I have by far too many documents to remember what to use when.
So, hoping it can make my resources more usable, I asked chatGPT
"how to fine-tune you on my data?"

This was its answer:
"Fine-tuning a language model involves training the model on a new dataset that is similar to the task or domain the model will be used for. This can be done by using the pre-trained model as a starting point and further training it on your own data
using supervised learning.
To fine-tune a language model using your own data, you will need to do the following:
1. Preprocess your data: You will need to clean and prepare your data for training. This may involve tokenizing the text, creating a vocabulary, and converting the data into a format that can be used by the model.
2. Choose a pre-trained language model: You will need to select a pre-trained language model that is appropriate for your task. There are many pre-trained models available, including BERT, GPT-2, and RoBERTa.
3. Fine-tune the model: You can fine-tune the model by using your own data to update the model's weights and biases. This is typically done using an optimization algorithm, such as stochastic gradient descent (SGD), and a loss function that measures the
model's performance on the task.
4. Evaluate the model: Once you have fine-tuned the model, you will need to evaluate its performance on your own data to determine how well it is able to complete the task.
Fine-tuning a language model can be a useful way to improve its performance on a specific task, but it requires a large amount of training data and can be computationally intensive. Do you have any other questions about fine-tuning a language model?"




Gianluca Miscione
University College Dublin
https://people.ucd.ie/gianluca.miscione

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