Data Splitting: A Key Ingredient for finetuning Large Language Models
When it comes to finetuning large language models for developing AI solutions, one essential step is data splitting. A careful and well structured data split strategy is a key ingredient for finetuning.
In our no-code LLMOps platform for finetuning LLM models - EasyFinetuner, we have added built-in features to do data splitting for finetuning LLMs and support data splitting for continuous finetuning.
This process involves dividing your dataset into three distinct subsets: the training set, the validation set, and the test set. In this fun-to-read article, we'll dive into the world of data splitting and explore its importance in finetuning large language models, with examples of accuracy percentage values against different datasets and how we decide the best model based on these values.
The Three Musketeers of Data Splitting: Training, Validation, and Test Sets
To understand the magic of data splitting, let's first get acquainted with the three subsets of data that play a crucial role in the process:
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Training set: This is the portion of the dataset that the model uses to learn the underlying patterns and relationships in the data. For instance, in sentiment classification, the model will learn to predict sentiments based on patterns and relationships between words and phrases.
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Validation set: This set is used to evaluate the performance of different models and hyperparameter choices. It helps in selecting the best model or parameter for the task at hand.
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Test set: This set is used to assess the final model's accuracy and performance. It should be used only after the validation set has been employed to select the best model.
Now that we know the key players, let's delve deeper into their roles in finetuning large language models, with examples of accuracy percentage values against different datasets.
Training Set: The Teacher of the Model
The training set is responsible for teaching the model by providing it with examples to learn from. It is essential to ensure that the training set is representative of the entire dataset and covers all classes or categories. Additionally, the training set should be unbiased to prevent the model from learning any incorrect patterns or relationships.
For example, in sentiment classification, the training set should include a diverse range of sentiments and text samples to help the model learn effectively. Suppose the model achieves an accuracy of 90% on the training set. In that case, it indicates that it has learned to predict sentiments with a high degree of accuracy based on the patterns and relationships in the training data.
Validation Set: The Model Selector
When finetuning large language models, we often experiment with different algorithms or model parameters. To choose the best model or parameter, we need an unbiased and independent dataset – the validation set.
Using the same data for training and tuning the model can lead to overfitting, which hampers the model's ability to generalize to unseen data. The validation set helps us avoid this pitfall by providing a separate dataset for evaluating and comparing the performance of different models.
For example, let's say we have three models with different hyperparameters, and their respective accuracies on the validation set are 85%, 88%, and 82%. Based on these values, we can decide that the second model with an accuracy of 88% is the best one for our task.
Test Set: The Final Verdict
Once we have trained, validated, and selected the best model, it's time to put it to the test. The test set is used to evaluate the final model's performance and accuracy. It is crucial to use the test set only after the validation set has been employed to select the best model, as using it prematurely can lead to overfitting and unreliable performance.
Suppose our chosen model achieves an accuracy of 87% on the test set. In that case, it indicates that the model generalizes well to unseen data and can be considered reliable for sentiment classification tasks.
In a Nutshell
Data splitting is an indispensable step in finetuning large language models for tasks like sentiment classification. Here's a quick recap of the key takeaways:
- Data splitting is essential for training, validating, and evaluating machine learning models.
- Divide your dataset into three subsets: training, validation, and test sets.
- Ensure that the training set is representative, diverse, and unbiased to facilitate effective learning.
- Use the validation set to evaluate and compare the performance of different models and select the best one based on accuracy values.
- Employ the test set to assess the final model's performance and accuracy.
By following these guidelines, you can harness the power of data splitting to fine-tune large language models effectively and achieve accurate and reliable performance in tasks like sentiment classification. Happy finetuning!