CharCNN | repository contains my implementation using Textify | Machine Learning library

by mhjabreel Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | CharCNN Summary

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

This repository contains my implementation using Textify for Character-level Convolutional Networks for Text Classification. It can be used to reproduce the results in the following article:. Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

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Support

CharCNN has a low active ecosystem.

It has 222 star(s) with 100 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 12 have been closed. On average issues are closed in 179 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CharCNN is current.

Quality

CharCNN has 0 bugs and 0 code smells.

Security

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

CharCNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CharCNN 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

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

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

Encode embeddings .
Builds a tf . dataset .
Build embeddings .

Get all kandi verified functions for this library.

CharCNN Key Features

No Key Features are available at this moment for CharCNN.

CharCNN Examples and Code Snippets

No Code Snippets are available at this moment for CharCNN.

Community Discussions

Trending Discussions on CharCNN

Change another word on condition of a word in the same row in command line?

How to replace Character Embedding using LSTM with CharCNN?

QUESTION

Change another word on condition of a word in the same row in command line?

Asked 2020-Nov-29 at 14:28

I got a html file which is as follows(delete some unuseful words).

...

ANSWER

Answered 2020-Nov-27 at 10:03

awk -v dict="{'example':l1, 'charcnn':l2, 'cifar10':l3}" '

       BEGIN{ 
                dict1=gensub(/[\}\{'"'"' ]/,"","g",dict);
                split(dict1,map,",") } 
       /toctree/ {
                cnt++;
                split(map[cnt],map1,":");
                print ""map1[1]"" }
            ' htmlfile

Source https://stackoverflow.com/questions/65033195

QUESTION

How to replace Character Embedding using LSTM with CharCNN?

Asked 2020-Oct-07 at 06:54

I'm working with this repo https://github.com/Franck-Dernoncourt/NeuroNER It is using LSTM for Char-Level-Embedding and I want to use CNN for this.

Link where it is using LSTM for Char-Level-Embedding

I tried using multiple implementation of CharCNN like this

...

ANSWER

Answered 2020-Oct-05 at 14:57

As the error message suggests, the rank of your tensors are different for concat operation hence causing the error.

Below is the simple code to reproduce your error.

Code to reproduce the error -

Source https://stackoverflow.com/questions/64182501

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

Vulnerabilities

No vulnerabilities reported

Install CharCNN

You can download it from GitHub.
You can use CharCNN 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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