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As Chatter Bot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase.
from chatterbot.trainers import Chatter Bot Corpus Trainer # Create a new trainer for the chatbot trainer = Chatter Bot Corpus Trainer(chatbot) # Train based on the english corpus trainer.train("chatterbot.corpus.english") # Train based on english greetings corpus trainer.train("chatterbot.corpus.english.greetings") # Train based on the english conversations corpus trainer.train("chatterbot.corpus.english.conversations") For examples, see the examples directory in this project's git repository.by responding to any input that contains the word 'MOTHER' with 'TELL ME MORE ABOUT YOUR FAMILY'). Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database.Thus an illusion of understanding is generated, even though the processing involved has been merely superficial. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held.An example of typical input would be something like this: An untrained instance of Chatter Bot starts off with no knowledge of how to communicate.Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to.
Chatter Bot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on collections of known conversations.