Merge pull request #527 from Mathieu-Prouveur/fix_value_training_loss
Update example files so that tr_loss is not affected by args.gradient…
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@@ -939,7 +939,7 @@ def main():
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elif output_mode == "regression":
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preds = np.squeeze(preds)
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result = compute_metrics(task_name, preds, all_label_ids.numpy())
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loss = tr_loss/nb_tr_steps if args.do_train else None
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loss = tr_loss/global_step if args.do_train else None
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result['eval_loss'] = eval_loss
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result['global_step'] = global_step
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@@ -1007,7 +1007,7 @@ def main():
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preds = preds[0]
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preds = np.argmax(preds, axis=1)
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result = compute_metrics(task_name, preds, all_label_ids.numpy())
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loss = tr_loss/nb_tr_steps if args.do_train else None
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loss = tr_loss/global_step if args.do_train else None
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result['eval_loss'] = eval_loss
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result['global_step'] = global_step
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