The architecture is based on a transformer which is shared for both tasks. |, | loss_type | "softmax" | The type of the loss function, either 'softmax' or 'margin'. Tokenizer from pre-trained language models. Is duckling correctly picking up the entity? """, rasa.shared.core.training_data.structures, rasa.shared.core.training_data.visualization, rasa.shared.core.training_data.story_writer, rasa.shared.core.training_data.story_writer.yaml_story_writer, rasa.shared.core.training_data.story_reader, rasa.shared.core.training_data.story_reader.yaml_story_reader, rasa.shared.core.training_data.story_reader.markdown_story_reader, rasa.shared.core.training_data.story_reader.story_step_builder, rasa.shared.core.training_data.story_reader.story_reader, rasa.shared.nlu.training_data.entities_parser, rasa.shared.nlu.training_data.synonyms_parser, rasa.shared.nlu.training_data.training_data, rasa.shared.nlu.training_data.lookup_tables_parser, rasa.shared.nlu.training_data.schemas.data_schema, rasa.shared.nlu.training_data.formats.readerwriter, rasa.shared.nlu.training_data.formats.luis, rasa.shared.nlu.training_data.formats.rasa, rasa.shared.nlu.training_data.formats.markdown, rasa.shared.nlu.training_data.formats.rasa_yaml, rasa.shared.nlu.training_data.formats.dialogflow, rasa.shared.nlu.training_data.formats.wit, rasa.shared.nlu.training_data.formats.markdown_nlg, rasa.core.training.converters.story_markdown_to_yaml_converter, rasa.core.featurizers.tracker_featurizers, rasa.core.featurizers.single_state_featurizer, rasa.nlu.utils.hugging_face.hf_transformers, rasa.nlu.utils.hugging_face.transformers_pre_post_processors, rasa.nlu.classifiers.sklearn_intent_classifier, rasa.nlu.classifiers.mitie_intent_classifier, rasa.nlu.classifiers.keyword_intent_classifier, rasa.nlu.featurizers.dense_featurizer.convert_featurizer, rasa.nlu.featurizers.dense_featurizer.lm_featurizer, rasa.nlu.featurizers.sparse_featurizer.lexical_syntactic_featurizer, rasa.nlu.featurizers.sparse_featurizer.regex_featurizer, rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer, rasa.nlu.training_data.converters.nlu_markdown_to_yaml_converter, rasa.nlu.training_data.converters.nlg_markdown_to_yaml_converter, rasa.nlu.extractors.duckling_http_extractor, rasa.nlu.extractors.regex_entity_extractor, rasa.nlu.extractors.mitie_entity_extractor, install duckling directly on your |, | dense_dimension | text: 512 | Dense dimension for sparse features to use if no dense |, | | label: 512 | features are present.
able to classify an intent with a confidence greater or equal than the threshold configuration. By default the language configured in the pipeline will be used as the language model name. the model name can be specified using the configuration variable model. Additionally, you will find this tutorial on
prediction's confidence and the associated response templates. The component can also be configured to train a response selector for a particular retrieval intent. |, | | | Set to '-1' to evaluate just once at the end of training. CRFEntityExtractor automatically finds the additional dense features and checks if the dense features are an
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