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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)
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