    loading the related class instance and associated data (configurations' hyper-parameters, 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 (plus its associated tokenizer and configuration) on a given task and
    [Trainer]/Keras.fit to quickly train or fine-tune a given model.
As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
    extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
    classes of the library to reuse functionalities like model loading/saving.
Provide state-of-the-art models with performances as close as possible to the original models:
