MLM | level Marketing System handles chaining of their users | Frontend Framework library

I have the following string to extract volume (match only ml, not mg/ml)

I am getting this error "PipelineException: No mask_token ([MASK]) found on the input" when I run this line. fill_mask("Auto Car .")

I am running it on Colab. My Code:

I am getting "RuntimeError: Input, output and indices must be on the current device." when I run this line. fill_mask("Auto Car .")

I am running it on Colab. My Code:

Trying to find any Tensorflow/Keras implementation of the original BERT model trained using MLM/NSP. The official google and HuggingFace implementations are very complex and has so much of added functionalities. But I want to learn and implement BERT for just learning its working.

Any leads will be helpful?

My question here is no how to add new tokens, or how to train using a domain-specific corpus, I'm already doing that.

The thing is, am I supposed to add the domain-specific tokens before the MLM training, or I just let Bert figure out the context? If I choose to not include the tokens, am I going to get a poor task-specific model like NER?

To give you more background of my situation, I'm training a Bert model on medical text using Portuguese language, so, deceased names, drug names, and other stuff are present in my corpus, but I'm not sure I have to add those tokens before the training.

I saw this one: Using Pretrained BERT model to add additional words that are not recognized by the model

But the doubts remain, as other sources say otherwise.

Thanks in advance.

I am trying to use the optim() function in R to minimize a value with matrix operations. In this case, I am trying to minimize the volatility of a group of stocks whose individual returns covary with each other. The objective function being minimized is calculate_portfolio_variance.

I am trying to tokenize some numerical strings using a WordLevel/BPE tokenizer, create a data collator and eventually use it in a PyTorch DataLoader to train a new model from scratch.

However, I am getting an error

AttributeError: 'ByteLevelBPETokenizer' object has no attribute 'pad_token_id'

when running the following code

Here my data frame (reproducible example)

I'm interested in Using functions and lapply to make properly labelled histograms.

But when I try to use a function and lapply to create histograms that display the spread of data, the xlab doesn't give the text of the variable of interest. Instead it uses the first value of the variable of interest. How to I fix this issue?

The code I used is below: