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| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch.nn.functional as F | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "textattack/distilbert-base-uncased-CoLA") | |
| model = AutoModelForSequenceClassification.from_pretrained( | |
| "textattack/distilbert-base-uncased-CoLA") | |
| def classify_correctness(sentence: str): | |
| encoded_input = tokenizer(sentence, return_tensors='pt') | |
| output = model(**encoded_input) | |
| output_softmaxed = F.softmax(output[0], dim=1) | |
| correct = output_softmaxed.detach().numpy()[:].flatten()[1] | |
| return "{:.2f}".format(correct) | |