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 the following pipeline if your training data is in English:

language: "en"

The pipeline contains the [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#convertfeaturizer) that provides pre-trained word embeddings of the user utterance. Pre-trained word embeddings are helpful as they already encode some kind of linguistic knowledge.

If your training data is not in English but you still want to use pre-trained word embeddings, we recommend using the following pipeline:

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

pipeline:

• name: SpacyNLP
• name: SpacyTokenizer
• name: SpacyFeaturizer
• 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 you don’t use any pre-trained word embeddings inside your pipeline, you are not bound to a specific language and can train your model to be more domain specific.

### 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](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#components) page.

A pipeline usually consists of three main parts:

- [Tokenization](https://legacy-docs-v1.rasa.com/1.10.25/nlu/choosing-a-pipeline/#tokenization)
- [Featurization](https://legacy-docs-v1.rasa.com/1.10.25/nlu/choosing-a-pipeline/#featurization)
- [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.25/nlu/choosing-a-pipeline/#entity-recognition-intent-classification-response-selectors)

#### Tokenization

For tokenization of English input, we recommend the [ConveRTTokenizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#converttokenizer).

#### Featurization

You need to decide whether to use components that provide pre-trained word embeddings or not.

##### Pre-trained Embeddings

The advantage of using pre-trained word embeddings in your pipeline is that if you have a training example like: "I want to buy apples", and Rasa is asked to predict the intent for "get pears", your model already knows that the words "apples" and "pears" are very similar.

- [MitieFeaturizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#mitiefeaturizer)
- [SpacyFeaturizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#spacyfeaturizer)
- [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#convertfeaturizer)
- [LanguageModelFeaturizer](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/#languagemodelfeaturizer)

### Entity Recognition / Intent Classification / Response Selectors

Depending on your data you may want to only perform intent classification, entity recognition or response selection. Or you might want to combine multiple of those tasks.