Choosing a Pipeline

In Rasa Open Source, incoming messages are processed by a sequence of components. These components are executed one after another in a so-called processing pipeline defined in your config.yml. Choosing an NLU pipeline allows you to customize your model and finetune it on your dataset.

How to Choose a Pipeline ¶

The Short Answer ¶

If your training data is in English, a good starting point is the following pipeline:

language: "en"

pipeline:
  - name: ConveRTTokenizer
  - name: ConveRTFeaturizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100

If your training data is not in English, start with the following pipeline:

language: "fr"  # your two-letter language code

pipeline:
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100

A Longer Answer ¶

We recommend using following pipeline, if your training data is in English:

The pipeline contains the ConveRTFeaturizer that provides pre-trained word embeddings of the user utterance.

Choosing the Right Components

There are components for entity extraction, for intent classification, response selection, pre-processing, and others. You can learn more about any specific component on the Components page.

A pipeline usually consists of three main parts:

• Tokenization
• Featurization
• Entity Recognition / Intent Classification / Response Selectors

Tokenization

For tokenization of English input, we recommend the ConveRTTokenizer. You can process other whitespace-tokenized (words are separated by spaces) languages with the WhitespaceTokenizer.

Featurization

You need to decide whether to use components that provide pre-trained word embeddings or not. We recommend in cases of small amounts of training data to start with pre-trained word embeddings.

Entity Recognition / Intent Classification / Response Selectors

Depending on your data you may want to only perform intent classification, entity recognition or response selection. We support several components for each of the tasks. All of them are listed in Components.

Multi-Intent Classification

You can use Rasa Open Source components to split intents into multiple labels. For example, you can predict multiple intents (thank+goodbye) or model hierarchical intent structure (feedback+positive). To do this, you need to use the DIETClassifier in your pipeline.

Comparing Pipelines

Rasa gives you the tools to compare the performance of multiple pipelines on your data directly. See Comparing NLU Pipelines for more information.