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

by threelittlemonkeys Python Version: Current License: No License

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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 applications. cnn-text-classification-pytorch has no bugs, it has no vulnerabilities and it has low support. However cnn-text-classification-pytorch build file is not available. You can download it from GitHub.

CNNs for Text Classification in PyTorch

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

It has 13 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

cnn-text-classification-pytorch has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

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

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cnn-text-classification-pytorch has no bugs reported.

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

License

cnn-text-classification-pytorch does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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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.

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 the model
Generate a tensor
Calculate the average score
Load data from file
Split the dataset into batches
Save a checkpoint
Load index from file
Load Tkn_to_index from file
Loads a checkpoint
Flattens a list
Tokenize a string
Compute the average score
Load data
Saves tkn to file
Save sequence data to file

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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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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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