unlikelihood | tensorflow implementation of the paper：Neural Text | Machine Learning library

by chenywang Python Version: Current License: No License

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kandi X-RAY | unlikelihood Summary

unlikelihood is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. unlikelihood has no bugs, it has no vulnerabilities and it has low support. However unlikelihood build file is not available. You can download it from GitHub.

tensorflow implementation of the paper: Neural Text Generation with Unlikelihood Training Sean Welleck*, Ilia Kulikov*, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston *Equal contribution. The order was decided by a coin flip.

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unlikelihood has a low active ecosystem.

It has 8 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of unlikelihood is current.

Quality

unlikelihood has no bugs reported.

Security

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

License

unlikelihood 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

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

unlikelihood 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.

Top functions reviewed by kandi - BETA

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

Calculates a sequence - loss loss .

Get all kandi verified functions for this library.

unlikelihood Key Features

No Key Features are available at this moment for unlikelihood.

unlikelihood Examples and Code Snippets

No Code Snippets are available at this moment for unlikelihood.

Community Discussions

Trending Discussions on unlikelihood

Puzzling KeyError when assigning to a new columns in a pandas DataFrame

QUESTION

Puzzling KeyError when assigning to a new columns in a pandas DataFrame

Asked 2018-Dec-01 at 00:05

I'm doing some natural language processing, and I have a MultiIndexed DataFrame that looks something like this (except there are actually about 3,000 rows):

...

ANSWER

Answered 2018-Nov-30 at 20:18

A nested for loop is not recommended, or required. You can use MultiLabelBinarizer from the sklearn.preprocessing library to provide one-hot encoding, then use groupby + sum with the results and join to your original dataframe.

Here's a demonstration:

Source https://stackoverflow.com/questions/53532140

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

Vulnerabilities

No vulnerabilities reported

Install unlikelihood

You can download it from GitHub.
You can use unlikelihood 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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