How can I use sciBERT for Token Classification?

I tried with the code below:
```
from transformers import AutoTokenizer, AutoModel,AutoModelForTokenClassification
import torch

#I am getting the label list from labels.txt file present in the Pytorch Huggingface model(scibert-scivocab-uncased)
def read_label_list():
f = open('labels.txt','r')
label_list = []
for line in f:
label_list.append(line)
return label_list

tokenizer = AutoTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')
model = AutoModel.from_pretrained('allenai/scibert_scivocab_uncased')

sequence = 'Effectiveness of current drug treatments for hospitalized patients with SARS-CoV-2 infection (COVID-19 patients) in routine clinical practice|Risk factors or modifiers of pharmacological effect such as demographic characteristics, comorbidity or underlying pathology, concomitant medication.'

label_list = read_label_list()
tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
inputs = tokenizer.encode(sequence, return_tensors="pt")
outputs = model(inputs)[0]
predictions = torch.argmax(outputs, dim = 2)

for token, prediction in zip(tokens,predictions[0].numpy()):
print((token, label_list[prediction]))
```

I am getting the following output which is not making sense:
('[CLS]', '##.49\n')
('effectiveness', '##.49\n')
('of', '##.49\n')
('current', '##.49\n')
('drug', '##.49\n')
('treatments', '##.49\n')
('for', '##.49\n')
('hospitalized', '##.49\n')
('patients', '##.49\n')
('with', '##.49\n')
('sar', '##.49\n')
('##s', '##.49\n')
('-', '##.49\n')
('cov', '##.49\n')
('-', '##.49\n')
('2', '##.49\n')
('infection', '##.49\n')
('(', '##.49\n')
('cov', '##.49\n')
('##id', '##.49\n')
('-', '##.49\n')
('19', '##.49\n')
('patients', '##.49\n')
(')', '##.49\n')
('in', '##.49\n')
('routine', '##.49\n')
('clinical', '##.49\n')
('practice', '##.49\n')
('|', '##.49\n')
('risk', '##.49\n')
('factors', '##.49\n')
('or', '##.49\n')
('modi', '##.49\n')
('##fi', '##.49\n')
('##ers', '##.49\n')
('of', '##.49\n')
('pharmacological', '##.49\n')
('effect', '##.49\n')
('such', '##.49\n')
('as', '##.49\n')
('demographic', '##.49\n')
('characteristics', '##.49\n')
(',', '##.49\n')
('comorbidity', '##.49\n')
('or', '##.49\n')
('underlying', '##.49\n')
('pathology', '##.49\n')
(',', '##.49\n')
('concomitant', '##.49\n')
('medication', '##.49\n')
('.', '##1-4\n')
('[SEP]', '##.49\n')