ChatBot using Python
This is a ChatBot using Python. Using some basic Natural Language Processing (NLP) techniques.
>>> Let's chat! (type 'quit' to exit)
>>> You: Hey
>>> Segredo: Hello, thanks for visiting
>>> You: It's time to me to go.
>>> Segredo: OK, it's was a pleasure to meet you, bye!
- NLP Basics: Tokenization, Stemming, Bag Of Words
- How to preprocess the data with
nltkto feed it to your neural net - How to implement the feed-forward neural net in Pytorch and train it
- The implementation should be easy to follow for beginners and provide a basic understanding of chatbots
- The implementation is straightforward with a Feed Forward Neural net with 2 hidden layers.
- Customization for your own use case is super easy. Just modify
intents.jsonwith possible pattern and responses and re-run the training.
First and foremost, you need to have Python installed and pip. After that you need to install PyTorch and dependencies.
pip install pytorch
After installing pytorch you need to install nltk and download it.
pip install nltk
To download it after nltk you need to open python command line:
$ python
>>> import nltk
>>> nltk.download('punkt')
After this your project will work correctly.
How this software works ? It's quite simple, you change the intents.json file the way you want (don't change the layout), and after that you train your BOT.
$ python train.py
After training your bot, you can start the chat, have some fun with it.
$ python chat.py
As I mentioned before, you can customize it for your own need. Just modify the intents.json with possible patterns and responses and re-run the training.
In Natural Language Processing (NLP), a bag of words (BoW) is a method for representing text data as a collection of word counts, disregarding the order and structure of the words in the text. The idea behind BoW is to treat a document as a "bag" of its constituent words, ignoring grammar and word order but keeping track of the frequency of each word.
Tokenization is a fundamental technique in Natural Language Processing (NLP) that involves breaking down a stream of text into smaller, meaningful units called tokens. These tokens can be words, phrases, or even individual characters, depending on the level of granularity needed for the analysis.
Stemming is a process in Natural Language Processing (NLP) that involves reducing a word to its base or root form, called a stem, by removing its affixes (suffixes or prefixes). The goal of stemming is to reduce different forms of a word to a common base form, so that they can be treated as the same word during analysis.
Octávio Marques (Segredo)