nmt-keras | Neural Machine Translation with Keras | Translation library

by lvapeab Python Version: 0.6 License: MIT

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kandi X-RAY | nmt-keras Summary

nmt-keras is a Python library typically used in Utilities, Translation, Deep Learning, Pytorch, Tensorflow, Neural Network applications. nmt-keras 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.

Neural Machine Translation with Keras

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nmt-keras has a low active ecosystem.

It has 530 star(s) with 132 fork(s). There are 27 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 120 have been closed. On average issues are closed in 59 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of nmt-keras is 0.6

Quality

nmt-keras has 0 bugs and 0 code smells.

Security

nmt-keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

nmt-keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

nmt-keras 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

nmt-keras releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

nmt-keras saves you 3448 person hours of effort in developing the same functionality from scratch.

It has 7387 lines of code, 143 functions and 68 files.

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

Top functions reviewed by kandi - BETA

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

Transformer
Get positional encodings of layer
Load model parameters
Sets the optimizer
Train a model
Builds a dataset instance
Build the callbacks
Prepare n captions
Sample an ensemble
Build a dataset instance
Score a corpus
Do a GET request
Generate a sample
Learn from source
Check params for preprocessing
Invokes a Spearmint with the given parameters
Load training data
Train the model
Convert word2vec to npy npy
Convert a txtvec vector into a numpy array
Parse command line arguments
Builds a glossary file
Update params from a dictionary
Average multiple models

Get all kandi verified functions for this library.

nmt-keras Key Features

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nmt-keras Examples and Code Snippets

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Community Discussions

Trending Discussions on nmt-keras

Python split tabspaced bilingual txt to two separate txt files (list) with newlines separating strings

Keras example word-level model with integer sequences gives `expected ndim=3, found ndim=4`

QUESTION

Python split tabspaced bilingual txt to two separate txt files (list) with newlines separating strings

Asked 2019-Jan-10 at 22:07

I have a bi-lingual corpora (EN-JP) from tatoeba and want to split this into two separate files. The strings have to say on the same line respectively.

I need this for training an NMT in nmt-keras and training data has to be stored in separate files for each language. I tried several approaches, but since I'm an absolute beginner with python and coding in general I feel like I'm running in circles.

So far the best I managed was the following:

Source txt:

...

ANSWER

Answered 2019-Jan-10 at 22:07

The first thing to be aware of is that iterating over a file retains the newlines. That means that in your two columns, the first has no newlines, while the second has newlines already appended to each line (except possibly the last).

Writing the second column is therefore trivial if you've already unpacked the generator columns:

Source https://stackoverflow.com/questions/54135703

QUESTION

Keras example word-level model with integer sequences gives `expected ndim=3, found ndim=4`

Asked 2018-Aug-18 at 17:06

I'm trying to implement the Keras word-level example on their blog listed under the Bonus Section -> What if I want to use a word-level model with integer sequences?

I've marked up the layers with names to help me reconnect the layers from a loaded model to a inference model later. I think I've followed their example model:

...

ANSWER

Answered 2018-Aug-18 at 17:06

The problem is in the input shape of Input layer. An embedding layer accepts a sequence of integers as input which corresponds to words indices in a sentence. Since here the number of words in sentences is not fixed, therefore you must set the input shape of Input layer as (None,).

I think you are mistaking it with the case that we don't have an Embedding layer in our model and therefore the input shape of the model is (timesteps, n_features) to make it compatible with LSTM layer.

Update:

You need to pass the decoder_inputs to the Embedding layer first and then pass the resulting output tensor to the decoder_lstm layer like this:

Source https://stackoverflow.com/questions/51829810

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