update demo

Code/inference.py

@@ -20,8 +20,9 @@ label = int(input())
 
 data_sample = {"sentence": sentence, "pos": target_pos, "label": label}
 print(data_sample)
+tokenizer=AutoTokenizer.from_pretrained("bert-base-uncased")
 
-
+input_as_dataset=preprocess.tokenizer_new(tokenizer, data_sample, max_length=512)
 # Load model
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
@@ -29,7 +30,7 @@ model=models.BertForWordClassification.from_pretrained("bert-base-uncased")
 model_path = "saved_models/bert_baseline.pth"
 model = torch.load(model_path, map_location=device)
 
-tokenizer=AutoTokenizer.from_pretrained("bert-base-uncased")
+
 
 train_sampler = RandomSampler(data_sample)
 train_dataloader=DataLoader(data_sample, sampler=train_sampler, batch_size=1)