

All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
    from_pretrained() method which downloads (if needed), caches and
    loads the related class instance and associated data (configurations' hyperparameters, tokenizers' vocabulary,
    and models' weights) from a pretrained checkpoint provided on Hugging Face Hub or your own saved checkpoint.
On top of those three base classes, the library provides two APIs: [pipeline] for quickly
    using a model for inference on a given task and [Trainer] to quickly train or fine-tune a PyTorch model (all TensorFlow models are compatible with Keras.fit).