Tokenizer | customizable text tokenization library with BPE | Natural Language Processing library

The error means you have no data_type column in your dataframe because you missed this step

The problem is not the order of interpretation, which is top to bottom as you expect; it's the scope. In Python, when referencing a global variable in a narrower function scope, if you modify the value, you must first tell the code that the global variable is the variable you are referencing, instead of a new local one. You do this with the global keyword. In this example, your program should actually look like this:

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:

It's not related to ES version.

Update max_expansions to more than 50.

max_expansions : Maximum number of variations created.

With 3 grams letter & digits as token_chars, ideal max_expansion will be (26 alphabets + 10 digits) * 3

You did not move your model to device, only the data. You need to call model.to(device) before using it with data located on device.

What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

Short answer is that downloading files with Python currently won't work in Pyodide because http.client, requests etc require POSIX sockets which are not supported in the browser VM.

It's curious that nltk.download doesn't error though -- it should have.

The workaround is to manually download the needed resources, for instance, using the JavaScript fetch API as illustrated in this comment;

This is not an error but a feature. BERT and other transformers use WordPiece tokenization algorithm that tokenizes strings into either: (1) known words; or (2) "word pieces" for unknown words in the tokenizer vocabulary.

In your examle, words "CTO", "TLR", and "Pty" are not in the tokenizer vocabulary, and thus WordPiece splits them into subwords. E.g. the first subword is "CT" and another part is "##O" where "##" denotes that the subword is connected to the predecessor.

This is a great feature that allows to represent any string.

If you can find distilbert folder in your pc, you can see vocabulary is basically txt file that contains only one column. You can do whatever you want to do.