Choosing a Pipeline

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/1.10.2/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.

If your training data is not in English, but you still want to use pre-trained word embeddings, we recommend using the following pipeline:

language: "fr" # your two-letter language code

pipeline:


## 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/1.10.2/user-guide/testing-your-assistant/#comparing-nlu-pipelines) for more information.

### 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. To mitigate this problem, you can use a `balanced` batching strategy. This algorithm ensures that all classes are represented in every batch, or at least in as many subsequent batches as possible, still mimicking the fact that some classes are more frequent than others. Balanced batching is used by default.

### 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:


### 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/1.10.2/nlu/components/#diet-classifier) in your pipeline. You’ll also need to define `intent_tokenization_flag` and `intent_split_symbol` in your tokenizer configuration.

### Summary

Understanding and using the pipeline effectively can greatly influence the performance of your Rasa assistant. By customizing each component, you can ensure that your assistant performs optimally for the tasks you need it to handle.