There are quite a few APIs that allow fine-tuning on large language models like GPT-3. You may want to have a go training your own model on WhatsApp chats. Generally, this is a bit awkward since fine tuning typically can only be done on data structured in a prompt-response fashion. This snippet of code helps you do this!
Turn the output
[09/11/2020, 12:14:48] A: Text 1
[09/11/2020, 12:15:04] A: Text 2
[09/11/2020, 12:41:26] B: Text 3
[09/11/2020, 12:41:26] A: Text 4
Into a csv with prompt and completion columns
,prompt,completion
0,Text 1. Text 2, Text 3
1, Text 4, NaN
Fine tuning can then be performed easily on something like openai's data preparation api.
openai tools fine_tunes.prepare_data -f output.csv
https://beta.openai.com/docs/guides/fine-tuning
Git clone this repo
git clone https://github.com/afiqhatta/chat_scrape.git
Set up a virtual environment, and then run the python script with the following command line arguments.
virtualenv env
pip install -r requirements.txt
source env/bin/activate
python wa_parser.py <EXPORTED_TEXT> <PROMPTER> <RESPONDER>
Export and download the WhatsApp chat of your choice
to a txt file.
The start of your WhatsApp export probably looks like this, in chat.txt.
[07/11/2020, 11:09:28] Alice Smith: Are you ok?
[07/11/2020, 11:09:32] Bob Jones: Hey there, yes I am!
To parse this, use
python wa_parser.py chat.txt 'Alice Smith' 'Bob Jones'
The original intention of creating this module was to create a service where people can parse chats through an api. Currently, this is run in production on a Google compute engine instance. To deploy this to google cloud, after creating the project, you can run
gcloud app deploy