Base Model - The pre-trained QA model is used as a starting point for fine-tuning. Users have the option of starting with BioBert, Distilbert, or RoBerta large language question answering (QA) models. If there is another model that is of interest to leverage as your initial base model, please reach out to your Certara.AI contact.
Epochs - An epoch is the number of times a model sees a phrase or an example input from the training corpora.
Test Ratio - This is the ratio of testing/training data. Of the documents you assigned to each class, a portion of each dataset will be separated from the training data and used to test the model afterward. By default, the test set size is set to 0.1, or 10% of your input sets.
Batch Size - Number of examples in a single mini-batch optimization step, used as part of the learning rate optimization.
Max Length - Maximum length of question+context pairs. For shorter documents, like tweets or PubMed abstracts, this can be reduced. Smaller numbers will mean faster training and inference.
Early Stop - Prevents overtraining/overfitting of the model by stopping training if certain criteria are met.