attention-is-all-you-need-pytorch | PyTorch implementation of the Transformer model | Machine Learning library

by jadore801120 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | attention-is-all-you-need-pytorch Summary

attention-is-all-you-need-pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Neural Network, Transformer applications. attention-is-all-you-need-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

A PyTorch implementation of the Transformer model in "Attention is All You Need".

Support

Quality

Security

License

Reuse

Support

attention-is-all-you-need-pytorch has a medium active ecosystem.

It has 7565 star(s) with 1798 fork(s). There are 97 watchers for this library.

It had no major release in the last 6 months.

There are 58 open issues and 115 have been closed. On average issues are closed in 86 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of attention-is-all-you-need-pytorch is current.

Quality

attention-is-all-you-need-pytorch has 0 bugs and 17 code smells.

Security

attention-is-all-you-need-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

attention-is-all-you-need-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

attention-is-all-you-need-pytorch 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

attention-is-all-you-need-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, examples and code snippets are available.

attention-is-all-you-need-pytorch saves you 510 person hours of effort in developing the same functionality from scratch.

It has 1198 lines of code, 77 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Evaluate a single epoch
Calculate loss
Calculate the performance of the loss
Encode the files
Process a single line
Encodes a BPE file
Segment a sentence
Download and extract source files
Download and extract the given url and extract it
Download a file from a URL
Check if a file exists in a directory
Translate a single sentence
Get the best score and index for each beam
Get the boolean mask for a sequence of sequences
Model decoder
Learn the vocabulary
Replace a pair of word indices
Calculate the pair frequencies for each pair
Prune the stats based on the given threshold
Prepare data for training
Convert src_seq to sequence
Prepare daloaders
Merge files into one
Load a model

Get all kandi verified functions for this library.

attention-is-all-you-need-pytorch Key Features

No Key Features are available at this moment for attention-is-all-you-need-pytorch.

attention-is-all-you-need-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for attention-is-all-you-need-pytorch.

Community Discussions

Trending Discussions on attention-is-all-you-need-pytorch

How to use an embedding layer as a linear layer in PyTorch?

QUESTION

How to use an embedding layer as a linear layer in PyTorch?

Asked 2020-Dec-27 at 02:10

I'm currently working on a personal reimplementation of the Transformer paper and had a question.

On page 5 in section "3.4 Embeddings and Softmax," it states:

In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation.

I've currently implemented my model to use just one embedding layer for both source and target tensors, but I'm wondering if there would be a way that I could use the weights of the embedding layer as a linear layer. What I've currently done is something like:

...

ANSWER

Answered 2020-Dec-26 at 13:47

You could define a nn.Linear layer and replace its weights by copying the weights from the nn.Embedding:

Source https://stackoverflow.com/questions/65456174

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

Vulnerabilities

No vulnerabilities reported

Install attention-is-all-you-need-pytorch

Since the interfaces is not unified, you need to switch the main function call from main_wo_bpe to main.

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 .

Find more information at: