FastBERT | The score code of FastBERT | Natural Language Processing library

by autoliuweijie Python Version: 0.2.2 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, Pytorch, Bert, Transformer applications. FastBERT has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install FastBERT' or download it from GitHub, PyPI.

Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

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Quality

Security

License

Reuse

Support

FastBERT has a low active ecosystem.

It has 488 star(s) with 69 fork(s). There are 17 watchers for this library.

It had no major release in the last 12 months.

There are 27 open issues and 9 have been closed. On average issues are closed in 11 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FastBERT is 0.2.2

Quality

FastBERT has 0 bugs and 155 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 25 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.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

FastBERT saves you 2943 person hours of effort in developing the same functionality from scratch.

It has 6353 lines of code, 372 functions and 105 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.

Perform the forward computation
Computes the distances between the given indices
Calculate Shannon entropy
Performs a single step
Train the model
Fine tuning of the backbone
Run inference on the given sentence
Evaluate the prediction
Main worker function
Builds a list of Instances from the given document
Tokenize text
Worker function
Fit the model
Self - distill
Performs self - distill training
Build the vocabulary
Check if file or file exists
Train and validate the model
Load the vocabulary
Compute the MLM loss
Build and save datasets
Implements the MLM
Return the label for the given sentence
Load vocabulary from file
Loads a dataset
Load model
Print the configuration of the model

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

Download the pre-trained Chinese BERT parameters from here, and save it to the models directory with the name of "Chinese_base_model.bin". Run the following command to validate our FastBERT with Speed=0.5 on the Book review datasets. Meaning of each option. Test results on the Book review dataset.
Download the pre-trained English BERT parameters from here, and save it to the models directory with the name of "English_uncased_base_model.bin". Download the ag_news.zip from here, and then unzip it to the datasets directory. Run the following command to validate our FastBERT with Speed=0.5 on the Ag.news datasets. Test results on the Ag.news dataset.

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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