cnn-text-classification-pytorch | CNNs for Sentence Classification in PyTorch | Machine Learning library

by Shawn1993 Python Version: Current License: Apache-2.0

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kandi X-RAY | cnn-text-classification-pytorch Summary

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

This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch.

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cnn-text-classification-pytorch has a medium active ecosystem.

It has 964 star(s) with 278 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 13 have been closed. On average issues are closed in 327 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cnn-text-classification-pytorch is current.

Quality

cnn-text-classification-pytorch has 0 bugs and 0 code smells.

Security

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

cnn-text-classification-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

cnn-text-classification-pytorch 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.

It has 263 lines of code, 13 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Train model
Evaluate the model
Save model to directory
Predicts the label for the given text
Build a MR dataset
Create dataset for splits
Download or unzip the tar archive

Get all kandi verified functions for this library.

cnn-text-classification-pytorch Key Features

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cnn-text-classification-pytorch Examples and Code Snippets

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Community Discussions

Trending Discussions on cnn-text-classification-pytorch

Use of PyTorch permute in RCNN

QUESTION

Use of PyTorch permute in RCNN

Asked 2021-Jan-14 at 21:57

I am looking at an implementation of RCNN for text classification using PyTorch. Full Code. There are two points where the dimensions of tensors are permuted using the permute function. The first is after the LSTM layer and before tanh. The second is after a linear layer and before a max pooling layer.

Could you please explain why the permutation is necessary or useful?

Relevant Code

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ANSWER

Answered 2021-Jan-05 at 05:11

What permute function does is rearranges the original tensor according to the desired ordering, note permute is different from reshape function, because when apply permute, the elements in tensor follow the index you provide where in reshape it's not.

Example code:

Source https://stackoverflow.com/questions/65571264

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

Vulnerabilities

No vulnerabilities reported

Install cnn-text-classification-pytorch

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