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from sentence_transformers import CrossEncoder
import numpy as np
# Let's use a reranker to get better results from our semantic search
def reranker(query, matches):
pairs = []
for match in matches:
pairs.append([query, match["metadata"]["text"]])
model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L-2-v2', max_length = 512)
scores = model.predict(pairs)
top_indices = np.argsort(scores)[::-4]
top_results = ["Class: " + matches[index]["metadata"]["text"] + "Class Url: " + matches[index]["metadata"]["url"] + "Class Time: " + str(matches[index]["metadata"]["time"]) for index in top_indices]
return top_results |