drop_bert

This repository contains the code for our CS 287 spring 2019 final project.

The code requires allennlp version 0.8.3, which can be installed by running pip install allennlp==0.8.3. When we set up our environment, we followed the instructions at the allennlp repo here.

The notebooks folder contains notebooks for generating specs for the DROP dataset and metrics for a trained model, as well as a sample notebook containing a sample allennlp train command we used to train our models.

The drop_bert folder contains our models.

• augmented_bert.py contains the NABERT base model
• augmented_bert_plus.py contains the NABERT+ model with only standard numbers
• augmented_bert_templated.py contains the full NABERT+ model (with standard numbers as well as templating)
• data_processing.py contains the code for processing the raw DROP JSON files into forms that can be fed into our models.

The config folder contains the JSON configurations needed to run the models using allennlp train. We assume that the train and dev data are in data/drop_dataset_train.json and data/drop_dataset_dev.json, respectively, except for the pickle example, where we expect pkl files instead of json.

• naqanet.json contains the config we used to train a version of the NAQANet model
• nabert.json contains a config for the basic NABERT model
• nabert-pickle.json contains a config for the basic NABERT model, except where a dataset reader has already been used to read in the data to allennlp Instances and the result from there saved to pickle files (this is useful if you don't want to re-run the dataset reader each time)
• nabert-attn.json contains a config for the basic NABERT model showing how to set a number representation different from the default first tag representation
• nabert-plus.json contains a config for NABERT+ with only standard numbers, where the standard numbers in the example are 100 and 1
• nabert-plus-templated.json contains a config for NABERT+ with standard numbers and templates, where the standard numbers in the example are 100 and 1