Custom NLU Components
Custom NLU Components
You can create a custom component to perform a specific task which NLU doesn’t currently offer (for example, sentiment analysis). Below is the specification of the rasa.nlu.components.Component class with the methods you’ll need to implement.
Note
There is a detailed tutorial on building custom components here.
You can add a custom component to your pipeline by adding the module path. So if you have a module called sentiment containing a SentimentAnalyzer class:
pipeline:
- name: "sentiment.SentimentAnalyzer"
Also be sure to read the section on the Component Lifecycle.
To get started, you can use this skeleton that contains the most important methods that you should implement:
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<br>import typing<br>from typing import Any, Optional, Text, Dict, List, Type<br>from rasa.nlu.components import Component<br>from rasa.nlu.config import RasaNLUModelConfig<br>from rasa.nlu.training_data import Message, TrainingData<br>if typing.TYPE_CHECKING:<br> from rasa.nlu.model import Metadata<br>class MyComponent(Component):<br> """A new component"""<br> @classmethod<br> def required_components(cls) -> List[Type[Component]]:<br> return []<br> defaults = {}<br> supported_language_list = None<br> not_supported_language_list = None<br> def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:<br> super().__init__(component_config)<br> def train(self, training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, **kwargs: Any) -> None:<br> pass<br> def process(self, message: Message, **kwargs: Any) -> None:<br> pass<br> def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:<br> pass<br> @classmethod<br> def load(cls, meta: Dict[Text, Any], model_dir: Optional[Text] = None, model_metadata: Optional["Metadata"] = None, cached_component: Optional["Component"] = None, **kwargs: Any) -> "Component":<br> if cached_component:<br> return cached_component<br> else:<br> return cls(meta)<br> |
Note
If you create a custom tokenizer you should implement the methods of rasa.nlu.tokenizers.tokenizer.Tokenizer. The train and process methods are already implemented and you simply need to overwrite the tokenize method.
Note
If you create a custom featurizer you should return a sequence of features. E.g. your featurizer should return a matrix of size (number-of-tokens x feature-dimension). The feature vector of the __CLS__ token should contain features for the complete message.
Component
class rasa.nlu.components.``Component(component_config=None)
A component is a message processing unit in a pipeline.
Components are collected sequentially in a pipeline. Each component is called one after another. This holds for initialization, training, persisting and loading the components.
classmethod required_components()
Specify which components need to be present in the pipeline.
Returns
The list of class names of required components.
classmethod required_packages()
Specify which python packages need to be installed.
classmethod create(component_config, config)
Creates this component (e.g. before training is started).
Parameters
- component_config – The components configuration parameters.
- config – The model configuration parameters.
provide_context()
Initialize this component for a new pipeline.
train(training_data, config=None, **kwargs)
Train this component.
Parameters
- training_data – The
rasa.nlu.training_data.training_data.TrainingData. - config – The model configuration parameters.
process(message, **kwargs)
Process an incoming message.
Parameters
- message – The
rasa.nlu.training_data.message.Messageto process.
persist(file_name, model_dir)
Persist this component to disk for future loading.
Parameters
- file_name – The file name of the model.
- model_dir – The directory to store the model to.
prepare_partial_processing(pipeline, context)
Sets the pipeline and context used for partial processing.
partially_process(message)
Allows the component to process messages during training (e.g. external training data).
classmethod can_handle_language(language)
Check if the component supports a specific language.