# Globally Normalized Reader This model implements the work in the following paper: Jonathan Raiman and John Miller. Globally Normalized Reader. Empirical Methods in Natural Language Processing (EMNLP), 2017. If you use the dataset/code in your research, please cite the above paper: ```text @inproceedings{raiman2015gnr, author={Raiman, Jonathan and Miller, John}, booktitle={Empirical Methods in Natural Language Processing (EMNLP)}, title={Globally Normalized Reader}, year={2017}, } ``` You can also visit https://github.com/baidu-research/GloballyNormalizedReader to get more information. # Installation 1. Please use [docker image](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html) to install the latest PaddlePaddle, by running: ```bash docker pull paddledev/paddle ``` 2. Download all necessary data by running: ```bash cd data && ./download.sh && cd .. ``` 3. Preprocess and featurizer data: ```bash python featurize.py --datadir data --outdir data/featurized --glove-path data/glove.840B.300d.txt ``` # Training a Model - Configurate the model by modifying `config.py` if needed, and then run: ```bash python train.py 2>&1 | tee train.log ``` # Inferring by a Trained Model - Infer by a trained model by running: ```bash python infer.py \ --model_path models/pass_00000.tar.gz \ --data_dir data/featurized/ \ --batch_size 2 \ --use_gpu 0 \ --trainer_count 1 \ 2>&1 | tee infer.log ```