Usually, the last layer of a classification model (in your case TFBertForSequenceClassification), produces raw prediction values as real numbers ranging from [-infinity, +infinity]  These raw, unconstrained prediction values are commonly known as logits
Now after this, we usually want to normalize these outputs to a probability distribution over predicted output classes. To do this, we usually use a normalization layer such as sigmoid and softmax for binary and multi-class classification respectively.
The output of your softmax function is now a probability and to convert that into your classes, we just take the max of these.
To do that with your onnx outputs, you can do something like this:
import numpy as np
from scipy.special import softmax

np.argmax(softmax(outputs[0][0], axis=0))