def _generate_training_examples_md(self, training_data: "TrainingData") -> Text: """Generates markdown training examples.""" import rasa.nlu.training_data.util as rasa_nlu_training_data_utils training_examples = OrderedDict() # Sort by intent while keeping basic intent order for example in [ e.as_dict_nlu() for e in training_data.training_examples ]: rasa_nlu_training_data_utils.remove_untrainable_entities_from( example) intent = example[INTENT] training_examples.setdefault(intent, []) training_examples[intent].append(example) # Don't prepend newline for first line prepend_newline = False lines = [] for intent, examples in training_examples.items(): section_header = self._generate_section_header_md( INTENT, intent, prepend_newline=prepend_newline) lines.append(section_header) prepend_newline = True lines += [ self.generate_list_item(self.generate_message(example)) for example in examples ] return "".join(lines)
def prepare_training_examples(training_data: "TrainingData") -> OrderedDict: """Pre-processes training data examples by removing not trainable entities.""" import rasa.nlu.training_data.util as rasa_nlu_training_data_utils training_examples = OrderedDict() # Sort by intent while keeping basic intent order for example in [e.as_dict_nlu() for e in training_data.training_examples]: rasa_nlu_training_data_utils.remove_untrainable_entities_from(example) intent = example[INTENT] training_examples.setdefault(intent, []) training_examples[intent].append(example) return training_examples