R-BERT | Pytorch re-implementation of R-BERT model | Natural Language Processing library

by mickeystroller Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | R-BERT Summary

R-BERT is a Python library typically used in Artificial Intelligence, Natural Language Processing, Pytorch, Bert, Transformer applications. R-BERT has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However R-BERT build file is not available. You can download it from GitHub.

Pytorch re-implementation of R-BERT model

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Quality

Security

License

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Support

R-BERT has a low active ecosystem.

It has 45 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of R-BERT is current.

Quality

R-BERT has 0 bugs and 0 code smells.

Security

R-BERT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

R-BERT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

R-BERT is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

R-BERT releases are not available. You will need to build from source code and install.

R-BERT has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

R-BERT saves you 377 person hours of effort in developing the same functionality from scratch.

It has 899 lines of code, 35 functions and 7 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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R-BERT Key Features

No Key Features are available at this moment for R-BERT.

R-BERT Examples and Code Snippets

No Code Snippets are available at this moment for R-BERT.

Community Discussions

Trending Discussions on R-BERT

How to calculate perplexity of a sentence using huggingface masked language models?

KeyBERT package is not working on Google Colab

Fastbert: BertDataBunch error for multilabel text classification

QUESTION

How to calculate perplexity of a sentence using huggingface masked language models?

Asked 2021-Dec-25 at 21:51

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 21:51

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:

Source https://stackoverflow.com/questions/70464428

QUESTION

KeyBERT package is not working on Google Colab

Asked 2021-Jun-24 at 03:46

I'm using KeyBERT on Google Colab to extract keywords from the text.

...

ANSWER

Answered 2021-Jun-24 at 03:46

I couldn't reproduce this issue with the code you've provided but from the provided error message I believe you're just missing an 's' in the model name so just make sure that the model name is as follows:

distilbert-base-nli-mean-tokens

and not

distilbert-base-nli-mean-token

Also refer to this link for all models available for use.

Source https://stackoverflow.com/questions/68107887

QUESTION

Fastbert: BertDataBunch error for multilabel text classification

Asked 2020-Apr-14 at 21:14

I'm following the FastBert tutorial from huggingface https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384

The problem is this the code is not exactly reproducible. The main issue I'm facing is the dataset preparation. In the tutorial, this dataset is used https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data

But, if I set-up the folder structure according the tutorial, and place the dataset files in the folders I get errors with the databunch.

...

ANSWER

Answered 2020-Apr-14 at 21:14

First of all, you can use the notebook from GitHub for FastBert.

https://github.com/kaushaltrivedi/fast-bert/blob/master/sample_notebooks/new-toxic-multilabel.ipynb

There is a small tutorial in the FastBert README on how to process the dataset before using.

Create a DataBunch object

Source https://stackoverflow.com/questions/61217278

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install R-BERT

You can download it from GitHub.
You can use R-BERT like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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