# Training Data Format

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.

## 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>"}`. The keywords `role`, `group`, and `value` are optional in this notation.

```
## 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
```

### 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`.

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

### Improving Intent Classification and Entity Recognition
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 range to apply to the string.

```
## intent:restaurant_search
- show me [chinese](cuisine) restaurants
```

### 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.

```
## lookup:plates
data/test/lookup_tables/plates.txt

## intent:food_request
- I'd like beef [tacos](plates) and a [burrito](plates)
- How about some [mapo tofu](plates)
```

### Normalizing Data
#### Entity Synonyms
If you define entities as having the same value they will be treated as synonyms.

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

### Note
To use the synonyms defined in your training data, make sure the pipeline contains the `EntitySynonymMapper` component.

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