# Training 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.

### Data Formats

You can provide training data in the following formats:

- **Markdown Format**: It's easy to read and write for humans.
- **JSON Format**: A structured format which consists of a top-level object called `rasa_nlu_data`, with the keys `common_examples`, `entity_synonyms` and `regex_features`.

```markdown
## 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:additional_currencies  <!-- specify lookup tables in an external file -->
path/to/currencies.txt
```

### 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 indicate the range in the string.

### Regular Expression Features

Regular expressions can enhance intent classification and entity extraction. For example:

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

### Lookup Tables

Lookup tables provide a convenient way to supply a list of entity examples. These must be provided in a newline-delimited format.

Example content for a lookup table:
```
tacos
beef
mapo tofu
burrito
lettuce wrap
```

### Normalizing Data

Entity synonyms can be defined as being equivalent. For example:

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

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