https://github.com/rohitawate/theofficescriptgenerator
LSTM-powered language model to generate television scripts for "The Office".
https://github.com/rohitawate/theofficescriptgenerator
Last synced: over 1 year ago
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LSTM-powered language model to generate television scripts for "The Office".
- Host: GitHub
- URL: https://github.com/rohitawate/theofficescriptgenerator
- Owner: RohitAwate
- License: apache-2.0
- Created: 2023-04-18T00:52:01.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-04-20T03:15:01.000Z (over 3 years ago)
- Last Synced: 2025-01-21T00:50:04.183Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 3.71 MB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# The Office Script Generator
LSTM-powered language model to generate television scripts for "The Office".
By Rohit Awate, Shubham Bhagwat, Rohan Devasthale
---
## Abstract
This project aims to develop a natural language processing (NLP) model capable of generating a script for the popular TV series "The Office (US)". Utilizing dialogues from all nine seasons that aired on the show as a dataset, we experiment with various deep learning architectures, such as Bidirectional Long Short-Term Memory (LSTM) networks, deep neural networks. The resulting model will produce a script containing character names followed by their respective lines of dialogue, capturing the essence of the show's unique humor and style.
---
## Index of Deliverables
- [Final Jupyter notebook](Final_NLP_Project.ipynb)
- [Report](ProjectReport_ArtistsStatement.pdf)
- [Presentation Slides](NLP_Presentation_Slides.pdf)
- [Artist's statement](ArtistsStatement.pdf)
- The `experiments/` directory contains all the Jupyter notebooks that we worked on while working on this project.
- The `dataset/` directory contains the CSV dataset that we obtained from [Kaggle](https://www.kaggle.com/datasets/nasirkhalid24/the-office-us-complete-dialoguetranscript?resource=download).
---
## Disclaimer
This is a class project executed as a part of the Computer Science course CS6120 "Natural Language Processing" at Northeastern University. Not affiliated with NBCUniversal and other copyright holders.