def METHOD_NAME(self, inputs: Dict[str, Any],
                **postprocess_params) -> Dict[str, Any]:
    r"""Precess the prediction results
        Args:
            inputs (dict[str, Any]): should be tensors from model
        Returns:
            Dict[str, Any]: the prediction results
    """
    text = inputs['text']
    if OutputKeys.PREDICTIONS not in inputs:
        logits = inputs[OutputKeys.LOGITS]
        if len(logits.shape) == 3:
            logits = logits[0]
        predictions = torch.argmax(logits, dim=-1)
    else:
        predictions = inputs[OutputKeys.PREDICTIONS]
        if len(predictions.shape) == 2:
            predictions = predictions[0]
    binary_prediction = []
    for i, ch in enumerate(text):
        if ch in self.PUNC_LIST:
            binary_prediction.append(0)
        else:
            binary_prediction.append(-100)
    result_text = ''
    for i, p in enumerate(predictions):
        if i >= len(text):
            continue
        result_text += text[i]
        if binary_prediction[i] != -100:
            binary_prediction[i] = p
            if p == 1:
                result_text += '|'
    outputs = {
        'text': inputs['text'],
        'logits': inputs['logits'],
        'prediction': binary_prediction
    }
    return outputs