicator mechanism\nThis clause describes the mechanisms that can be employed to indicate access technology specific dependencies in a\nmulti-access technology environment.\nThere are cases where toolkit applications need to know which access technology the terminal is currently in so that it\ncan issue access technology dependent commands as well as determine that the response to a particular command is\ntechnology dependent. Setting up the event, ACCESS TECHNOL" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
