https://github.com/hamelsmu/seq2seq_tutorial

Code For Medium Article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"
https://github.com/hamelsmu/seq2seq_tutorial

data-science deep-learning deeplearning keras keras-tutorials machine-learning medium-article nlp-machine-learning rnn-encoder-decoder seq2seq-tutorial sequence-to-sequence

Last synced: 9 days ago
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Code For Medium Article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"

• Host: GitHub
• URL: https://github.com/hamelsmu/seq2seq_tutorial
• Owner: hamelsmu
• License: apache-2.0
• Created: 2018-01-14T01:38:27.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-07T23:43:25.000Z (almost 2 years ago)
• Last Synced: 2024-10-16T02:44:14.253Z (21 days ago)
• Topics: data-science, deep-learning, deeplearning, keras, keras-tutorials, machine-learning, medium-article, nlp-machine-learning, rnn-encoder-decoder, seq2seq-tutorial, sequence-to-sequence
• Language: Jupyter Notebook
• Homepage:
• Size: 103 KB
• Stars: 138
• Watchers: 7
• Forks: 50
• Open Issues: 30
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[![GitHub license](https://img.shields.io/github/license/hamelsmu/Seq2Seq_Tutorial.svg)](https://github.com/hamelsmu/Seq2Seq_Tutorial/blob/master/LICENSE)

## Sequence-to-Sequence Tutorial with Github Issues Data

Code For Medium Article: ["How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"](https://medium.com/@hamelhusain/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8)

## Installation

`pip install -r requirements.txt`

If you are using the AWS Deep Learning Ubuntu AMI, many of the required dependencies will already be installed,

so you only need to run:

```

source activate tensorflow_p36

pip install ktext annoy nltk pydot

```

See #4 below if you wish to run this tutorial using Docker.

## Resources:

1. [Tutorial Notebook](https://nbviewer.jupyter.org/github/hamelsmu/Seq2Seq_Tutorial/blob/master/notebooks/Tutorial.ipynb):  The Jupyter notebook that coincides with the Medium post.

2. [seq2seq_utils.py](./notebooks/seq2seq_utils.py):  convenience functions that are used in the tutorial notebook to make predictions.

3. [ktext](https://github.com/hamelsmu/ktext): this library is used in the tutorial to clean data.  This library can be installed with `pip`.  

4. [Nvidia Docker Container](https://hub.docker.com/r/hamelsmu/seq2seq_tutorial/): contains all libraries that are required to run the tutorial.  This container is built with Nvidia-Docker v1.0.  You can install Nvidia-Docker and run this container like so:

```

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update

sudo apt-get install nvidia-docker

sudo nvidia-docker run hamelsmu/seq2seq_tutorial

```

This should work with both Nvidia-Docker v1.0 and v2.0.