pytorch-nlp | The NLP toolkit use pytorch | Natural Language Processing library

by endymecy Python Version: Current License: MIT

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

kandi X-RAY | pytorch-nlp Summary

pytorch-nlp is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Neural Network applications. pytorch-nlp has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

The NLP toolkit use pytorch

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Quality

Security

License

Reuse

Support

pytorch-nlp has a low active ecosystem.

It has 39 star(s) with 16 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 243 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-nlp is current.

Quality

pytorch-nlp has 0 bugs and 0 code smells.

Security

pytorch-nlp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-nlp code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-nlp 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

pytorch-nlp 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.

pytorch-nlp saves you 4162 person hours of effort in developing the same functionality from scratch.

It has 8838 lines of code, 583 functions and 88 files.

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

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pytorch-nlp Key Features

No Key Features are available at this moment for pytorch-nlp.

pytorch-nlp Examples and Code Snippets

No Code Snippets are available at this moment for pytorch-nlp.

Community Discussions

Trending Discussions on pytorch-nlp

pytorch embedding index out of range

PyTorch custom loss function

QUESTION

pytorch embedding index out of range

Asked 2019-May-06 at 19:00

I'm following this tutorial here https://cs230-stanford.github.io/pytorch-nlp.html. In there a neural model is created, using nn.Module, with an embedding layer, which is initialized here

...

ANSWER

Answered 2019-May-06 at 19:00

You've got some things wrong. Please correct those and re-run your code:

params['vocab_size'] is the total number of unique tokens. So, it should be len(vocab) in the tutorial.
params['embedding_dim'] can be 50 or 100 or whatever you choose. Most folks would use something in the range [50, 1000] both extremes inclusive. Both Word2Vec and GloVe uses 300 dimensional embeddings for the words.
self.embedding() would accept arbitrary batch size. So, it doesn't matter. BTW, in the tutorial the commented things such as # dim: batch_size x batch_max_len x embedding_dim indicates the shape of output tensor of that specific operation, not the inputs.

Source https://stackoverflow.com/questions/56010551

QUESTION

PyTorch custom loss function

Asked 2018-Dec-30 at 19:26

How should a custom loss function be implemented ? Using below code is causing error :

...

ANSWER

Answered 2018-Dec-30 at 19:26

Your loss function is programmatically correct except for below:

Source https://stackoverflow.com/questions/53980031

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-nlp

You can download it from GitHub.
You can use pytorch-nlp 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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