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from sentence_transformers import SentenceTransformer, util | |
import torch | |
# save model in current directory | |
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2', device='cpu', cache_folder='./') | |
# save model in models folder (you need to create the folder on your own beforehand) | |
# model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2', device='cpu', cache_folder='./models/') | |
# Corpus with example sentences | |
corpus = [ | |
'I am a boy', | |
'What are you doing?', | |
'Can you help me?', | |
'A man is riding a horse.', | |
'A woman is playing violin.', | |
'A monkey is chasing after a goat', | |
'The quick brown fox jumps over the lazy dog' | |
] | |
# Query sentences: | |
queries = ['I am in need of assistance', '我是男孩子', 'Qué estás haciendo'] | |
corpus_embedding = model.encode(corpus, convert_to_tensor=True) | |
top_k = min(5, len(corpus)) | |
for query in queries: | |
query_embedding = model.encode(query, convert_to_tensor=True) | |
cos_scores = util.cos_sim(query_embedding, corpus_embedding)[0] | |
top_results = torch.topk(cos_scores, k=top_k) | |
print("Query:", query) | |
print("---------------------------") | |
for score, idx in zip(top_results[0], top_results[1]): | |
print(f'{round(score.item(), 3)} | {corpus[idx]}') |
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