seq-to-seq | Sequence to Sequence Learning for chatbots | Chat library

by pranoyr Python Version: Current License: MIT

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

kandi X-RAY | seq-to-seq Summary

seq-to-seq is a Python library typically used in Messaging, Chat, Deep Learning, Tensorflow, Neural Network applications. seq-to-seq has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However seq-to-seq build file is not available. You can download it from GitHub.

Sequence to Sequence Learning for chatbots.

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Support

seq-to-seq has a low active ecosystem.

It has 11 star(s) with 4 fork(s). There are no 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 823 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of seq-to-seq is current.

Quality

seq-to-seq has 0 bugs and 0 code smells.

Security

seq-to-seq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

seq-to-seq code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

seq-to-seq 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

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

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

Predict sequence prediction
One hot encode sequence
Define training models
One - hot encode features
Invert sequence of words in sequence

Get all kandi verified functions for this library.

seq-to-seq Key Features

No Key Features are available at this moment for seq-to-seq.

seq-to-seq Examples and Code Snippets

No Code Snippets are available at this moment for seq-to-seq.

Community Discussions

Trending Discussions on seq-to-seq

Add attention layer to Seq2Seq model

Embedding layer in neural machine translation with attention

QUESTION

Add attention layer to Seq2Seq model

Asked 2020-Dec-11 at 00:55

I have build a Seq2Seq model of encoder-decoder. I want to add an attention layer to it. I tried adding attention layer through this but it didn't help.

Here is my initial code without attention

...

ANSWER

Answered 2020-Dec-11 at 00:55

the dot products need to be computed on tensor outputs... in encoder you correctly define the encoder_output, in decoder you have to add decoder_outputs, state_h, state_c = decoder_lstm(enc_emb, initial_state=encoder_states)

the dot products now are

Source https://stackoverflow.com/questions/62357239

QUESTION

Embedding layer in neural machine translation with attention

Asked 2020-Nov-04 at 07:02

I am trying to understanding how to implement a seq-to-seq model with attention from this website.

My question: Is nn.embedding just returns some IDs for each word, so the embedding for each word would be the same during whole training? Or are they getting changed during the procedure of training?

My second question is because I am confused whether after training, the output of nn.embedding is something such as word2vec word embeddings or not.

Thanks in advance

...

ANSWER

Answered 2020-Nov-04 at 07:02

According to the PyTorch docs:

A simple lookup table that stores embeddings of a fixed dictionary and size.

This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.

In short, nn.Embedding embeds a sequence of vocabulary indices into a new embedding space. You can indeed roughly understand this as a word2vec style mechanism.

As a dummy example, let's create an embedding layer that takes as input a total of 10 vocabularies (i.e. the input data only contains a total of 10 unique tokens), and returns embedded word vectors living in 5-dimensional space. In other words, each word is represented as 5-dimensional vectors. The dummy data is a sequence of 3 words with indices 1, 2, and 3, in that order.

Source https://stackoverflow.com/questions/64675228

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

Vulnerabilities

No vulnerabilities reported

Install seq-to-seq

You can download it from GitHub.
You can use seq-to-seq 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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