{"id":13467731,"url":"https://github.com/hamelsmu/Seq2Seq_Tutorial","last_synced_at":"2025-03-26T03:30:58.603Z","repository":{"id":28291759,"uuid":"117392811","full_name":"hamelsmu/Seq2Seq_Tutorial","owner":"hamelsmu","description":"Code For Medium Article \"How To Create Data Products That Are Magical Using Sequence-to-Sequence Models\"","archived":false,"fork":false,"pushed_at":"2022-12-07T23:43:25.000Z","size":105,"stargazers_count":138,"open_issues_count":30,"forks_count":50,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-03-19T21:31:35.621Z","etag":null,"topics":["data-science","deep-learning","deeplearning","keras","keras-tutorials","machine-learning","medium-article","nlp-machine-learning","rnn-encoder-decoder","seq2seq-tutorial","sequence-to-sequence"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hamelsmu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-01-14T01:38:27.000Z","updated_at":"2024-01-04T16:19:59.000Z","dependencies_parsed_at":"2023-01-14T08:34:04.835Z","dependency_job_id":null,"html_url":"https://github.com/hamelsmu/Seq2Seq_Tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamelsmu%2FSeq2Seq_Tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamelsmu%2FSeq2Seq_Tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamelsmu%2FSeq2Seq_Tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamelsmu%2FSeq2Seq_Tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hamelsmu","download_url":"https://codeload.github.com/hamelsmu/Seq2Seq_Tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245584609,"owners_count":20639585,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","deep-learning","deeplearning","keras","keras-tutorials","machine-learning","medium-article","nlp-machine-learning","rnn-encoder-decoder","seq2seq-tutorial","sequence-to-sequence"],"created_at":"2024-07-31T15:00:59.839Z","updated_at":"2025-03-26T03:30:58.338Z","avatar_url":"https://github.com/hamelsmu.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"[![GitHub license](https://img.shields.io/github/license/hamelsmu/Seq2Seq_Tutorial.svg)](https://github.com/hamelsmu/Seq2Seq_Tutorial/blob/master/LICENSE)\n\n## Sequence-to-Sequence Tutorial with Github Issues Data\nCode 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)\n\n## Installation\n\n`pip install -r requirements.txt`\n\nIf you are using the AWS Deep Learning Ubuntu AMI, many of the required dependencies will already be installed,\nso you only need to run:\n\n```\nsource activate tensorflow_p36\npip install ktext annoy nltk pydot\n```\n\nSee #4 below if you wish to run this tutorial using Docker.\n\n\n## Resources:\n\n1. [Tutorial Notebook](https://nbviewer.jupyter.org/github/hamelsmu/Seq2Seq_Tutorial/blob/master/notebooks/Tutorial.ipynb):  The Jupyter notebook that coincides with the Medium post.\n\n2. [seq2seq_utils.py](./notebooks/seq2seq_utils.py):  convenience functions that are used in the tutorial notebook to make predictions.\n\n3. [ktext](https://github.com/hamelsmu/ktext): this library is used in the tutorial to clean data.  This library can be installed with `pip`.  \n\n4. [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:\n\n\n```\ncurl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -\ndistribution=$(. /etc/os-release;echo $ID$VERSION_ID)\ncurl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list\nsudo apt-get update\nsudo apt-get install nvidia-docker\n\nsudo nvidia-docker run hamelsmu/seq2seq_tutorial\n\n```\n\nThis should work with both Nvidia-Docker v1.0 and v2.0.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamelsmu%2FSeq2Seq_Tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhamelsmu%2FSeq2Seq_Tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamelsmu%2FSeq2Seq_Tutorial/lists"}