NSEC_NMT | first instance of NSEC , a spelling error correction system

by naetherm Python Version: Current License: MIT

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

kandi X-RAY | NSEC_NMT Summary

NSEC_NMT is a Python library. NSEC_NMT 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.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

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Quality

Security

License

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Support

NSEC_NMT 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.

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

It has a neutral sentiment in the developer community.

The latest version of NSEC_NMT is current.

Quality

NSEC_NMT has no bugs reported.

Security

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

License

NSEC_NMT 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

NSEC_NMT 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, examples and code snippets are available.

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NSEC_NMT Key Features

No Key Features are available at this moment for NSEC_NMT.

NSEC_NMT Examples and Code Snippets

No Code Snippets are available at this moment for NSEC_NMT.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install NSEC_NMT

If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run.
PyTorch version >= 1.1.0
Python version >= 3.5
For training new models, you'll also need an NVIDIA GPU and NCCL
For faster training install NVIDIA's apex library with the --cuda_ext option
The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

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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