BERT-LS | Lexical Simplification with Pretrained Encoders

by qiang2100 Python Version: Current License: No License

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

kandi X-RAY | BERT-LS Summary

BERT-LS is a Python library. BERT-LS has no bugs, it has no vulnerabilities and it has low support. However BERT-LS build file is not available. You can download it from GitHub.

Lexical Simplification with Pretrained Encoders

Support

Quality

Security

License

Reuse

Support

BERT-LS has a low active ecosystem.

It has 45 star(s) with 12 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 6 have been closed. On average issues are closed in 41 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of BERT-LS is current.

Quality

BERT-LS has 0 bugs and 0 code smells.

Security

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

BERT-LS code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BERT-LS 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

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

BERT-LS 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.

BERT-LS saves you 4341 person hours of effort in developing the same functionality from scratch.

It has 9199 lines of code, 540 functions and 37 files.

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

Top functions reviewed by kandi - BETA

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

Create a trained model from a pretrained pretrained model
Load weights from a tf checkpoint file
Construct an instance from a json file
Load a pretrained model from a pretrained model
Load weights from a TensorFlow checkpoint file
Construct an instance from a JSON file
Create a embedding from a pretrained model
Load tf weights from OpenAI checkpoint folder
Create a trained model from a pretrained model
Set the weights for the embedding
Forward computation
Get all synonyms
Run the experiment
Perform the forward computation
Convert a single word to a feature
Calculates the rank of a source word based on the substitution score
Convert token to feature
Compute log probability for each cluster
Read a model
Convert a sentence to a list of tokens
Load a pre - trained model from a pre - trained model
Tokenize text
Converts a TensorFlow checkpoint file to PyTorch
Perform substitution
Read an eval dataset
Generate a candidate for BERT generation

Get all kandi verified functions for this library.

BERT-LS Key Features

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

BERT-LS Examples and Code Snippets

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

Community Discussions

No Community Discussions are available at this moment for BERT-LS.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install BERT-LS

You can download it from GitHub.
You can use BERT-LS 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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