def METHOD_NAME(model, metric, data_loader):
    model.eval()
    metric.reset()
    for input_ids, seg_ids, lens, labels in data_loader:
        preds = model(input_ids, seg_ids, lengths=lens)
        n_infer, n_label, n_correct = metric.compute(lens, preds, labels)
        metric.update(n_infer.numpy(), n_label.numpy(), n_correct.numpy())
        precision, recall, f1_score = metric.accumulate()
    print("[EVAL] Precision: %f - Recall: %f - F1: %f" % (precision, recall, f1_score))
    model.train()