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More parameters will allow the model to pick up more complexity from the dataset. Model size here refers to the number of parameters in the model.

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Want an even smarter and more eloquent model? Feel free to train a larger model like DialoGPT-medium or even DialoGPT-large. The model will be stored in a folder named output-small. I have about 700 lines and the training takes less than ten minutes. Running through the training section of the notebook should take less than half an hour.

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Change the dataset and the target character in code snippets like:.

Select GPU as the runtime, which will speed up our model training.

All you need to do is the following: (please refer to the video for a detailed walkthrough) My GitHub repo for this tutorial contains the notebook file named model_train_upload_workflow.ipynb to get you started. Instead of training from scratch, we will load Microsoft's pre-trained GPT, DialoGPT-small, and fine-tune it using our dataset. Under the hood, our model will be a Generative Pre-trained Transfomer (GPT), the most popular language model these days.

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Try it out on this Python regex website yourself! How to Train the Model Using a regular expression like (+): (.+), we can extract out the two columns of interest, character name, and dialogue line. For example, check out this Peppa Pig transcript. A great place to look for transcripts is Transcript Wiki. We can create datasets from raw transcripts.

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Example dataset: Harry Potter movie transcript Can't Find Your Favorite Character on Kaggle?Ĭan't find your favorite character on Kaggle? No worries. We only need two columns from these datasets: character name and dialogue line.

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There are a lot of interesting datasets on Kaggle for popular cartoons, TV shows, and other media. This is essentially how our chatbot is going to respond to different exchanges and contexts.

How to Prepare the Dataįor our chatbot to learn to converse, we need text data in the form of dialogues. To learn more about how to build Discord bots, you may also find these two freeCodeCamp posts useful – there's a Python version and a JavaScript version.

Keep the bot running indefinitely with Uptime Robot.

Set up the Discord bot's permissions so they don't spam non-bot channels.

Build a Discord bot in either Python or JavaScript, your choice! 🤩.

Deploy the model to Hugging Face, an AI model hosting service.

Train the model in Google Colab, a cloud-based Jupyter Notebook environment with free GPUs.

Gather text data for your character using one of these two methods: find pre-made datasets on Kaggle or make custom datasets from raw transcripts.

The video version of this tutorial runs for a total of one hour and features the following topics: Other updates in this tutorial address changes in Hugging Face's model hosting services, including API changes that affect how we push the model to Hugging Face's model repositories. You may also create a custom dataset that captures the speech between you and your friends and build a chatbot that speaks like yourself! Since a lot of people are interested in building their own bots based on their favorite characters, I updated my tutorial to include an in-depth explanation on how to gather text data for any character, fictional or non-fictional. 😳 You can read more about my deployment post-mortem in this post.

I'm honestly surprised by how popular it became – there were 5.9k views of my previous tutorial, plus, when I deployed my bot to a 1k+ user server, people flooded it with 300+ messages in an hour, effectively crashing the bot. My chatbot project started as a joke with a friend when we were playing video games. Here is an example of the Discord AI chatbot that we will have built by the end of this tutorial. This can be a huge help in improving the efficiency of your customer services team, ensuring that your staff are dealing with the more relevant and potentially lucrative conversations, customers and leads while smaller, simpler enquiries are handled by the chatbot.If you want, you can dive right into my video tutorial on YouTube – or read on for more details. This means that a chatbot can help to take some of the strain from a customer services team, automating simpler interactions, with Flow XO allowing human agents to step in if necessary before handing back over to the chatbot to complete the conversation. Instead, a chatbot uses the workflows you set up to understand and respond to customers, putting the information they need directly in front of them as quickly as possible. Flow XO is the perfect toolset for any business that wants to ensure their interactions with their customers are as efficient, effective and intelligent as possible.Ī chatbot provides a means for a customer to communicate with a business in a fast and reactive way, avoiding extensive email chains, phone calls and enquiry forms.