Warning: This document is for an old version of rasa NLU. The latest version is 0.15.1.

Training Data Format

In the rasa NLU data format, there are three lists of examples: common_examples, intent_examples, and entity_examples. The common_examples are used to train both the entity and the intent models. In many cases it’s fine to put all of your training examples in there. However, if you need lots and lots of examples to train a good entity recogniser, that can mess up your intent model because your classes are totally unbalanced. In that case it makes sense to split up these lists.

Entities are specified with a start and end value, which together make a python style range to apply to the string, e.g. in the example below, with text="show me chinese restaurants", then text[8:15] == 'chinese'. Entities can span multiple words, and in fact the value field doesn’t have to correspond exactly to the substring in your example. That way you can map syonyms, or misspellings, to the same value.

  "text": "show me chinese restaurants",
  "intent": "restaurant_search",
  "entities": [
      "start": 8,
      "end": 15,
      "value": "chinese",
      "entity": "cuisine"