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

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 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.

Featurization

You need to decide whether to use components that provide pre-trained word embeddings or not.

Pre-trained Embeddings

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. We support a few components that provide pre-trained word embeddings:

1. MitieFeaturizer
2. SpacyFeaturizer
3. ConveRTFeaturizer
4. LanguageModelFeaturizer

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

Depending on your data you may want to only perform intent classification, entity recognition or response selection.

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 in your pipeline.

Comparing Pipelines

Rasa gives you the tools to compare the performance of multiple pipelines on your data directly.

Handling Class Imbalance

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

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.

For example, for the sentence "I am looking for Chinese food", the output is:

{
    "text": "I am looking for Chinese food",
    "entities": [\
        {\
            "start": 8,\
            "end": 15,\
            "value": "chinese",\
            "entity": "cuisine",\
            "extractor": "DIETClassifier",\
            "confidence": 0.864\
        }\
    ],
    "intent": {"confidence": 0.6485910906220309, "name": "restaurant_search"},
    "intent_ranking": [\
        {"confidence": 0.6485910906220309, "name": "restaurant_search"},\
        {"confidence": 0.1416153159565678, "name": "affirm"}\
    ]
}

This is created as a combination of the results of the different components in the following pipeline:

pipeline:
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
  - name: DIETClassifier
  - name: EntitySynonymMapper
  - name: ResponseSelector