def METHOD_NAME(model, metric, data_loader):
    model.eval()
    metric.reset()
    for token_ids, lengths, label_ids in data_loader:
        preds = model(token_ids, lengths)
        n_infer, n_label, n_correct = metric.compute(lengths, preds, label_ids)
        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()