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1from transformers import AutoModelForSequenceClassification, AutoTokenizer
2device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
3model = AutoModelForSequenceClassification.from_pretrained("TURKCELL/gibberish-detection-model-tr")
4tokenizer = AutoTokenizer.from_pretrained("TURKCELL/gibberish-detection-model-tr", do_lower_case=True, use_fast=True)
5
6model.to(device)
7
8def get_result_for_one_sample(model, tokenizer, device, sample):
9 d = {
10 1: 'gibberish',
11 0: 'real'
12 }
13 test_sample = tokenizer([sample], padding=True, truncation=True, max_length=256, return_tensors='pt').to(device)
14 # test_sample
15 output = model(**test_sample)
16 y_pred = np.argmax(output.logits.detach().to('cpu').numpy(), axis=1)
17 return d[y_pred[0]]
18
19sentence = "nabeer rdahdaajdajdnjnjf"
20result = get_result_for_one_sample(model, tokenizer, device, sentence)
21print(result)
22