fastseq | efficient implementation of the popular sequence models | Natural Language Processing library

by microsoft Python Version: 0.0.3 License: MIT

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

kandi X-RAY | fastseq Summary

fastseq is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Bert, Transformer applications. fastseq has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install fastseq' or download it from GitHub, PyPI.

FastSeq provides efficient implementation of popular sequence models (e.g. Bart, ProphetNet) for text generation, summarization, translation tasks etc. It automatically optimizes inference speed based on popular NLP toolkits (e.g. FairSeq and HuggingFace-Transformers) without accuracy loss. All these can be easily done (no need to change any code/model/data if using our command line tool, or simply add one-line code import fastseq if using source code).

Support

Quality

Security

License

Reuse

Support

fastseq has a low active ecosystem.

It has 408 star(s) with 41 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 20 have been closed. On average issues are closed in 53 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fastseq is 0.0.3

Quality

fastseq has 0 bugs and 0 code smells.

Security

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

fastseq code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fastseq is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fastseq releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions are not available. Examples and code snippets are available.

It has 7653 lines of code, 290 functions and 58 files.

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

Top functions reviewed by kandi - BETA

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

Run the program .
Main function .
Generate summaries or translations .
Generate summaries from examples .
Detokenize a sample .
Process training data .
Main function for the script .
Replaces target_obj .
Compute embedding .
Override method .

Get all kandi verified functions for this library.

fastseq Key Features

No Key Features are available at this moment for fastseq.

fastseq Examples and Code Snippets

No Code Snippets are available at this moment for fastseq.

Community Discussions

Trending Discussions on fastseq

When using Python's unittest to test an class, an strange error occurs

QUESTION

When using Python's unittest to test an class, an strange error occurs

Asked 2021-Aug-17 at 19:39

I made the following test case using unittest:

...

ANSWER

Answered 2021-Aug-17 at 19:39

I solved the problem. Apparently, loader.py could not get the TestList class because __name__ was not __main__. So I just moved the if __name__ == "__main__": line to the spot where I call unittest.main().

Source https://stackoverflow.com/questions/68822466

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

Vulnerabilities

No vulnerabilities reported

Install fastseq

You can install using 'pip install fastseq' or download it from GitHub, PyPI.
You can use fastseq 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

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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