![]() ![]() #7436: Make rasa data validate stories work for end-to-end. Response_selection_evaluation as machine-readable JSON payload instead of string. The report field for intent_evaluation, entity_evaluation and #7408: POST /model/test/intents now returns #7397: Add rasa train -dry-run command that allows to check if training needs to be performed Introduce make static-checks command to run all static checks locally. #7284: Run bandit checks on pull requests. #7257: Changed the format of the histogram of confidence values for both correct and incorrect predictions produced by running rasa test. #7232: Add support for the top-level response keys quick_replies, attachment and elements refered to in .send_reponse, as well as metadata. ![]() #3998: Added a message showing the location where the failed stories file was saved. ![]() Please update your configuration files using the following mapping: Old model option New model option transformer_size dictionary “transformer_size” with keys “text”, “action_text”, “label_action_text”, “dialogue” number_of_transformer_layers dictionary “number_of_transformer_layers” with keys “text”, “action_text”, “label_action_text”, “dialogue” dense_dimension dictionary “dense_dimension” with keys “text”, “action_text”, “label_action_text”, “intent”, “action_name”, “label_action_name”, “entities”, “slots”, “active_loop” Improvements Some model options for TEDPolicy got renamed. bot: On it # actual text that bot can output intent: inform # user message with entities action: utter_ask_howcanhelp # action that the bot should execute intent: greet # user message with no entities story: collect restaurant booking info # name of the story - just for debugging Here's an example of a dialogue in the Rasa story format: stories: If you don't have text in your stories, TED will behave the same way as before.Īdd possibility to predict entities using TED. Intent and entities or user text and bot actions or bot text. Namely, make it possible to train TED on stories that contain #7496: Make TED Policy an end-to-end policy. This URL will also be called in case of errors. To trigger asynchronous processing specifyĪ callback URL in the query parameter callback_url which Rasa Open Source should send To use cross-validation specify the query parameter cross_validation_folds in addition #7408: Add the option to use cross-validation to the These parameters are useful to configure when using incremental training for your pipelines. For moreĭetailed explanation of the command, check out the docs on incrementalĪdded a configuration parameter additional_vocabulary_size toĪnd number_additional_patterns to RegexFeaturizer. Instead, you can initialize the pipeline with a previously trained modelĪnd continue finetuning the model on the complete dataset consisting of If you have added new NLU training examples or new stories/rules forĭialogue manager, you don't need to train the pipeline from scratch. #6971: Incremental training of models in a pipeline is now supported. Their internal versions are now called during the Domain creation.Ĭalling them manually is no longer required. #7529: Domain.add_categorical_slot_default_value, Domain.add_requested_slotĪnd Domain.add_knowledge_base_slots are deprecated and will be removed in Rasa Open ![]()
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