Load a pre-trained model from disk with Huggingface Transformers

Question

From the documentation for from_pretrained, I understand I don't have to download the pretrained vectors every time, I can save them and load from disk with this syntax:

  - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
  - (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.

So, I went to the model hub:

• https://huggingface.co/models

I found the model I wanted:

• https://huggingface.co/bert-base-cased

I downloaded it from the link they provided to this repository:

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.

Stored it in:

  /my/local/models/cased_L-12_H-768_A-12/

Which contains:

 ./
 ../
 bert_config.json
 bert_model.ckpt.data-00000-of-00001
 bert_model.ckpt.index
 bert_model.ckpt.meta
 vocab.txt

So, now I have the following:

  PATH = '/my/local/models/cased_L-12_H-768_A-12/'
  tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

And I get this error:

>           raise EnvironmentError(msg)
E           OSError: Can't load config for '/my/local/models/cased_L-12_H-768_A-12/'. Make sure that:
E           
E           - '/my/local/models/cased_L-12_H-768_A-12/' is a correct model identifier listed on 'https://huggingface.co/models'
E           
E           - or '/my/local/models/cased_L-12_H-768_A-12/' is the correct path to a directory containing a config.json file

Similarly for when I link to the config.json directly:

  PATH = '/my/local/models/cased_L-12_H-768_A-12/bert_config.json'
  tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

        if state_dict is None and not from_tf:
            try:
                state_dict = torch.load(resolved_archive_file, map_location="cpu")
            except Exception:
                raise OSError(
>                   "Unable to load weights from pytorch checkpoint file. "
                    "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
                )
E               OSError: Unable to load weights from pytorch checkpoint file. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.

What should I do differently to get huggingface to use my local pretrained model?

Update to address the comments

YOURPATH = '/somewhere/on/disk/'

name = 'transfo-xl-wt103'
tokenizer = TransfoXLTokenizerFast(name)
model = TransfoXLModel.from_pretrained(name)
tokenizer.save_pretrained(YOURPATH)
model.save_pretrained(YOURPATH)

>>> Please note you will not be able to load the save vocabulary in Rust-based TransfoXLTokenizerFast as they don't share the same structure.
('/somewhere/on/disk/vocab.bin', '/somewhere/on/disk/special_tokens_map.json', '/somewhere/on/disk/added_tokens.json')

So all is saved, but then....

YOURPATH = '/somewhere/on/disk/'
TransfoXLTokenizerFast.from_pretrained('transfo-xl-wt103', cache_dir=YOURPATH, local_files_only=True)

    "Cannot find the requested files in the cached path and outgoing traffic has been"
ValueError: Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable model look-ups and downloads online, set 'local_files_only' to False.

Answer 1

Where is the file located relative to your model folder? I believe it has to be a relative PATH rather than an absolute one. So if your file where you are writing the code is located in 'my/local/', then your code should be like so:

PATH = 'models/cased_L-12_H-768_A-12/'
tokenizer = BertTokenizer.from_pretrained(PATH, local_files_only=True)

You just need to specify the folder where all the files are, and not the files directly. I think this is definitely a problem with the PATH. Try changing the style of "slashes": "/" vs "\", these are different in different operating systems. Also try using ".", like so ./models/cased_L-12_H-768_A-12/ etc.