Welcome back to our journey towards mastering the fine tuning of large language models in PyTorch! In the previous chapter, we discussed the importance of choosing the appropriate pretrained model for your specific natural language processing task. Now, we will delve into the process of evaluating the performance of your fine tuned model.
Evaluating model performance is a crucial step in ensuring the model is accurate, efficient and effective for the desired task. To help us better understand the importance of evaluating model performance, we have a special guest, Thomas G. Dietterich, Professor Emeritus at Oregon State University and the co-founder and president of BigML. Prof. Dietterich is a renowned expert in the field of machine learning and has published numerous articles on the topic, including "10 Safer Ways to Update a Machine Learning System" [1].
Prof. Dietterich emphasizes the importance of testing your model on both your training data and unseen test data, also known as validation data. "Many people fall into the trap of overfitting, where the model performs well on the training data but is not able to generalize to new data", he says. "Evaluating on test data helps to ensure that the model is not overfit and to accurately measure its performance."
There are many evaluation metrics that can be used to assess the performance of a language model, including accuracy, precision, recall, F1 score, perplexity and more. Depending on your specific task, some metrics may be more relevant than others. One commonly used metric for language modeling tasks is the perplexity score, which represents how well the model can predict the next word in a sentence [2].
To evaluate the performance of your fine tuned model, you can use PyTorch's built-in evaluation functions, such as torch.nn.functional.cross_entropy() or torch.nn.functional.nll_loss(). These functions can calculate the loss or error rate of your model on a given dataset. You can also use third-party libraries such as Hugging Face's Transformers [3], which include pre-built evaluation functions for language modeling tasks.
In the next section, we will walk you through the process of evaluating your fine tuned model on both the training and validation data. Prof. Dietterich will provide valuable insights on best practices for evaluating model performance and avoiding overfitting. So, let's get started!
References: [1] Dietterich, T. G. (2000). "10 Safer Ways to Update a Machine Learning System." Retrieved from http://web.cs.iastate.edu/~honavar/safe-updates.pdf [2] Bengio, Y., et al. (2003). "A Neural Probabilistic Language Model." Journal of Machine Learning Research, Vol. 3, pp. 1137-1155. [3] Hugging Face (2021). "Transformers: State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0." Retrieved from https://huggingface.co/transformers/
Once upon a time, in the land of Oz, there was a scarecrow who desperately wanted to speak. The scarecrow had been given a voice through a fine tuned language model in PyTorch, but he wanted to ensure that his voice was accurate and could be understood by all. So, the scarecrow decided to seek the help of the Great Wizard of Oz, who was known to have the power to evaluate any language model.
The scarecrow and his friend, a young girl named Dorothy, set off on the yellow brick road towards the Emerald City to meet the Great Wizard of Oz. Along the way, they encountered special guest, Professor Thomas G. Dietterich, who joined them on their journey.
As they walked, the scarecrow and Dorothy shared their concerns with Prof. Dietterich about the accuracy of the scarecrow's speech. Prof. Dietterich listened attentively and shared his expertise on evaluating language models. "It's important to evaluate the model on both the training data and test data to ensure that the model performs well on new and unseen data," Prof. Dietterich said.
When the group finally arrived at the Emerald City, they presented the scarecrow's fine tuned language model to the Great Wizard of Oz for evaluation. The Great Wizard of Oz tested the model on both the training and test data and assessed its accuracy through various evaluation metrics, including perplexity.
After a few tweaks and adjustments, the Great Wizard of Oz gave the scarecrow's speech a stamp of approval, and the scarecrow finally had the confidence to speak to all who passed through Oz.
The resolution to this story is that evaluating the performance of a fine tuned language model requires testing the model on both the training and test data, ensuring that the model is not overfit and can generalize well to new data. By considering various evaluation metrics, such as perplexity, and optimizing the model through testing and evaluation, we can ensure that the model performs accurately for the desired task.
We hope you've enjoyed this parable and learned the importance of evaluating the performance of a fine tuned language model in PyTorch. Remember to incorporate best practices, such as testing on both training and test data and utilizing various evaluation metrics, to ensure the accuracy and effectiveness of your language model. And as always, may the power of PyTorch be with you! To evaluate the performance of a fine tuned language model in PyTorch, we can use the following code:
def evaluate(model, data_loader, criterion):
model.eval()
total_loss = 0.
total_count = 0.
for idx, (inputs, targets) in enumerate(data_loader):
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
loss = criterion(outputs.view(-1, ntokens), targets.view(-1))
total_count += len(inputs.view(-1))
total_loss += loss.item() * len(inputs.view(-1))
return total_loss / total_countIn this code, we define the evaluate() function, which takes in the fine tuned model, the data to be evaluated (data_loader), and the loss criterion used to evaluate the model (criterion).
First, we set the model to evaluation mode using model.eval(). We then initialize total_loss and total_count to zero to keep track of the loss and number of tokens processed.
We then loop through the data loader and calculate the model's outputs using model(inputs) where inputs represents a batch of input sequences. We then calculate the loss between the outputs and targets using the specified criterion.
Finally, we update the total_loss and total_count variables with the loss multiplied by the number of tokens in the input sequence. The final perplexity score can be calculated by exponentiating the total loss divided by the total count.
By utilizing functions such as evaluate(), we can ensure that our model is accurately evaluated on both training and test data, leading to a fine tuned language model that performs effectively for the desired task.
May the power of PyTorch be with you on your language modeling endeavors!