e9eb9d3cdef98f21f754b577b88dc259a9914ede,finetune/target_models/regressor.py,Regressor,predict,#Regressor#Any#,36

Before Change

        :param X: list or array of text to embed.
        :returns: list of class labels.
        
        return super().predict(X).tolist()

    def predict_proba(self, X):
        
        Produces a probability distribution over classes for each example in X.

After Change

        for _, start_of_doc, end_of_doc, label, _ in self.process_long_sequence(X, task="regression"):
            if start_of_doc:
                // if this is the first chunk in a document, start accumulating from scratch
                doc_labels = []
                
            doc_labels.append(label)

            if end_of_doc:
                // last chunk in a document
                means = np.mean(doc_labels, axis=0)
                label = self.input_pipeline.label_encoder.inverse_transform([means])
                all_labels.append(list(label))
        return all_labels

    def predict_proba(self, X):
        

Instances