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Simple Chat bot - coffee shop

using PyTorch and NLTK

The NLP preprocessing pipeline

  • Tokenize
  • lower + stem
  • exlude punctuation characters
  • generate bag of words

instruction

  1. for training the neural network: (this should be done at first time)
python train.py
  1. for chatting:
python chat.py

or you can use jupyter notebook

Comments

  • about pytorch modules
  • The Porter stemming algorithm (or ‘Porter stemmer’) is a process for removing the commoner morphological and inflexional endings from words in English. more info
  • Punkt Sentence Tokenizer: This tokenizer divides a text into a list of sentences by using an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences.
  • nltk tokenize: split sentence into array of words/tokens a token can be a word or punctuation character, or number
  • nltk stem: stemming = find the root form of the word examples:
words = ["organize", "organizes", "organizing"]
words = [stem(w) for w in words]
# return: ["organ", "organ", "organ"]
  • bag of word: return bag of words array: 1 for each known word that exists in the sentence, 0 otherwise example:
sentence = ["hello", "how", "are", "you"]
words = ["hi", "hello", "I", "you", "bye", "thank", "cool"]
bag   = [  0 ,    1 ,    0 ,   1 ,    0 ,    0 ,      0]
  • Pytorch Dataset: map-style dataset: "represents a map from (possibly non-integral) indices/keys to data samples."
  • Pytorch nn.CrossEntropyLoss:more info
  • Adam Algorithm: "for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments." more info

Libraries