Choosing a Pipeline

Choosing an NLU pipeline allows you to customize your model and finetune it on your dataset.

The Short Answer

If you have less than 1000 total training examples, and there is a spaCy model for your language, use the pretrained_embeddings_spacy pipeline:

language: "en"

pipeline: "pretrained_embeddings_spacy"

If you have 1000 or more labelled utterances, use the supervised_embeddings pipeline:

language: "en"

pipeline: "supervised_embeddings"

A Longer Answer

The two most important pipelines are supervised_embeddings and pretrained_embeddings_spacy. The biggest difference between them is that the pretrained_embeddings_spacy pipeline uses pre-trained word vectors from either GloVe or fastText. The supervised_embeddings pipeline, on the other hand, doesn’t use any pre-trained word vectors, but instead fits these specifically for your dataset.

pretrained_embeddings_spacy

The advantage of the pretrained_embeddings_spacy 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 very much training data.

supervised_embeddings

The advantage of the supervised_embeddings pipeline is that your word vectors will be customized for your domain. 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. This pipeline doesn’t use a language-specific model, so it will work with any language that you can tokenize (on whitespace or using a custom tokenizer).

MITIE

You can also use MITIE as a source of word vectors in your pipeline. The MITIE backend performs well for small datasets, but training can take very long if you have more than a couple of hundred examples. However, we do not recommend that you use it as mitie support is likely to be deprecated in a future release.

Comparing different pipelines for your data

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

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, rasa’s supervised_embeddings pipeline uses a balanced batching strategy.

language: "en"

pipeline:
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"
  batch_strategy: sequence

Multiple Intents

If you want to split intents into multiple labels, e.g. for predicting multiple intents or for modeling hierarchical intent structure, you can only do this with the supervised embeddings pipeline.

Understanding the Rasa NLU Pipeline

In Rasa NLU, incoming messages are processed by a sequence of components. These components are executed one after another in a so-called processing pipeline. Each component processes the input and creates an output. The output can be used by any component that comes after this component in the pipeline.

Component Lifecycle

Every component can implement several methods from the Component base class. In a pipeline, these different methods will be called in a specific order.

The “entity” object explained

After parsing, the entity is returned as a dictionary.

Pre-configured Pipelines

A template is just a shortcut for a full list of components.

Custom pipelines

You don’t have to use a template; you can also run a fully custom pipeline by listing the names of the components you want to use.