afef07922d8a5070e1afd54ee53b210c6b3ec527,finetune/target_models/sequence_labeling.py,SequenceLabeler,_predict,#SequenceLabeler#Any#Any#Any#,214

Before Change

            proba_seq_masked = proba_seq.copy()
            for il, label in enumerate(label_seq):
                label_idx = self.input_pipeline.label_encoder.classes_.index(label)
                proba_seq_masked[il:, label_idx] = 0.0
            doc_level_probas.append(np.max(proba_seq_masked, axis=0))

            for label, start_idx, end_idx, proba in zip(

After Change

        elif return_negative_probs:
            classes = self.input_pipeline.label_encoder.classes_
            output = []
            for anno, probas in zip(doc_annotations, all_doc_level_probas):
                output.append(
                    {
                        "prediction": anno,
                        "negative_probs": dict(zip(classes, probs))
                    }
                )
            return output
        else:
            return doc_annotations

Instances