R-BERT | Pytorch implementation of R-BERT : `` Enriching Pre | Natural Language Processing library

by monologg Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)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 applications. R-BERT has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Pytorch implementation of R-BERT: "Enriching Pre-trained Language Model with Entity Information for Relation Classification"

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Quality

Security

License

Reuse

Support

R-BERT has a low active ecosystem.

It has 309 star(s) with 73 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 8 have been closed. On average issues are closed in 17 days. 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 Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Build file is available. You can build the component from source.

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

R-BERT saves you 304 person hours of effort in developing the same functionality from scratch.

It has 733 lines of code, 39 functions and 7 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed R-BERT and discovered the below as its top functions. This is intended to give you an instant insight into R-BERT implemented functionality, and help decide if they suit your requirements.

Train the model
Evaluate the model
Compute the simple accuracy and f1
Calculate the official competition score
Performs predictions on the input file
Load a Bertree tokenizer
Load a model
Convert input file to Tensor dataset
Load and cache examples
Load examples from the given mode
Create input examples from a list of lines
Read a tsv file
Forward computation
Calculate the entity average
Return the official competition score
Load the model
Set the seed
Load tokenizer

Get all kandi verified functions for this library.

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

Fastbert: BertDataBunch error for multilabel text classification

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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