Natural Language Processing course project

1. Download the dataset from the DaDoEval competition website
2. Put the training set, test set, and gold file folder into the data folder in this repository.
3. Install the conda environment with conda env create -f environment.yml. If you don't have conda install it from here.
4. Run the preprocessing scripts into src/utils to preprocess the data. Example (from the src folder): python -m utils.preprocess_train_data
5. Train the model with python train.py. The default strategy is Umberto with truncation of the first 512 tokens and embedding derived from the sum of the embedding of the [CLS] token of the last four layers.

• By now the change of parameters is manual and embedded in the code. I'll provide an automatic way of training a given model without directly changing the code.
• A GPU is not required but recommended.
• On a NVidia Titan X GPU the training took about 10 minutes.

If you want to know more details about the project and the tests, have a look at the report here.