Hi @tonysinghmss. Do you want to correct the entire input message before it’s tokenised? In that case the custom component implementation would be a bit simpler. I quickly put together an example with autocorrect:
from rasa.nlu.components import Component
import typing
from typing import Any, Optional, Text, Dict
if typing.TYPE_CHECKING:
from rasa.nlu.model import Metadata
class SpellChecker(Component):
"""A new component"""
# Defines what attributes the pipeline component will
# provide when called. The listed attributes
# should be set by the component on the message object
# during test and train, e.g.
# ```message.set("entities", [...])```
provides = ['text'] #set as text but shouldn't really have much influence on the component
# Which attributes on a message are required by this
# component. e.g. if requires contains "tokens", than a
# previous component in the pipeline needs to have "tokens"
# within the above described `provides` property.
requires = []
# Defines the default configuration parameters of a component
# these values can be overwritten in the pipeline configuration
# of the model. The component should choose sensible defaults
# and should be able to create reasonable results with the defaults.
defaults = {}
# Defines what language(s) this component can handle.
# This attribute is designed for instance method: `can_handle_language`.
# Default value is None which means it can handle all languages.
# This is an important feature for backwards compatibility of components.
language_list = None
def __init__(self, component_config=None):
super(SpellChecker, self).__init__(component_config)
def train(self, training_data, cfg, **kwargs):
"""Train this component.
This is the components chance to train itself provided
with the training data. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`components.Component.pipeline_init`
of ANY component and
on any context attributes created by a call to
:meth:`components.Component.train`
of components previous to this one."""
pass
def process(self, message, **kwargs):
"""Process an incoming message.
This is the components chance to process an incoming
message. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`components.Component.pipeline_init`
of ANY component and
on any context attributes created by a call to
:meth:`components.Component.process`
of components previous to this one."""
from autocorrect import spell
mesg = message.text #get original message
text = spell(mesg) #correct the message with autocorrect
message.text = text #set the corrected message as the message for the next components to process
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this component to disk for future loading."""
pass
@classmethod
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":
"""Load this component from file."""
if cached_component:
return cached_component
else:
return cls(meta)
In my pipeline configuration this component then comes first. For example:
language: en
pipeline:
- name: spellcheck.SpellChecker
- name: "WhitespaceTokenizer"
- name: "RegexFeaturizer"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"
intent_tokenization_flag: true
intent_split_symbol: "+"
I hope it helps. Let me know if you have any issues with the implementation