In this case, the data is written in Korean.

Here’s how it works: first, you download the Llama 2 model from its website or a repository like Hugging Face (which is what we did). Then, you load that model into your computer and start feeding it text to classify. But since Llama 2 was trained on general English language data, it might not do as well with Korean text. That’s where fine-tuning comes in: by training the model specifically on Korean data, we can improve its accuracy for that language.

To do this, you first need to prepare your data. This involves cleaning and preprocessing the text (removing punctuation, converting all characters to lowercase, etc.) so that it’s ready for the model to learn from. Then, you split the data into training and testing sets: the majority of the data will be used to train the model, while a smaller portion will be held out as a test set to evaluate its performance on new, unseen data.

Next, you load your prepared data into a machine learning framework like TensorFlow or PyTorch (we’re using TRL in this case) and define a training loop that iterates over the data multiple times. During each iteration, the model is fed an input (a piece of text) along with its corresponding label (the category it belongs to). The model then tries to predict what the label should be based on the input, and we calculate how close its prediction was to the actual label using a loss function like cross-entropy.

Finally, we use an optimization algorithm called DPO (Distributed Policy Optimization) to update the weights of the model’s parameters so that it can better predict labels for new text data. This involves taking small steps in parameter space based on the gradient of the loss function, which tells us how much each weight should be adjusted to improve performance.

And that’s pretty much it! After training is complete, we evaluate our model on the test set and see how well it performs compared to other models or baselines (like a random guesser). If everything goes according to plan, we can use our fine-tuned Llama 2 model for text classification in Korean with confidence.