Training Data Format

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

User Guide

NLU

Core

Conversation Design

API Reference

Migrate from (beta)

Reference

Glossary

Versions

viewing: 1.10.8

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

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.

## intent:check_balance
- what is my balance
- how much do I have on my [savings](source_account)

## intent:greet
- hey
- hello

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]

Notes

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. Therefore, it must not be assumed that having a single example would be enough for the model to robustly identify intents and/or entities across all variants of that example.

Images