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/1.10.22/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.

### 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/1.10.22/nlu/components/#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](https://legacy-docs-v1.rasa.com/1.10.22/nlu/components/#converttokenizer). You can process other whitespace-tokenized (words are separated by spaces) languages with the [WhitespaceTokenizer](https://legacy-docs-v1.rasa.com/1.10.22/nlu/components/#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. Once you have a larger amount of data and ensure that most relevant words will be in your data and therefore will have a word embedding, supervised embeddings, which learn word meanings directly from your training data, can make your model more specific to your domain.

### 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` being more similar to `feedback+negative` than `chitchat`). To do this, you need to use the [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.22/nlu/components/#diet-classifier) 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](https://legacy-docs-v1.rasa.com/1.10.22/user-guide/testing-your-assistant/#comparing-nlu-pipelines) for more information.