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forecasting

This is the code for the paper, Written Justifications are Key to Aggregate Crowdsourced Forecasts.

Code Details

To install dependencies, pip install the libraries listed in requirements.txt.

Model.py contains 2 model architectures implemented using HuggingFace: The LSTM that did not concatenate question information (LSTM_Model), and the LSTM that did concatenate question information (LSTM_Model_With_Question)

Utils.py contains the code for processing the actual GJO Questions (which are found in data/), and creating the train/dev/test splits. The actual train/dev/test splits I used are found in questions.save.

Train.py contains the code for initializing all the hyperparameters of the model and the code for the training and testing loops/printing out the results.

To replicate, run utils.py then train.py.

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