Benchmark of knowledge-augmented pre-trained language models for biomedical relation extraction

This repository contains source code to run benchmarks for knowledge-augmented pre-trained language models for biomedical relation extraction.

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Installation

First, download the repository and change into the directory.

git clone https://github.com/mariosaenger/biore-kplm-benchmark
cd biore-kplm-benchmark

Setup a virtual environment, using conda (or a framework of your choice):

conda create -n biore-kplm
conda activate biore-kplm

Install all necessary packages:

pip install -r requirements.txt

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Usage

Experiment configuration

The code uses Hydra for experiment configuration and grid search for hyperparameter evaluation. The default configuration is given in _configs/config.yaml. Each subfolder in _configs contains alternative configurations for different experimental aspects:

• callbacks: Callbacks (e.g. checkpointing) to be used during experiment execution
• context_info: Configurations of context information to be used
• data: Dataset for which the benchmark should be executed
• hydra: Configuration options of the Hydra framework (e.g. output and logging directory)
• logger: Logger (e.g., csv, wandb, comet) to used during experiment execution
• model: Model to be tested
• trainer: Options for the trainer (e.g., cpu or gpu) used

All configurations can also be overridden while calling the program (see Hydra reference manual)

Experiment execution

Experiments can be executed (using the configuration in _configs/config.yaml) with:

python -m kplmb.train

Default configuration options can be overridden via program parameters:

python -m kplmb.train model=pubmedbert-ft model.lr=3e-5 batch_size=16

To run multiple experiments at once --multi-run can be used. For instance, the following call runs 18 experiments testing 2 different learning rates, 3 different batch sizes and 3 different max lengths:

python -m kplmb.train --multirun \
	model=pubmedbert-ft \
	model.lr=3e-5,5e-5 \
	model.max_length=256,384,512 \
	batch_size=8,16,32

For the available configuration options see the configuration files in _configs.