DoSSIER @ SDP2022
$ conda create --name sdp2022 python==3.9.12
$ pip install -r requirements.txtInstall the sdp2022 package:
$ pip install -e .Add your CORE API key into data/api_key.txt file
Install spacy language model:
$ python -m spacy download en_core_web_sm
├── LICENSE
├── README.md <- The top-level README for developers using this project.
│
├── config <- The YML configuration files containing training and prediction params.
│
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── raw <- The original, immutable data dump.
│ └── external <- Additional data resources
│
├── 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.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
└── sdp2022 <- Source code for use in this project.
│
├── data <- Scripts to download or generate data
│
├── utils <- Scripts utilities used during data generation or training
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├── training <- Scripts to train models
│
├── validate <- Scripts to validate models
│
└── visualization <- Scripts to create exploratory and results oriented visualizations