![]() If intent not in ourClasses: # add unexisting tags to their respective classes ourClasses. extend(ournewTkns) # extends the tokens documentX. word_tokenize(pattern) # tokenize the patterns newWords. # Each intent is tokenized into words and the patterns and their associated tags are added to their respective lists. Lm = WordNetLemmatizer() #for getting words # lists ourClasses = The chatbot will return pre-programmed responses to answer questions: Identifying these trends can help the chatbot train itself on how people query about our chatbot’s name, allowing it to be more responsive. Every new tag would require a unique pattern. These tags include name, age, and many others. We first need a set of tags that users can use to categorize their queries. This step will create an intents JSON file that lists all the possible outcomes of user interactions with our chatbot. download( "wordnet") # word database Step two: Creating a JSON file download( "punkt") # required package for tokenization nltk. from tensorflow.keras import Sequential # Sequential groups a linear stack of layers into a tf.keras.Model from import Dense, Dropout import tensorflow as tensorF # A multidimensional array of elements is represented by this symbol. ![]() The code below shows how we import the libraries:įrom nltk.stem import WordNetLemmatizer # It has the ability to lemmatize. Sequential: Sequential groups a linear stack of layers into a tf.keras.Model.Tensorflow: A multidimensional array of elements is represented by this symbol.Random: For various distributions, this module implements pseudo-random number generators. ![]()
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