FastBERT | 对ACL2020 FastBERT论文的复现，论文地址 https | Natural Language Processing library

by BitVoyage Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | FastBERT Summary

FastBERT is a Python library typically used in Artificial Intelligence, Natural Language Processing, Bert, Neural Network, Transformer applications. FastBERT has no bugs, it has no vulnerabilities and it has low support. However FastBERT build file is not available. You can download it from GitHub.

对ACL2020 FastBERT论文的复现，论文地址:

Support

Quality

Security

License

Reuse

Support

FastBERT has a low active ecosystem.

It has 153 star(s) with 33 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 3 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FastBERT is current.

Quality

FastBERT has 0 bugs and 0 code smells.

Security

FastBERT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FastBERT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FastBERT does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

FastBERT releases are not available. You will need to build from source code and install.

FastBERT 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.

FastBERT saves you 585 person hours of effort in developing the same functionality from scratch.

It has 1364 lines of code, 112 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Train a model
Perform a single step
Train a single epoch
Save model weights to path
Tokenize text
Convert input to unicode
Split text into tokens
Infer the model for the given dataset
Compute precision recall
Compute the attention matrix
Transpose the input tensor
Evaluate a model
Load a saved model
Load vocabulary from file
Predict on given text
Load a config file

Get all kandi verified functions for this library.

FastBERT Key Features

No Key Features are available at this moment for FastBERT.

FastBERT Examples and Code Snippets

No Code Snippets are available at this moment for FastBERT.

Community Discussions

Trending Discussions on FastBERT

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 FastBERT

You can download it from GitHub.
You can use FastBERT 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 .

Find more information at: