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[SDP 2022] Benchmark for research theme classification of scholarly documents

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sdp2022

DoSSIER @ SDP2022

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Installation

$ conda create --name sdp2022 python==3.9.12
$ pip install -r requirements.txt

Install 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

Project Organization

├── 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
    │
    ├── training                            <- Scripts to train models
    │
    ├── validate                            <- Scripts to validate models
    │
    └── visualization                       <- Scripts to create exploratory and results oriented visualizations

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