TypeError: __init__() got an unexpected keyword argument 'evaluate_during_training'

[jtfields]

When running the sample Colab file, I receive an error when evaluate_during_training is set to True. When I comment out evaluate_during_training, the code runs but the calc_classification_metrics are not generated. I did find this message on HuggingFace which seems to be related to the issue with evaluate_during training -- https://github.com/huggingface/transformers/issues/8792.

[jtfields]

I changed the training_args from evaluate_during_training=True to evaluation_strategy='epoch'. With this change, trainer.train produces the error - AxisError: axis 1 is out of bounds for array of dimension 1. Any suggestions for how to fix this?

[akashsara]

I think this might just be an issue due to a change in how EvalPrediction returns results. In the calc_classification_metrics function, just set predictions = p.predictions[0] at the start and use predictions everywhere. I'll push a change reflecting this sometime soon.

[jtfields]

I changed evaluate_during_training = True to evaluation_strategy='epoch' and changed the following in calc_classification_metrics and everything works now. Thank you for the help resolving this issue!

def calc_classification_metrics(p: EvalPrediction):
  predictions = p.predictions[0]
  pred_labels = np.argmax(predictions, axis=1)
  pred_scores = softmax(predictions, axis=1)[:, 1]
  labels = p.label_ids
  if len(np.unique(labels)) == 2:  # binary classification
      roc_auc_pred_score = roc_auc_score(labels, pred_scores)
      precisions, recalls, thresholds = precision_recall_curve(labels,
                                                                pred_scores)
      fscore = (2 * precisions * recalls) / (precisions + recalls)
      fscore[np.isnan(fscore)] = 0
      ix = np.argmax(fscore)
      threshold = thresholds[ix].item()
      pr_auc = auc(recalls, precisions)
      tn, fp, fn, tp = confusion_matrix(labels, pred_labels, labels=[0, 1]).ravel()
      result = {'roc_auc': roc_auc_pred_score,
                'threshold': threshold,
                'pr_auc': pr_auc,
                'recall': recalls[ix].item(),
                'precision': precisions[ix].item(), 'f1': fscore[ix].item(),
                'tn': tn.item(), 'fp': fp.item(), 'fn': fn.item(), 'tp': tp.item()
                }
  else:
      acc = (pred_labels == labels).mean()
      f1 = f1_score(y_true=labels, y_pred=pred_labels)
      result = {
          "acc": acc,
          "f1": f1,
          "acc_and_f1": (acc + f1) / 2,
          "mcc": matthews_corrcoef(labels, pred_labels)
      }
  return result