Save_pre_trained_locally.py

import torch
from transformers import *

# Transformers has a unified API
# for 8 transformer architectures and 30 pretrained weights.
#          Model          | Tokenizer          | Pretrained weights shortcut
MODELS = [(BertModel,      BertTokenizer,        'bert-base-multilingual-cased')]

# To use TensorFlow 2.0 versions of the models, simply prefix the class names with 'TF', e.g. `TFRobertaModel` is the TF 2.0 counterpart of the PyTorch model `RobertaModel`

# Let's encode some text in a sequence of hidden-states using each model:
for model_class, tokenizer_class, pretrained_weights in MODELS:
    # Load pretrained model/tokenizer
    tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
    model = model_class.from_pretrained(pretrained_weights,
                                        output_hidden_states=True,
                                        output_attentions=False)

    model.save_pretrained('Pre_trained_BERT/')  # save
    tokenizer.save_pretrained('Pre_trained_BERT/')  # save