adversarial_attack | 2019DIAC对抗攻击比赛（语义相似度比赛）

by FreeFlyXiaoMa Python Version: Current License: No License

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

kandi X-RAY | adversarial_attack Summary

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

2019DIAC对抗攻击比赛（语义相似度比赛）

Support

Quality

Security

License

Reuse

Support

adversarial_attack has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

adversarial_attack has no issues reported. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of adversarial_attack is current.

Quality

adversarial_attack has no bugs reported.

Security

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

License

adversarial_attack 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

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

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

Top functions reviewed by kandi - BETA

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

Train model
Convert examples to features
Create a sequence of tokens from a document
Build a function for the TPUEstimator
Loads and cache training examples
A single step
Evaluate the given model
Convert a sequence of examples into features
Convert a single example
Checks if the word spans in the document
Truncate a sequence pair
Load a pretrained model from a pretrained model
Updates the statistics
Create an instance from a json file
Evaluate a model
Train the model
Transformer transformer
Attention layer
Create Tokenizer from pretrained model
Create an optimizer
Create a Config object from a pretrained model
Performs a single step of the optimizer
Compute the log probability for each cluster
Load weights from an XLS file
Load and cache training examples
Forward computation
Embedding postprocessor
Create tokens from a document
Perform the forward computation
Build a function for TPUEstimator
Convert a roberta checkpoint
Create train dataset
Forward attention

Get all kandi verified functions for this library.

adversarial_attack Key Features

No Key Features are available at this moment for adversarial_attack.

adversarial_attack Examples and Code Snippets

No Code Snippets are available at this moment for adversarial_attack.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install adversarial_attack

You can download it from GitHub.
You can use adversarial_attack 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: