seq2seq | Minimal Seq2Seq model with Attention for Neural Machine | Translation library

by keon Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | seq2seq Summary

seq2seq is a Python library typically used in Utilities, Translation, Pytorch, Neural Network, Transformer applications. seq2seq has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However seq2seq build file is not available. You can download it from GitHub.

Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

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Support

seq2seq has a highly active ecosystem.

It has 662 star(s) with 167 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 11 open issues and 9 have been closed. On average issues are closed in 142 days. There are 1 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of seq2seq is current.

Quality

seq2seq has 0 bugs and 0 code smells.

Security

seq2seq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

seq2seq code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

seq2seq is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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seq2seq releases are not available. You will need to build from source code and install.

seq2seq has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed seq2seq and discovered the below as its top functions. This is intended to give you an instant insight into seq2seq implemented functionality, and help decide if they suit your requirements.

Train model
Loads a training dataset
Evaluate the model
Parse hyperparameters
Compute the attention layer
Compute the energy of the model

Get all kandi verified functions for this library.

seq2seq Key Features

No Key Features are available at this moment for seq2seq.

seq2seq Examples and Code Snippets

Create a tf . seq2seq .

python

Lines of Code : 39

License : Permissive (MIT License)

Copy

def _create_loss(self):
        print('Creating loss... \nIt might take a couple of minutes depending on how many buckets you have.')
        start = time.time()
        def _seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
            setattr(t

Community Discussions

Trending Discussions on seq2seq

Create iterator from a Data Frame in Python

Tensorflow seq2seq - keep max three checkpoints not working

Tensorflow addons seq2seq output of BasicDecoder call (tfa.seq2seq)

Tensorflow's seq2seq: tensorflow.python.framework.errors_impl.InvalidArgumentError

Simple Transformers producing nothing?

Sagemaker Instance not utilising GPU during training

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

The role of initial state of lstm layer in seq2seq encoder

Save model after each epoch - AllenNLP

AllenNLP - dataset_reader config for transformers

QUESTION

Create iterator from a Data Frame in Python

Asked 2022-Mar-17 at 20:13

I am working on an NLP project using Seq2Seq. I created a data frame from my dataset then created a batch iterator using data loader, see the following code:

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ANSWER

Answered 2022-Mar-17 at 20:13

You can redefine __getitem__ in your Dataset to return a dictionary:

Source https://stackoverflow.com/questions/71515161

QUESTION

Tensorflow seq2seq - keep max three checkpoints not working

Asked 2022-Mar-08 at 08:30

I am writing a seq2seq and would like to keep only three checkpoints; I thought I was implementing this with

...

ANSWER

Answered 2022-Mar-08 at 07:10

Hmm maybe you should try restoring your checkpoint every time you begin training again:

Source https://stackoverflow.com/questions/71387565

QUESTION

Tensorflow addons seq2seq output of BasicDecoder call (tfa.seq2seq)

Asked 2022-Mar-01 at 11:58

Building a seq2seq based on tfa.seq2seq, basically works like in https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#train_the_model. I am looking at the nature of the outputs when calling a BasicDecoder. I create an instance of decoder

...

ANSWER

Answered 2022-Mar-01 at 11:58

Using class GreedyEmbeddingSampler(Sampler): for inference https://github.com/tensorflow/addons/blob/v0.15.0/tensorflow_addons/seq2seq/sampler.py#L559-L650

Source https://stackoverflow.com/questions/71292414

QUESTION

Tensorflow's seq2seq: tensorflow.python.framework.errors_impl.InvalidArgumentError

Asked 2022-Feb-28 at 06:26

I am following quite closely the Seq2seq for translation tutorial here https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#define_the_optimizer_and_the_loss_function while testing on other data. I meet an error when instantiating the Encoder which is defined as

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ANSWER

Answered 2022-Feb-27 at 18:15

This error occurs when you have a sequence which contains integer values outside the range of the defined vocabulary size. You can reproduce your error with the following example, because the vocabulary size of the Embedding layer is 106, meaning sequences can have values between 0 and 105 and I pass a random sequence with values between 0 and 200 to enforce an error:

Source https://stackoverflow.com/questions/71286714

QUESTION

Simple Transformers producing nothing?

Asked 2022-Feb-22 at 11:54

I have a simple transformers script looking like this.

...

ANSWER

Answered 2022-Feb-22 at 11:54

Use this model instead.

Source https://stackoverflow.com/questions/71200243

QUESTION

Sagemaker Instance not utilising GPU during training

Asked 2022-Jan-03 at 11:19

I'm training a Seq2Seq model on Tensorflow on a ml.p3.2xlarge instance. When I tried running the code on google colab, the time per epoch was around 40 mins. However on the instance it's around 5 hours!

This is my training code

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ANSWER

Answered 2021-Aug-13 at 16:35

If you're using SageMaker Notebook instance. Open a terminal and run nvidia-smi to see the GPU utilization rate. If you it's 0% then you're not using the right device. If it's more than 0% but very far from 100%, then you have a non GPU bottleneck to handle.
If you're using SageMaker training, then check the GPU usage via Cloudwatch metrics for the job.

Source https://stackoverflow.com/questions/68741326

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

The role of initial state of lstm layer in seq2seq encoder

Asked 2021-May-16 at 18:34

I am trying to follow this guide to implement a seq2seq machine tranlsation model: https://www.tensorflow.org/tutorials/text/nmt_with_attention

The tutorial's Encoder has an initialize_hidden_state() function that is used to generate all 0 as initial state for the encoder. However I am a bit confused as to why this is neccessary. As far as I can tell, the only times when encoder is called (in train_step and evaluate), they were initialized with the initialize_hidden_state() function. My questions are 1.) what is the purpose of this initial state? Doesn't Keras layer automatically initialize LSTM states to begin with? And 2.) why not always just initialize the encoder with all 0 hidden states if encoder is always called with initial states generated by initialize_hidden_state()?

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ANSWER

Answered 2021-May-16 at 18:34

you are totally right. The code in the example is a little misleading. The LSTM cells are automatically initialized with zeros. You can just delete the initialize_hidden_state() function.

Source https://stackoverflow.com/questions/67351642

QUESTION

Save model after each epoch - AllenNLP

Asked 2021-May-06 at 23:03

Is there a parameter that I can set in the config file (maybe for the trainer?) that would save the model (archive) after each epoch or after a specific number of steps? I'm using seq2seq dataloader and "composed_seq2seq" as my model. This is how my trainer looks like currently:

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ANSWER

Answered 2021-May-06 at 23:03

Can you explain a little more about what you're trying to do with a model from every epoch/some number of steps? I think it already archives the model every time it gets a new best score, so I'm wondering what you want to do that can't be accomplished with that.

Edit:

It looks like AllenNLP already saves a model every epoch, but it only keeps a maximum of 2 by default. I believe you can change that by adding a checkpointer to your training config, e.g.:

Source https://stackoverflow.com/questions/67360264

QUESTION

AllenNLP - dataset_reader config for transformers

Asked 2021-Apr-29 at 17:28

I would like to use bert for tokenization and also indexing for a seq2seq model and this is how my config file looks like so far:

...

ANSWER

Answered 2021-Apr-29 at 17:28

Please set add_special_tokens = False.
Use tokenizer.convert_tokens_to_string (which takes the list of subword tokens as input), where tokenizer refers to the tokenizer used by your DatasetReader.

Please let us know if you have further questions!

Source https://stackoverflow.com/questions/67306841

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

Vulnerabilities

No vulnerabilities reported

Install seq2seq

You can download it from GitHub.
You can use seq2seq 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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