{"id":20614454,"url":"https://github.com/junteudjio/amazon_reviews_summarizer","last_synced_at":"2025-07-10T16:09:19.587Z","repository":{"id":83850448,"uuid":"116334370","full_name":"junteudjio/amazon_reviews_summarizer","owner":"junteudjio","description":"A deep learning based text summarizer for Amazon reviews in 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 Amazon Reviews Summarizer (ars)\nA deep learning based text summarizer for Amazon reviews in tensorflow.\n\n## Synopsis\n\n- In this project, we use the amazon fine food reviews dataset to implement a text summarizers for these reviews.\n- The code is meant to be high quality, clean and flexible enough to handle testing different kinds of architecture easily.\n- To achieve this goal we use the same coding style as in the beautiful official tensorflow's example for neural machine translation,\nfrom which we have used/adapted several code snippets.\n\n\n## Architecture overview\n\n- A seq2seq model with bi-directional, multi layers RNN/GRU/LSTM cells.\n- Attention mechanism on the decoder.\n- 3 different graphs for train, evaluation and test modes (more work but makes code clean and fast).\n- used the beautiful dataset/iterator for input data feeding.\n- used glove vectors for embeddings initialization.\n\n\n## Requirements\n\nInstall all needed dependnecies through\n`pip install -r requirements.txt`.\nOr\n`python setup.py develop`.\n\n\n\n## Running the code\n\n\n- You can get started by downloading the datasets and doing some basic preprocessing:\n\n$ code/get_started.sh\n\nNote that you will always want to run your code from the \"ars\" directory, not the code directory, like so:\n\n$ python code/train.py\n\nThis ensures that any files created in the process don't pollute the code directoy.\n\n- Now train/evaluate/test the model by running :\n\n$ python code/run_ars.py\n\nchange the cmd line args to try different architecture flavours.\n\n\n## Contributors\n\n- junior Teudjio Mbativou : https://www.linkedin.com/in/junior-teudjio-3a125b8a\n\n\n# BibTex and Acknowledgment\n\n```\n@article{luong17,\n  author  = {Minh{-}Thang Luong and Eugene Brevdo and Rui Zhao},\n  title   = {Neural Machine Translation (seq2seq) Tutorial},\n  journal = {https://github.com/tensorflow/nmt},\n  year    = {2017},\n}\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjunteudjio%2Famazon_reviews_summarizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjunteudjio%2Famazon_reviews_summarizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjunteudjio%2Famazon_reviews_summarizer/lists"}