How to load the saved tokenizer from pretrained model

Question

I fine-tuned a pretrained BERT model in Pytorch using huggingface transformer. All the training/validation is done on a GPU in cloud.

At the end of the training, I save the model and tokenizer like below:

best_model.save_pretrained('./saved_model/')
tokenizer.save_pretrained('./saved_model/')

This creates below files in the saved_model directory:

config.json
added_token.json
special_tokens_map.json
tokenizer_config.json
vocab.txt
pytorch_model.bin

Now, I download the saved_model directory in my computer and want to load the model and tokenizer. I can load the model like below

model = torch.load('./saved_model/pytorch_model.bin',map_location=torch.device('cpu'))

But how do I load the tokenizer? I am new to pytorch and not sure because there are multiple files. Probably I am not saving the model in the right way?

Answer 1

If you look at the syntax, it is the directory of the pre-trained model that you are supposed to pass. Hence, the correct way to load tokenizer must be:

tokenizer = BertTokenizer.from_pretrained(<Path to the directory containing pretrained model/tokenizer>)

In your case:

tokenizer = BertTokenizer.from_pretrained('./saved_model/')

./saved_model here is the directory where you'll be saving your pretrained model and tokenizer.