electra | A desktop application for test account managment | User Interface library

The operation performed in the code is quadratic in its nature. While I managed to execute your snippet with 10000 samples within a few minutes, a 14900 long input ran out of memory on 32GB RAM runtime. Is it possible that your runtime is experiencing swapping?

It is not clear what is the snippet trying to achieve. Do you intend to train model? In such case you can define the text_input as an Input layer and use fit to train. Here is an example: https://www.tensorflow.org/text/tutorials/classify_text_with_bert#define_your_model

When instantiating AutoModel, you must specify a model_type parameter in ./MRPC/config.json file (downloaded during Notebook runtime).

List of model_types can be found here.

Code that appends model_type to config.json, in the same format:

Since there was no input example, I used your "Output I have" as input.

I also checked if the whole line contains the word terror or bird, but you can change it if you need the column where it is.

There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:

The issue is in these 3 lines.

rename works for me, perhaps you have a function conflict with another package. Try using dplyr::rename.

To change the columns based on range of values you may use case_when or cut.

The exception comes from a Django internal middleware, since it's trying to process your returned qs as a response.

You'll need to return a response, not just a queryset, e.g. this simple example to return a list of user IDs.

It seems that @npit is right. The output of the convert_electra_original_tf_checkpoint_to_pytorch.py does not contain the configuration that I gave (hparams.json), therefore I created an ElectraConfig object -- with the same parameters -- and provided it to the from_pretrained function. That solved the issue.

I think the easiest way to achieve this is to define a tidy_custom.polr method as described here in the documentation.. For instance, you could do: