LearningMachine.py [747:760]:
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                                streaming_recoder.record(field, confidence_specified.data.numpy())
                    elif ProblemTypes[self.problem.problem_type] == ProblemTypes.regression:
                        logits = list(logits.values())[0]
                        # logits_softmax is unuseful for regression task!
                        logits_softmax = list(logits_softmax.values())[0]
                        logits_flat = logits.squeeze(1)
                        prediction_scores = logits_flat.detach().cpu().numpy()
                        streaming_recoder.record_one_row([prediction_scores])
                    elif ProblemTypes[self.problem.problem_type] == ProblemTypes.mrc:
                        for key, value in logits.items():
                            logits[key] = value.squeeze()
                        for key, value in logits_softmax.items():
                            logits_softmax[key] = value.squeeze()
                        passage_identify = None
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LearningMachine.py [858:871]:
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                        streaming_recoder.record(field, confidence_specified.data.numpy())
            elif ProblemTypes[self.problem.problem_type] == ProblemTypes.regression:
                logits = list(logits.values())[0]
                # logits_softmax is unuseful for regression task!
                logits_softmax = list(logits_softmax.values())[0]
                logits_flat = logits.squeeze(1)
                prediction_scores = logits_flat.detach().cpu().numpy()
                streaming_recoder.record_one_row([prediction_scores])
            elif ProblemTypes[self.problem.problem_type] == ProblemTypes.mrc:
                for key, value in logits.items():
                    logits[key] = value.squeeze()
                for key, value in logits_softmax.items():
                    logits_softmax[key] = value.squeeze()
                passage_identify = None
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