# 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>"}}`. Using the latter syntax, you can also assign synonyms, roles, or groups to an entity, e.g.
`[<entity-text>]{{"entity": "<entity name>", "role": "<role name>", "group": "<group name>", "value": "<entity synonym>"}}`.
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
```

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": []
    }
}
```

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.

### 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]

Entities are specified with a `start` and an `end` value, which together make a range to apply to the string, e.g. in the example below, with `text="show me chinese restaurants"`, then `text[8:15] == 'chinese'`.

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

### Regular Expression Features

Regular expressions can be used to support the intent classification and entity extraction. For example, if your entity has a deterministic structure (like a zipcode or an email address), you can use a regular expression to ease detection of that entity.

```
## 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. For example, `data/test/lookup_tables/plates.txt` may contain:

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

And can be loaded and used as shown here:

```
## 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. 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)
```

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

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

```
## synonym:New York City
- NYC
- nyc
- the big apple
```

> **Note**: 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.**

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