Partial order dependency parser

This repository contains the code for partial order dependency parser.

Setting Up The Environment

Set up a virtual environment and install the dependencies:

conda create -n environment_partial.yml
# activate the environment
conda activate partial

Getting The Data

Training

For running one single experiment:

CUDA_VISIBLE_DEVICES=0 python run.py train --lang English --tagger part --model bert --epochs 50 --batch-size 32 --lr 2e-5 --order-dim 2 --n-lstm-layers 0 --model-path bert-base-cased --output-path ./checkpoints/ --use-tensorboard True

This command will train a partial order dependency parser on English PTB. Model weights will be saved in ./checkpoints/.

For running experiments in batch:

bash run_order_dim.sh

run_order_dim.sh looks like this:

#!/bin/bash

# running experiments for k from 2 to 10
for k in {2..10}
do
    CUDA_VISIBLE_DEVICES=0 python run.py train --lang English --tagger part --model bert --epochs 50 --batch-size 32 --lr 2e-5 --order-dim $k --n-lstm-layers 0 --model-path bert-base-cased --output-path ./checkpoints/ --use-tensorboard True
done

Submitting job using slurm:

sbatch --account=es_cott --ntasks=4 --time=24:00:00 --mem-per-cpu=4096 --gpus="rtx_4090:1" run_order_dim.sh;