Waqas Ali Vivek Bhatnagar Adam Childs
After pulling, unzip the .zip file found in ./data/raw
If working locally, be sure 'LOCAL = True' is set in the notebook. Set this False when running on Kaggle.
Project Requirements https://docs.google.com/document/d/1rfd54BVXDzj3awGkZU6HrT7UyTSfQs09zED-2W1oRoY/edit
Project Slides Intro https://docs.google.com/presentation/d/1U2K5zC758AGo8IPGuEOsbO9HzYp78SHiZ298NhKo5WM/edit?usp=sharing
Feedback Prize - Predicting Effective Arguments | Kaggle. Kaggle.com. Published 2022. Accessed July 12, 2022. https://www.kaggle.com/competitions/feedback-prize-effectiveness/data.
argumentation_scheme_and_rubrics_kaggle.docx. argumentation_scheme_and_rubrics_kaggle.docx. Google Docs. Published 2022. Accessed July 12, 2022. https://docs.google.com/document/d/1G51Ulb0i-nKCRQSs4p4ujauy4wjAJOae/edit
Presentation on the Corpus from which the contest data is drawn (PERSUADE corpus): https://www.youtube.com/watch?v=AETWJWL2M5Q
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io