0.5.0

Added

• Tests to ensure that the whole pipeline is robust to naughty strings
• Added a NLUEngine.tag(text, intent) method for autotagging. When an intent as less than x queries, the tag method return (by order of priority in case of overlap): the previously seen entities in the NLUEngine intents, the bulitin entities, the result of the intent model trained with x queries. When there are more than x queries, then the autotagging output is the output of the model trained with x queries
• Added the ability to add pretrained intent to an NLUEngine with the NLUEngine.add_pretrained_model(intent, model_data) method
• Added the ability to only train particular intents when fitting a dataset with the intents argument of the NLUEngine.fit(dataset, intents=None) method

Changed

• Changed the serialization of the NLUEngine and most other objects of the lib
• Improved some feature performances by adding caching
• Improved builtin entities handling in regex generation
• snips_nlu_version key is now mandatory in the input dataset

Fixed

• Fixed bug with synonyms in the dataset, see: #224
• Fixed bug with unseen CRF labels at inference time, if some labels were not seen during training, we could ask the CRF probabilities of unseens labels when post processing builtin entities
• Fixed the bug happening with the intent classification feature extraction when the input queries were empty or only contained stop words, leading to an empty vocab for the Featurize.count_vectorizer

Removed

• the force_builtin_entities flag in the NLUEngine.parse method, autotagging is now handle by the NLUEngine.tag method
• removed deep intent support with the rust library, builtin intent are now light intents added from the registry