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/1.10.15/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/1.10.15/nlu/components/#components) page.

A pipeline usually consists of three main parts:

- [Tokenization](https://legacy-docs-v1.rasa.com/1.10.15/nlu/choosing-a-pipeline/#tokenization)

- [Featurization](https://legacy-docs-v1.rasa.com/1.10.15/nlu/choosing-a-pipeline/#featurization)

- [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.15/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/1.10.15/nlu/components/#converttokenizer). You can process other whitespace-tokenized languages with the [WhitespaceTokenizer](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#whitespacetokenizer).

#### 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.

##### 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. This is especially useful if you don’t have enough training data. We support a few components that provide pre-trained word embeddings:

1. [MitieFeaturizer](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#mitiefeaturizer)
2. [SpacyFeaturizer](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#spacyfeaturizer)
3. [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#convertfeaturizer)
4. [LanguageModelFeaturizer](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#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. For example, in general English, the word “balance” is closely related to “symmetry”, but very different to the word “cash”. In a banking domain, “balance” and “cash” are closely related and you’d like your model to capture that.

#### Entity Recognition / Intent Classification / Response Selectors  
Depending on your data you may want to only perform intent classification, entity recognition or response selection. Or you might want to combine multiple of those tasks. We support several components for each of the tasks. All of them are listed in [Components](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#components). We recommend using [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#diet-classifier) for intent classification and entity recognition and [ResponseSelector](https://legacy-docs-v1.rasa.com/1.10.15/nlu/components/#response-selector) for response selection.