olaffson:

#transform to tf.Dataset
    dataset = tf.data.Dataset.from_tensor_slices((dict(encodings)))



=> this is not necessary. Normally, you can directly provide the encodings to the model. Make sure to specify the additional argument return_tensors="tf" to get Tensorflow Tensors:
def predict_single_sentence(text_list, model, tokenizer):  
    # encode
    encoding = tokenizer(text_list, 
                         max_length=280, 
                         truncation=True, 
                         padding=True,
                         return_tensors="tf")
    # forward pass
    outputs = model(encoding)
    logits = outputs.logits
    
    # transform to array with probabilities
    probs = tf.nn.softmax(preds, axis=1).numpy() 
    
    return probs