Text-Classification-PyTorch | Implementation of papers for text classification | Natural Language Processing library

 by   Doragd Python Version: Current License: No License

kandi X-RAY | Text-Classification-PyTorch Summary

kandi X-RAY | Text-Classification-PyTorch Summary

Text-Classification-PyTorch is a Python library typically used in Artificial Intelligence, Natural Language Processing, Bert applications. Text-Classification-PyTorch has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Implementation of papers for text classification task on SST-1/SST-2
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              Text-Classification-PyTorch has a low active ecosystem.
              It has 37 star(s) with 9 fork(s). There are 3 watchers for this library.
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              It had no major release in the last 6 months.
              There are 0 open issues and 2 have been closed. On average issues are closed in 204 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Text-Classification-PyTorch is current.

            kandi-Quality Quality

              Text-Classification-PyTorch has 0 bugs and 0 code smells.

            kandi-Security Security

              Text-Classification-PyTorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Text-Classification-PyTorch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Text-Classification-PyTorch does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Text-Classification-PyTorch releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              Text-Classification-PyTorch saves you 246 person hours of effort in developing the same functionality from scratch.
              It has 598 lines of code, 31 functions and 8 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Text-Classification-PyTorch and discovered the below as its top functions. This is intended to give you an instant insight into Text-Classification-PyTorch implemented functionality, and help decide if they suit your requirements.
            • Creates input files
            • Load embeddings
            • Update the sum
            • Train the model
            • Train model
            • Validate the loss function
            • Save checkpoint
            • Clip gradients from optimizer
            • Decrement learning rate
            • Calculate accuracy
            • Test the model
            • Evaluate the model
            Get all kandi verified functions for this library.

            Text-Classification-PyTorch Key Features

            No Key Features are available at this moment for Text-Classification-PyTorch.

            Text-Classification-PyTorch Examples and Code Snippets

            No Code Snippets are available at this moment for Text-Classification-PyTorch.

            Community Discussions

            Trending Discussions on Text-Classification-PyTorch

            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

            ...

            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 Text-Classification-PyTorch

            You can download it from GitHub.
            You can use 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.

            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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