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/1.10.5/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/1.10.5/nlu/components/#components) page.

A pipeline usually consists of three main parts:

- [Tokenization](https://legacy-docs-v1.rasa.com/1.10.5/nlu/choosing-a-pipeline/#tokenization)
- [Featurization](https://legacy-docs-v1.rasa.com/1.10.5/nlu/choosing-a-pipeline/#featurization)
- [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.5/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/1.10.5/nlu/components/#converttokenizer). You can process other whitespace-tokenized (words are separated by spaces) languages with the [WhitespaceTokenizer](https://legacy-docs-v1.rasa.com/1.10.5/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 can make your model more specific to your domain.

##### Pre-trained Embeddings

The advantage of using pre-trained word embeddings in your pipeline is that it allows your model to understand synonyms based on context. We support a few components that provide pre-trained word embeddings:

1. [MitieFeaturizer](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/#mitiefeaturizer)
2. [SpacyFeaturizer](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/#spacyfeaturizer)
3. [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/#convertfeaturizer)
4. [LanguageModelFeaturizer](https://legacy-docs-v1.rasa.com/1.10.5/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. We recommend using [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/#diet-classifier) for intent classification and entity recognition and [ResponseSelector](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/#response-selector) for response selection.