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

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

#### [Tokenization](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id15)
For tokenization of English input, we recommend the [ConveRTTokenizer](https://legacy-docs-v1.rasa.com/nlu/components/#converttokenizer).

### [Featurization](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id16)
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.

##### [Pre-trained Embeddings](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id18)
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. This is especially useful if you don’t have enough training data.

##### [Supervised Embeddings](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id19)
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.

### [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id17)
Depending on your data you may want to only perform intent classification, entity recognition or response selection. We support several components for each of these tasks.

### [Multi-Intent Classification](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id10)
You can use Rasa Open Source components to split intents into multiple labels.

## [Comparing Pipelines](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id11)
Rasa gives you the tools to compare the performance of multiple pipelines on your data directly.

## [Handling Class Imbalance](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id12)
Classification algorithms often do not perform well if there is a large class imbalance, for example if you have a lot of training data for some intents and very little training data for others.

## [Component Lifecycle](https://legacy-docs-v1.rasa.com/nlu/choosing-a-pipeline/#id13)
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`; the output of a component can be used by any other component that comes after it in the pipeline.

👋 I can help you get started with Rasa and answer your technical questions.