Training Data Format
These docs are for version 1.x of Rasa Open Source.
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Reference
Versions
viewing: 1.2.9
Warning: This document is for an old version of Rasa. The latest version is 1.10.26.
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.
## 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" -->
The training data for Rasa NLU is structured into different parts:
- common examples
- synonyms
- regex features and
- lookup tables
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.