# 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:

```yaml
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:

```yaml
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:

```yaml
language: "en"

This pipeline contains the [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.9.1/nlu/components/#convertfeaturizer) that provides pre-trained word embeddings of the user utterance.

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

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

pipeline:
  - name: SpacyNLP
  - name: SpacyTokenizer
  - name: SpacyFeaturizer
  - 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. 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](https://legacy-docs-v1.rasa.com/1.9.1/nlu/components/#converttokenizer).

### Featurization

You need to decide whether to use components that provide pre-trained word embeddings or not. We recommend starting with pre-trained word embeddings in cases of small amounts of training data.

### Entity Recognition / Intent Classification / Response Selectors

Depending on your data, you may want to combine multiple tasks, using components like [DIETClassifier](https://legacy-docs-v1.rasa.com/1.9.1/nlu/components/#diet-classifier) for intent classification and entity recognition.