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strategy_embedding
Commit 73e77473 authored by 赵威's avatar 赵威
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add printer

parent 911a2f60
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doc_similarity/answer_similarity.py
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import os import os
import sys import sys
import random
sys.path.append(os.path.realpath(".")) sys.path.append(os.path.realpath("."))
import numpy as np import numpy as np
...@@ -52,6 +53,7 @@ if __name__ == "__main__": ...@@ -52,6 +53,7 @@ if __name__ == "__main__":
level_dict[content_level].append(id) level_dict[content_level].append(id)
embedding_dict[id] = bc.encode([content]) embedding_dict[id] = bc.encode([content])
print(random.choice(list(embedding_dict.item())))
answer_ids = np.array(list(embedding_dict.keys())).astype("int") answer_ids = np.array(list(embedding_dict.keys())).astype("int")
answer_embeddings = np.array(list(embedding_dict.values())).astype("float32") answer_embeddings = np.array(list(embedding_dict.values())).astype("float32")
print(answer_embeddings.shape) print(answer_embeddings.shape)
...@@ -64,7 +66,7 @@ if __name__ == "__main__": ...@@ -64,7 +66,7 @@ if __name__ == "__main__":
print("trained: " + str(index2.is_trained)) print("trained: " + str(index2.is_trained))
print("total index: " + str(index2.ntotal)) print("total index: " + str(index2.ntotal))
for i in [59753, 54792, 42643]: for i in [1015527, 1015536, 292379]:
D, I = index2.search(np.array(answer_embeddings[i]).astype("float32")) D, I = index2.search(np.array(answer_embeddings[i]).astype("float32"))
res = I.tolist() res = I.tolist()
print(res, "\n") print(res, "\n")
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