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/1.10.6/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. For example, if you have a sentence like “I want to buy apples” in your training data, 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.

### 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/1.10.6/nlu/components/#components) page. If you want to add your own component, for example to run a spell-check or to do sentiment analysis, check out [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.10.6/api/custom-nlu-components/#custom-nlu-components).

A pipeline usually consists of three main parts:
- [Tokenization](https://legacy-docs-v1.rasa.com/1.10.6/nlu/choosing-a-pipeline/#tokenization)
- [Featurization](https://legacy-docs-v1.rasa.com/1.10.6/nlu/choosing-a-pipeline/#featurization)
- [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.6/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/1.10.6/nlu/components/#converttokenizer). You can process other whitespace-tokenized (words are separated by spaces) languages with the [WhitespaceTokenizer](https://legacy-docs-v1.rasa.com/1.10.6/nlu/components/#whitespacetokenizer). If your language is not whitespace-tokenized, you should use a different tokenizer. We support a number of different [tokenizers](https://legacy-docs-v1.rasa.com/1.10.6/nlu/components/#tokenizers), or you can create your own [custom tokenizer](https://legacy-docs-v1.rasa.com/1.10.6/api/custom-nlu-components/#custom-nlu-components).

#### 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. If you can’t find a pre-trained model for your language, you should use supervised embeddings.

### 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/1.10.6/nlu/components/#diet-classifier) in your pipeline. You’ll also need to define these flags in whichever tokenizer you are using:

- `intent_tokenization_flag`: Set it to `True`, so that intent labels are tokenized.
- `intent_split_symbol`: Set it to the delimiter string that splits the intent labels. In this case `+`, default `_`.

### 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/1.10.6/user-guide/testing-your-assistant/#comparing-nlu-pipelines) for more information.

Note: Intent classification is independent of entity extraction. So sometimes NLU will get the intent right but entities wrong, or the other way around. You need to provide enough data for both intents and entities.