 How to use  You can use the model by feeding sentences to the following code:  ```Python import torch from transformers import CanineTokenizer, CanineForTokenClassification  tokenizer = CanineTokenizer.from_pretrained('slone/canine-c-bashkir-gec-v1') model = CanineForTokenClassification.from_pretrained('slone/canine-c-bashkir-gec-v1') if torch.cuda.is_available():     model.cuda()  LABELS_THIS = [c[5:] for c in model.config.id2label.values() if c.startswith('THIS_')] LABELS_NEXT = [c[5:] for c in model.config.id2label.values() if c.startswith('NEXT_')]  def fix_text(text, boost=0):     """Apply the model to edit the text. `boost` is a parameter to control edit aggressiveness."""     bx = tokenizer(text, return_tensors='pt', padding=True)     with torch.inference_mode():         out = model(**bx.to(model.device))         n1, n2 =  len(LABELS_THIS), len(LABELS_NEXT)         logits1 = out.logits[0, :, :n1].view(-1, n1)         logits2 = out.logits[0, :, n1:].view(-1, n2)         if boost:             logits1[1:, 0] -= boost             logits2[:, 0] -= boost         ids1, ids2 = logits1.argmax(-1).tolist(), logits2.argmax(-1).tolist()     result = []     for c, id1, id2 in zip(' ' + text, ids1, ids2):         l1, l2 = LABELS_THIS[id1], LABELS_NEXT[id2]         if l1 == 'KEEP':             result.append(c)         elif l1 != 'DELETE':             result.append(l1)         if l2 != 'PASS':             result.append(l2)     return ''.join(result)  text = 'У йыл дан д ың йөҙө һoрөмлэнде.' print(fix_text(text))  