# 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](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id6)

### [The Short Answer](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id7)

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](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id8)

We recommend using following pipeline, if your training data is in English:

```yaml
language: "en"

The pipeline contains the [ConveRTFeaturizer](https://legacy-docs-v1.rasa.com/1.10.23/nlu/components/#convertfeaturizer) that provides pre-trained word embeddings of the user utterance.

### [Choosing the Right Components](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id9)

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/1.10.23/nlu/components/#components) page.

A pipeline usually consists of three main parts:

- [Tokenization](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#tokenization)
- [Featurization](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#featurization)
- [Entity Recognition / Intent Classification / Response Selectors](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#entity-recognition-intent-classification-response-selectors)

#### [Tokenization](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id15)

For tokenization of English input, we recommend the [ConveRTTokenizer](https://legacy-docs-v1.rasa.com/1.10.23/nlu/components/#converttokenizer).

#### [Featurization](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id16)

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.

##### [Pre-trained Embeddings](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id18)

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.

##### [Supervised Embeddings](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id19)

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.

### [Multi-Intent Classification](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id10)

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`).

```yaml
language: "en"

pipeline:
- name: "WhitespaceTokenizer"
  intent_tokenization_flag: True
  intent_split_symbol: "_"
- name: "CountVectorsFeaturizer"
- name: "DIETClassifier"
```

### [Handling Class Imbalance](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id12)

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.

```
language: "en"

pipeline:
# - ... other components
- name: "DIETClassifier"
  batch_strategy: sequence
```

### [Component Lifecycle](https://legacy-docs-v1.rasa.com/1.10.23/nlu/choosing-a-pipeline/#id13)

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`.

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

```json
{
    "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"}
    ]
}
```