Warning: This document is for an old version of Rasa. The latest version is 1.2.4.

Training Data Format

Data Format

You can provide training data as Markdown or as JSON, as a single file or as a directory containing multiple files. Note that Markdown is usually easier to work with.

Markdown Format

Markdown is the easiest Rasa NLU format for humans to read and write. Examples are listed using the unordered list syntax, e.g. minus -, asterisk *, or plus +. Examples are grouped by intent, and entities are annotated as Markdown links, e.g. [entity](entity name).

## intent:check_balance
- what is my balance <!-- no entity -->
- how much do I have on my [savings](source_account) <!-- entity "source_account" has value "savings" -->
- how much do I have on my [savings account](source_account:savings) <!-- synonyms, method 1-->
- Could I pay in [yen](currency)?  <!-- entity matched by lookup table -->

## intent:greet
- hey
- hello

## synonym:savings   <!-- synonyms, method 2 -->
- pink pig

## regex:zipcode
- [0-9]{5}

## lookup:currencies   <!-- lookup table list -->
- Yen
- USD
- Euro

## lookup:additional_currencies  <!-- no list to specify lookup table file -->
path/to/currencies.txt

The training data for Rasa NLU is structured into different parts:

  • examples
  • synonyms
  • regex features and
  • lookup tables

Synonyms will map extracted entities to the same name, for example mapping “my savings account” to simply “savings”. However, this only happens after the entities have been extracted, so you need to provide examples with the synonyms present so that Rasa can learn to pick them up.

Lookup tables may be specified either directly as lists or as txt files containing newline-separated words or phrases. Upon loading the training data, these files are used to generate case-insensitive regex patterns that are added to the regex features. For example, in this case a list of currency names is supplied so that it is easier to pick out this entity.

JSON Format

The JSON format consists of a top-level object called rasa_nlu_data, with the keys common_examples, entity_synonyms and regex_features. The most important one is common_examples.

{
    "rasa_nlu_data": {
        "common_examples": [],
        "regex_features" : [],
        "lookup_tables"  : [],
        "entity_synonyms": []
    }
}

The common_examples are used to train your model. You should put all of your training examples in the common_examples array. Regex features are a tool to help the classifier detect entities or intents and improve the performance.

Generating More Entity Examples

It is sometimes helpful to generate a bunch of entity examples, for example if you have a database of restaurant names. There are a couple of tools built by the community to help with that.

You can use Chatito , a tool for generating training datasets in rasa’s format using a simple DSL or Tracy, a simple GUI to create training datasets for rasa.

However, creating synthetic examples usually leads to overfitting, it is a better idea to use Lookup Tables instead if you have a large number of entity values.

Common Examples

Common examples have three components: text, intent, and entities. The first two are strings while the last one is an array.

  • The text is the user message [required]
  • The intent is the intent that should be associated with the text [optional]
  • The entities are specific parts of the text which need to be identified [optional]

Entities are specified with a start and an 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 does not have to correspond exactly to the substring in your example. That way you can map synonyms, or misspellings, to the same value.

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

Entity Synonyms

If you define entities as having the same value they will be treated as synonyms. Here is an example of that:

[
  {
    "text": "in the center of NYC",
    "intent": "search",
    "entities": [
      {
        "start": 17,
        "end": 20,
        "value": "New York City",
        "entity": "city"
      }
    ]
  },
  {
    "text": "in the centre of New York City",
    "intent": "search",
    "entities": [
      {
        "start": 17,
        "end": 30,
        "value": "New York City",
        "entity": "city"
      }
    ]
  }
]

As you can see, the entity city has the value New York City in both examples, even though the text in the first example states NYC. By defining the value attribute to be different from the value found in the text between start and end index of the entity, you can define a synonym. Whenever the same text will be found, the value will use the synonym instead of the actual text in the message.

To use the synonyms defined in your training data, you need to make sure the pipeline contains the EntitySynonymMapper component (see Components).

Alternatively, you can add an “entity_synonyms” array to define several synonyms to one entity value. Here is an example of that:

{
  "rasa_nlu_data": {
    "entity_synonyms": [
      {
        "value": "New York City",
        "synonyms": ["NYC", "nyc", "the big apple"]
      }
    ]
  }
}

Note

Please note that adding synonyms using the above format does not improve the model’s classification of those entities. Entities must be properly classified before they can be replaced with the synonym value.

Regular Expression Features

Regular expressions can be used to support the intent classification and entity extraction. E.g. if your entity has a certain structure as in a zipcode, you can use a regular expression to ease detection of that entity. For the zipcode example it might look like this:

{
    "rasa_nlu_data": {
        "regex_features": [
            {
                "name": "zipcode",
                "pattern": "[0-9]{5}"
            },
            {
                "name": "greet",
                "pattern": "hey[^\\s]*"
            },
        ]
    }
}

The name doesn’t define the entity nor the intent, it is just a human readable description for you to remember what this regex is used for and is the title of the corresponding pattern feature. As you can see in the above example, you can also use the regex features to improve the intent classification performance.

Try to create your regular expressions in a way that they match as few words as possible. E.g. using hey[^\s]* instead of hey.*, as the later one might match the whole message whereas the first one only matches a single word.

Regex features for entity extraction are currently only supported by the CRFEntityExtractor component! Hence, other entity extractors, like MitieEntityExtractor or SpacyEntityExtractor won’t use the generated features and their presence will not improve entity recognition for these extractors. Currently, all intent classifiers make use of available regex features.

Note

Regex features don’t define entities nor intents! They simply provide patterns to help the classifier recognize entities and related intents. Hence, you still need to provide intent & entity examples as part of your training data!

Lookup Tables

Lookup tables in the form of external files or lists of elements may also be specified in the training data. The externally supplied lookup tables must be in a newline-separated format. For example, data/test/lookup_tables/plates.txt may contain:

tacos
beef
mapo tofu
burrito
lettuce wrap

And can be loaded as:

{
    "rasa_nlu_data": {
        "lookup_tables": [
            {
                "name": "plates",
                "elements": "data/test/lookup_tables/plates.txt"
            }
        ]
    }
}

Alternatively, lookup elements may be directly included as a list

{
    "rasa_nlu_data": {
        "lookup_tables": [
            {
                "name": "plates",
                "elements": ["beans", "rice", "tacos", "cheese"]
            }
        ]
    }
}

When lookup tables are supplied in training data, the contents are combined into a large, case-insensitive regex pattern that looks for exact matches in the training examples. These regexes match over multiple tokens, so lettuce wrap would match get me a lettuce wrap ASAP as [0 0 0 1 1 0]. These regexes are processed identically to the regular regex patterns directly specified in the training data.

Note

For lookup tables to be effective, there must be a few examples of matches in your training data. Otherwise the model will not learn to use the lookup table match features.

Warning

You have to be careful when you add data to the lookup table. For example if there are false positives or other noise in the table, this can hurt performance. So make sure your lookup tables contain clean data.