How to modify the model config?
Well I am trying to generate embedding for a large sentence. I get this error 

> Traceback (most recent call last):
    all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)
  File "/Users/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/venv/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 611, in forward
    embedding_output = self.embeddings(input_ids, token_type_ids)
  File "/Users/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/venv/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 196, in forward
    position_embeddings = self.position_embeddings(position_ids)
  File "/Users/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/venv/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 110, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/Users/venv/lib/python3.6/site-packages/torch/nn/functional.py", line 1110, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: index out of range at /Users/soumith/code/builder/wheel/pytorch-src/aten/src/TH/generic/THTensorMath.cpp:352

I find that max_position_embeddings (default size 512) is getting exceeded. Which is taken from the config that is downloaded as part of the initial step. Initially the download was done to the default location `PYTORCH_PRETRAINED_BERT_CACHE` where I was not able to find the config.json other than the model file and vocab.txt (named with random characters). I did it to a specific location in local with the `cache_dir` param, here also I was facing the same problem of finding the bert_config.json.

Also I found a file in both the default cache and local cache, named with junk characters of JSON type. When I tried opening it, I could just see this

_{"url": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", "etag": "\"61343686707ed78320e9e7f406946db2-49\""}_

Any help to modify the config.json would be appreciated.

Or if this is been caused for a different reason, Please let me know.
The problem is  because of the `max_position_embeddings` default size is 512 and it is exceeding in the case of my input as I mentioned. For now I have just made hack by hard coding it directly in the [modelling.py](url) file directly 😅. Yet need to know, where to find the bert_config.json file and changing it there would be the correct way of doing it.<|||||>The config file is located in the .tar.gz archive that is getting downloaded, cached, and then extracted on the fly as you create a `BertModel` instance with the static `from_pretrained()` constructor. 
You'll see a log message like
```
 extracting archive file /home/USERNAME/.pytorch_pretrained_bert/bert-base-cased.tar.gz to temp dir /tmp/tmp96bkwrj0
```
If you extract that archive yourself, you'll find the bert_config.json file. The thing, though, is that it doesn't make sense to modify this file, as it is tied to the pretrained models. If you increase `max_position_embeddings` in the config, you won't be able to use the pretrained models.

Instead, you will have to train a model from scratch, which may or -- more likely -- may not be feasible depending on the hardware you have access to.<|||||>Yeah as you said, while debugging I noticed that every time the .tar.gz file was extracted to a new temp cache location and from there models are fetched. Even in that case we are not able to find the json file where it was extracted. Also I think `max_position_embeddings` does not relate with the model training because, when I changed its value(before loading the model with torch.load) like this 

`config.__dict__['max_position_embeddings'] = 2048`

from 512 to 2048 (hard coded way) the code ran properly without any error.

And the [lines](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L99-L101) in modelling.py tells that it can be customised if required. But I don't see a way parameterising it so that it will be changed while fetching the config, because it is loaded like [this](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L500-L501).

It would be great if customisations are supported for the applicable options.<|||||>It does not make sense to customize options when using pretrained models, it only makes sense when training your own model from scratch.

You cannot use the pretrained models with another max_position_embeddings than 512, because the pretrained models contain pretrained embeddings for 512 positions.
The original transformer paper introduced a [positional encoding](http://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding) which allows extrapolation to arbitrary input lengths, but this was not used in BERT.

You can override max_position_embeddings, but this won't have any effect. The model will probably run fine for shorter inputs, but you will get a `RuntimeError: cuda runtime error (59)` for an input longer than 512 word pieces, because the embedding lookup [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/3ba5470eb85464df62f324bea88e20da234c423f/pytorch_pretrained_bert/modeling.py#L194) will attempt to use an index that is too large.<|||||>Indeed, it doesn't make sense to go over 512 tokens for a pre-trained model.

If you have longer text, you should try the sliding window approach detailed on the original Bert repo: https://github.com/google-research/bert/issues/66<|||||>1. What if my sentences are well within 100 token in length.  In that case does it make sense to change max_position_embeddings?
2. Adding 1 more similar question to, during model evaluation if I pass sentence to model and generate embeddings will it take sentence length as total tokens or 512 default? In that scenario if my sentence  has 10 unique tokens then what does 512 stands for in hidden layers?