def METHOD_NAME(model, metric, data_loader):
    """
    Given a dataset, it evals model and computes the metric.
    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
    """
    model.eval()
    metric.reset()
    for batch in data_loader:
        input_ids, token_type_ids, att_mask, pos_ids, start_ids, end_ids = batch
        start_prob, end_prob = model(input_ids, token_type_ids, att_mask, pos_ids)
        start_ids = paddle.cast(start_ids, "float32")
        end_ids = paddle.cast(end_ids, "float32")
        num_correct, num_infer, num_label = metric.compute(start_prob, end_prob, start_ids, end_ids)
        metric.update(num_correct, num_infer, num_label)
    precision, recall, f1 = metric.accumulate()
    model.train()
    return precision, recall, f1