# Choosing a Pipeline

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

## [The Short Answer](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id7)  
If your training data is in english, a good starting point is using `pretrained_embeddings_convert` pipeline.

```
language: "en"

pipeline: "pretrained_embeddings_convert"
```

In case your training data is multi-lingual and is rich with domain specific vocabulary, use the `supervised_embeddings` pipeline:

```
language: "en"

pipeline: "supervised_embeddings"
```

## [A Longer Answer](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id8)  
The three most important pipelines are `supervised_embeddings`, `pretrained_embeddings_convert` and `pretrained_embeddings_spacy`. The `pretrained_embeddings_spacy` pipeline uses pre-trained word vectors from either GloVe or fastText, whereas `pretrained_embeddings_convert` uses a pretrained sentence encoding model [ConveRT](https://github.com/PolyAI-LDN/polyai-models) to extract vector representations of complete user utterance as a whole. On the other hand, the `supervised_embeddings` pipeline doesn’t use any pre-trained word vectors or sentence vectors, but instead fits these specifically for your dataset.

Note

These recommendations are highly dependent on your dataset and hence approximate. We suggest experimenting with different pipelines to train the best model.

### [pretrained_embeddings_spacy](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id9)  
The advantage of `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 large enough training data.

### [pretrained_embeddings_convert](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id10)  
> Warning  
> Since `ConveRT` model is trained only on an **English** corpus of conversations, this pipeline should only be used if your training data is in English language.

This pipeline uses [ConveRT](https://github.com/PolyAI-LDN/polyai-models) model to extract vector representation of a sentence and feeds them to `EmbeddingIntentClassifier` for intent classification. The advantage of using `pretrained_embeddings_convert` pipeline is that it doesn’t treat each word of the user message independently, but creates a contextual vector representation for the complete sentence.

### [supervised_embeddings](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id11)  
The advantage of the `supervised_embeddings` pipeline is that your word vectors will be customised 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](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id12)  
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](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id13)  
Rasa gives you the tools to compare the performance of both of these pipelines on your data directly.

## [Class imbalance](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id14)  
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](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id15)  
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](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id16)  
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. There are components for entity extraction, for intent classification, response selection, pre-processing, and others.

## [Component Lifecycle](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id17)  
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](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id18)  
After parsing, the entity is returned as a dictionary. There are two fields that show information about how the pipeline impacted the entities returned: the `extractor` field of an entity tells you which entity extractor found this particular entity.

## [Pre-configured Pipelines](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id19)  
A template is just a shortcut for a full list of components.

### [supervised_embeddings](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id20)  
To train a Rasa model in your preferred language, define the `supervised_embeddings` pipeline as your pipeline in your `config.yml` or other configuration file:

```
language: "en"

pipeline: "supervised_embeddings"
```

### [pretrained_embeddings_convert](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id21)  
To use the `pretrained_embeddings_convert` template:

```
language: "en"

pipeline: "pretrained_embeddings_convert"
```

### [pretrained_embeddings_spacy](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id22)  
To use the `pretrained_embeddings_spacy` template:

```
language: "en"

pipeline: "pretrained_embeddings_spacy"
```

### [MITIE](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id23)  
To use the MITIE pipeline, you will have to train word vectors from a corpus.

## [Custom pipelines](https://legacy-docs-v1.rasa.com/1.7.2/nlu/choosing-a-pipeline/#id24)  
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:

```
pipeline:
- name: "SpacyNLP"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"
```