These docs are for version 1.x of Rasa Open Source.

# Training Data Format

## Data Formats
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 text>](<entity name>)`, or by using the following syntax `[<entity-text>]{"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]{"entity": "source_account", "value": "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:additional_currencies  <!-- specify lookup tables in an external file -->
path/to/currencies.txt
```

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

- common examples
- synonyms
- regex features and
- lookup tables

While common examples is the only part that is mandatory, including the others will help the NLU model learn the domain with fewer examples and also help it be more confident of its predictions.

### 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": []
    }
}
```

### Improving Intent Classification and Entity Recognition

#### 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 \[optional\]

### Regular Expression Features
Regular expressions can be used to support the intent classification and entity extraction.

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

## regex:greet
- hey[^\\s]*
```

### Lookup Tables
Lookup tables provide a convenient way to supply a list of entity examples. The supplied lookup table files must be in a newline-delimited format. Example:

```
tacos
beef
mapo tofu
burrito
lettuce wrap
```

### Normalizing Data

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

```
## intent:search
- in the center of [NYC]{"entity": "city", "value": "New York City"}
- in the centre of [New York City](city)
```

## Note  
The common theme here is that common examples, regex features, and lookup tables merely act as cues to the final NLU model by providing additional features to the machine learning algorithm during training.