def METHOD_NAME(model, data_loader):
    """
    Predicts the data labels.
    Args:
        model (obj:`SemanticIndexBase`): A model to extract text embedding or calculate similarity of text pair.
        data_loader (obj:`List(Example)`): The processed data ids of text pair:
                                [query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids]
    Returns:
        results(obj:`List`): cosine similarity of text pairs.
    """
    batch_probs = []
    model.eval()
    with paddle.no_grad():
        for batch_data in data_loader:
            input_ids, token_type_ids = batch_data
            input_ids = paddle.to_tensor(input_ids)
            token_type_ids = paddle.to_tensor(token_type_ids)
            batch_prob = model(input_ids=input_ids, token_type_ids=token_type_ids).numpy()
            batch_probs.append(batch_prob)
        batch_probs = np.concatenate(batch_probs, axis=0)
        return batch_probs