Tokenizer | Discord bot to find the tokens of other Discord bot | Bot library
kandi X-RAY | Tokenizer Summary
kandi X-RAY | Tokenizer Summary
Tokenizer - A Discord bot to find the tokens of other Discord bots on GitHub.
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Top functions reviewed by kandi - BETA
- Handles a user request
- Gets the GHPass password
- Gets username
- Handle a tokenizer
- Evaluate an event for a guild message
- Run token
- Handles all registered commands
- Gets OAuth token
- Get OAuth token string
- Gets OAuth URL
- Main entry point
- Put OAuth token
Tokenizer Key Features
Tokenizer Examples and Code Snippets
Community Discussions
Trending Discussions on Tokenizer
QUESTION
I am working on a CNN Sentiment analysis machine learning model which uses the IMDb dataset provided by the Torchtext library. On one of my lines of code
vocab = Vocab(counter, min_freq = 1, specials=('\', '\', '\', '\'))
I am getting a TypeError for the min_freq argument even though I am certain that it is one of the accepted arguments for the function. I am also getting UserWarning Lambda function is not supported for pickle, please use regular python function or functools partial instead. Full code
...ANSWER
Answered 2022-Apr-04 at 09:26As https://github.com/pytorch/text/issues/1445 mentioned, you should change "Vocab" to "vocab". I think they miss-type the legacy-to-new notebook.
correct code:
QUESTION
I am getting the following error : attributeerror: 'dataframe' object has no attribute 'data_type'"
. I am trying to recreate the code from this link which is based on this article with my own dataset which is similar to the article
ANSWER
Answered 2022-Jan-10 at 08:41The error means you have no data_type
column in your dataframe because you missed this step
QUESTION
Take a sample program:
...ANSWER
Answered 2021-Dec-29 at 03:13The 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:
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There 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:
QUESTION
I have a mapping in elasticsearch with a field analyzer having tokenizer:
...ANSWER
Answered 2021-Dec-09 at 11:28It'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
QUESTION
I trained a model for sequence classification using transformers (BertForSequenceClassification) and I get the error:
Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select)
I don't really get where is the problem, if it's on my model, on how I tokenize the data, or what.
Here is my code:
LOADING THE PRETRAINED MODEL
...ANSWER
Answered 2021-Nov-25 at 06:19You 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
.
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What 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
.
QUESTION
I am trying to use NLTK in browser, thanks to pyodide. Pyodide starts well, manages to load NLTK, print its version.
Nevertheless, while the package downloading seems fine, when invoking nltk.sent_tokenize(str)
, NLTK raises the error that it can't find the package "punkt".
I would say the downloaded resource is lost somewhere, but I didn't understand well how Pyodide / WebAssembly manage files. Any insights ?
Simple version:
...ANSWER
Answered 2021-Sep-02 at 14:53Short 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;
QUESTION
I am using HuggingFace transformers AutoTokenizer to tokenize small segments of text. However this tokenization is splitting incorrectly in the middle of words and introducing # characters to the tokens. I have tried several different models with the same results.
Here is an example of a piece of text and the tokens that were created from it.
...ANSWER
Answered 2021-Nov-13 at 06:48This 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.
QUESTION
I have some text which I want to perform NLP on. To do so, I download a pre-trained tokenizer like so:
...ANSWER
Answered 2021-Nov-02 at 02:16If 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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Tokenizer
You can use Tokenizer like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Tokenizer component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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