Bert_Test2 | Bidirectional Encoder Representations from Transformers

by goldenbili Python Version: Current License: Apache-2.0

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kandi X-RAY | Bert_Test2 Summary

Bert_Test2 is a Python library. Bert_Test2 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.

BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here:

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Support

Bert_Test2 has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Bert_Test2 has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Bert_Test2 is current.

Quality

Bert_Test2 has no bugs reported.

Security

Bert_Test2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Bert_Test2 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

Bert_Test2 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.

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Bert_Test2 Key Features

No Key Features are available at this moment for Bert_Test2.

Bert_Test2 Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install Bert_Test2

You can download it from GitHub.
You can use Bert_Test2 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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