machine-translation | repository contains PyTorch implementations of sequence | Translation library

by tangbinh Python Version: Current License: No License

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

kandi X-RAY | machine-translation Summary

machine-translation is a Python library typically used in Utilities, Translation, Deep Learning, Pytorch, Neural Network, Transformer applications. machine-translation has no bugs, it has no vulnerabilities and it has high support. However machine-translation build file is not available. You can download it from GitHub.

This repository contains PyTorch implementations of sequence to sequence models for machine translation. The code is based on fairseq and purportedly made simple for the sake of readability, although main features such as multi-GPU training and beam search remain intact. Two encoder-decoder models are implemented in this repository: a classic model based on LSTM networks with attention mechanism (Bahdanau et al.) and Transformer, a recently favored model built entirely from self-attention (Vaswani et al.).

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machine-translation has a highly active ecosystem.

It has 50 star(s) with 17 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 147 days. There are 1 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of machine-translation is current.

Quality

machine-translation has 0 bugs and 0 code smells.

Security

machine-translation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

machine-translation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

machine-translation does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

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

Reuse

machine-translation releases are not available. You will need to build from source code and install.

machine-translation has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

machine-translation saves you 617 person hours of effort in developing the same functionality from scratch.

It has 1435 lines of code, 112 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Compute a query
Forward computation
Argument parser
Embed embedding
Return a buffered mask of a tensor
Validate the dataset
Build a binary dataset from a file
Add a word to the list
Binarize a string
Compute embedding
Get embedding
Calculates the final index based on a threshold
Read lines from a file
Load a dictionary from a file
Reads a file
Tokenize a line
Builds a dictionary from a list of files
Save the word counts to a file
Replace hypo characters in src_str
String representation of tensor
Reorder an incremental state
Post - processing post - processing
Get the value for the given key
Returns the full increment state key for a module
Set the value of the given key to the given value
Generate model

Get all kandi verified functions for this library.

machine-translation Key Features

No Key Features are available at this moment for machine-translation.

machine-translation Examples and Code Snippets

No Code Snippets are available at this moment for machine-translation.

Community Discussions

Trending Discussions on machine-translation

ValueError: None values not supported. Code working properly on CPU/GPU but not on TPU

100% training and valuation accuracy, tried gradient clipping too

QUESTION

ValueError: None values not supported. Code working properly on CPU/GPU but not on TPU

Asked 2021-Nov-09 at 12:35

I am trying to train a seq2seq model for language translation, and I am copy-pasting code from this Kaggle Notebook on Google Colab. The code is working fine with CPU and GPU, but it is giving me errors while training on a TPU. This same question has been already asked here.

Here is my code:

...

ANSWER

Answered 2021-Nov-09 at 06:27

Need to down-grade to Keras 1.0.2 If works then great, otherwise I will tell other solution.

Source https://stackoverflow.com/questions/69752055

QUESTION

100% training and valuation accuracy, tried gradient clipping too

Asked 2020-Jun-10 at 13:30

I get always 100% training and validation accuracies. Here's how it looks:

...

ANSWER

Answered 2020-Jun-10 at 12:39

You initialize decoder_targets_one_hot as vectors of zeros, but do not set the index of true class as 1 anywhere. So, basically the target vectors are not one-hot vectors. The model tries to learn same target for all inputs, i.e. the vector of zeros.

Source https://stackoverflow.com/questions/62303604

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

Vulnerabilities

No vulnerabilities reported

Install machine-translation

The code was written for Python 3.6 or higher, and it has been tested with PyTorch 0.4.1. Training is only available with GPU. To get started, try to clone the repository.

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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