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:

language: "en"

The pipeline contains the [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.18/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](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#id9)

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/1.10.18/nlu/components/#components) page.

A pipeline usually consists of three main parts:

1. [Tokenization](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#tokenization)
2. [Featurization](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#featurization)
3. [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#entity-recognition-intent-classification-response-selectors)

### [Handling Class Imbalance](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#id12)

Classification algorithms often do not perform well if there is a large class imbalance. To mitigate this problem, you can use a `balanced` batching strategy.

```yaml
language: "en"

pipeline:
# - ... other components
- name: "DIETClassifier"
  batch_strategy: sequence

Component Lifecycle

Each component processes an input and/or creates an output. The order of the components is determined by the order they are listed in the config.yml.