what the topic below, response in JSON:
This pretraining is usually done on very large amounts of data. Therefore, it requires a very large corpus of data, and training can take up to several weeks.

Fine-tuning, on the other hand, is the training done after a model has been pretrained. To perform fine-tuning, you first acquire a pretrained language model, then perform additional training with a dataset specific to your task. Wait — why not simply train directly for the final task? There are a couple of reasons:

The pretrained model was already trained on a dataset that has some similarities with the fine-tuning dataset. The fine-tuning process is thus able to take advantage of knowledge acquired by the initial model during pretraining (for instance, with NLP problems, the pretrained model will have some kind of statistical understanding of the language you are using for your task).
Since the pretrained model was already trained on lots of data, the fine-tuning requires way less data to get decent results.
For the same reason, the amount of time and resources needed to get good results are much lower.
For example, one could leverage a pretrained model trained on the English language and then fine-tune it on an arXiv corpus, resulting in a science/research-based model. The fine-tuning will only require a limited amount of data: the knowledge the pretrained model has acquired is “transferred,” hence the term transfer learning. The response should be in all capital letters.
user