multi-class-text-classification-cnn | Classify Kaggle Consumer Finance Complaints into 11 classes | Machine Learning library

by jiegzhan Python Version: Current License: Apache-2.0

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kandi X-RAY | multi-class-text-classification-cnn Summary

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

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

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multi-class-text-classification-cnn has a low active ecosystem.

It has 423 star(s) with 203 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 24 open issues and 4 have been closed. On average issues are closed in 86 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multi-class-text-classification-cnn is current.

Quality

multi-class-text-classification-cnn has 0 bugs and 0 code smells.

Security

multi-class-text-classification-cnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

multi-class-text-classification-cnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multi-class-text-classification-cnn 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.

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multi-class-text-classification-cnn releases are not available. You will need to build from source code and install.

multi-class-text-classification-cnn has no build file. You will be need to create the build yourself to build the component from source.

multi-class-text-classification-cnn saves you 110 person hours of effort in developing the same functionality from scratch.

It has 279 lines of code, 8 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Train CNN
Load data and labels from a CSV file
Clean a string
Generate batches of data
Predict unseen data

Get all kandi verified functions for this library.

multi-class-text-classification-cnn Key Features

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multi-class-text-classification-cnn Examples and Code Snippets

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Community Discussions

Trending Discussions on multi-class-text-classification-cnn

ImportError: No module named tensorflow, but tensorflow does exist

QUESTION

ImportError: No module named tensorflow, but tensorflow does exist

Asked 2017-Feb-21 at 08:40

I installed tensorflow with virtualenv on linux. There is a tensorflow package under sitepackage folder, but when I run the demo downloaded from Github, it shows:

...

ANSWER

Answered 2017-Feb-21 at 07:46

you are using sudo python CNN_sentence_tensorflow-master/sentence_classfier_with_tensorflow.py

if you use sudo i think it will use your main python version not the one in your virtualenv

Source https://stackoverflow.com/questions/42361316

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

Vulnerabilities

No vulnerabilities reported

Install multi-class-text-classification-cnn

You can download it from GitHub.
You can use multi-class-text-classification-cnn 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.

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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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