This method is more keyword based and doesn’t require the machine to learn context Elasticsearch (basically TF-IDF) - choose this option if you don’t have much data.The post will split later on after the general preparation steps. I’ll outline two ways, and feel free to try both however, which you choose depends on the amount of text message data you have. So there’s plenty of easy ways to make a useless chatbot, but which way you should do it depends on your data. With that in mind, let’s start to dive in. It isn’t safe and it’s a dick move to your friends. The model(s) we’ll use will forever have access to messages from your friends, drunk nights out, lovers, hitmen, parents, drug dealers etc… Don’t post your model(s), data or sensitive results anywhere so that someone can access your info. *IMPORTANT* - Doing analysis on your texts is fun, but be conscious of the information in there. If you’re just looking to screw around for kicks, keep reading! If you’re looking to build a business oriented chatbot from scratch to provide good information and answer questions, try something like Rasa NLU. More complex and advanced chatbots likely have an architecture that involves entity recognition, intent recognition, some kind of tailored response database, some LSTMs sprinkled on there etc…but I’m not going over any of that.
#SEXTING CHATBOT HOW TO#
Well, as humans we generate training data all the time - and what better training data for a conversational bot than your own text messages? In this post, I’ll show you how to build a very simple, easy and underachieving chatbot using python and your own text messages.
Especially for tasks like natural language processing (NLP), lots of data is required for machines to learn good word context, labels or general “understanding”. Having reliable “ground truth” examples is what fuels good machine learning. For those of you familiar with data science, one of the biggest challenges in the field is acquiring training data that doesn’t suck.
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