|What is my nationaly:||Nicaraguan|
|I prefer to listen:||Hip hop|
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in. The goal of this guide is to build a system capable of chatting like you, using your own WhatsApp and Telegram chats as an ML dataset. What we will do could be summarized in the following steps:. Do you want to see some ?
Bla - private chat and dating
First of all, we n eed to gather the data from our chat applications. We will now learn how to export data from two of the most commonly used instant messaging apps: WhatsApp and Telegram. We have to export one. So, as described on the official Bla chat website :. Note that only 1 to 1 chats are allowed bla chat individualwe suggest to export chats with the highest of messages, in order to achieve a bigger dataset and get better final.
Now you should have more files, each with a structure that looks like the snippet below:. This parameter is your name for the WhatsApp app and we will use this value later. The process here will be faster than WhatsApp because Telegram will export everything in a single.
Online conversation mobile chat app sending receiving messages with bla bla bla sticker messenger application communication social media concept smartphone screen flat
So, as described on the official Telegram website :. To train a GPT-2 neural network, first of all we need to pre-process the data, in order to obtain a single. For the sake of simplicity and since the ML model we will use requires a GPU to work, we are going to use Google Bla chat for the next step. Open this Colab notebook and follow these bla chat.
To work with the data, we need to them on Colab, into the right folders. WhatsApp chats Select all your. You can also change the date format parsing system if some of the exported data show a different format due to local time formatting. A progress bar will be shown, and the training could take up to 10 hours, it depends mostly on which GPU type Colab is running and how many much messages were provided.
Wait until the process ends. So write your message and then press enter.
After the first message is sent, the system will prompt some information about the bla chat. You will now see the most interesting as a list of messages:. After the replied message is generated, you could continue to chat for a total of 5 messages. After this, you could re-run the cell to start a new conversation with the model.
So in this guide we have seen how simple it is to train your GPT-2 model from scratch, the task is simple but not trivial!
Note that if your chat messages are in English you could easily obtain better than the ones we got with this standard approach, since you could use the transfer learning from a GPT-2 pretrained bla chat. The pistoBot repository allows you to train or fine-tune different models, including the chance to start from a GPT-2 pretrained model: check t he repository folder for more information.
We have chosen the standard, un-trained GPT-2 model so that even the non-english users could use this AI. Based in Bologna, I have intense curiosity about numerous unrelated subjects.
How to create an ai that chats like you
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How to create an AI that chats like you. Simone Guardati.
Get the data First of all, we n eed to gather the data from our chat applications. So, as described on the official WhatsApp website : Open your WhatsApp mobile app Pick an individual chat one individual chat e. Gmail app and add your e-mail address as a recipient Bla chat to receive the mail with chat history as txt file attachment Download and store the txt attachment on the computer Repeat those steps for every individual chat you want to include Note that only 1 to 1 bla chat are allowed namely individualwe suggest to export chats with the highest of messages, in order to achieve a bigger dataset and get better final.
Parse the data To train a GPT-2 neural network, first of all we need to pre-process the data, in order to obtain a single. Conclusion So in this guide we have seen how simple it is to train your GPT-2 model from scratch, the task is simple but not trivial! Thanks to Stefano Romanazzi and Elliot Gunn.
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