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1from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
2import numpy as np
3import torch
4
5model_name = "ruanchaves/bert-large-portuguese-cased-assin2-similarity"
6s1 = "A gente faz o aporte financeiro, é como se a empresa fosse parceira do Monte Cristo."
7s2 = "Fernando Moraes afirma que não tem vínculo com o Monte Cristo além da parceira."
8model = AutoModelForSequenceClassification.from_pretrained(model_name)
9tokenizer = AutoTokenizer.from_pretrained(model_name)
10config = AutoConfig.from_pretrained(model_name)
11model_input = tokenizer(*([s1], [s2]), padding=True, return_tensors="pt")
12with torch.no_grad():
13 output = model(**model_input)
14 score = output[0][0].detach().numpy().item()
15 print(f"Similarity Score: {np.round(float(score), 4)}")@software{Chaves_Rodrigues_eplm_2023,
author = {Chaves Rodrigues, Ruan and Tanti, Marc and Agerri, Rodrigo},
doi = {10.5281/zenodo.7781848},
month = {3},
title = {{Evaluation of Portuguese Language Models}},
url = {https://github.com/ruanchaves/eplm},
version = {1.0.0},
year = {2023}
}