Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
Main concepts
The library is built around three types of classes for each model:
Model classes such as [BertModel], which are 30+ PyTorch models (torch.nn.Module) or Keras models (tf.keras.Model) that work with the pretrained weights provided in the
  library.
Configuration classes such as [BertConfig], which store all the parameters required to build
  a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
  without any modification, creating the model will automatically take care of instantiating the configuration (which
  is part of the model).
Tokenizer classes such as [BertTokenizer], which store the vocabulary for each model and
