#Supporting code for paper Ablations over Transformer Models for Biomedical Relationship Extraction

This repo is a fork of HuggingFace/Transformers, with some extensions to demonstrate the code used in the paper Ablations over Transformer Models for Biomedical Relationship Extraction.

run_semeval.py: relationship classification using R-Bert

R-BERT is a relationship classification head for BERT and RoBERTa, described here.

This example code fine-tunes R-BERT on the semeval 2010 Task 8 dataset:

python ./examples/run_semeval.py \
--data_dir $SEMEVAL_DIR \
--output_dir $RESULTS_DIR \
--model_name_or_path bert-base-uncased \
--do_train \
--do_eval \
--overwrite_output_dir \
--num_train_epochs 8.0 \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 16 \
--learning_rate 2e-5 \
--max_seq_length 128 \
--task_name semeval2010_task8 \
--train_on_other_labels \
--eval_on_other_labels \
--include_directionality

The $SEMEVAL_DIR should point to the extracted archive.

The --include_directionality flag trains a classifier using all 18 semeval classes. The --train_on_other_labels and --eval_on_other_labels flags also include instances labeled as 'Other' in the training and evaluation respectively. Include all of these to be able to use the official evaluation script.

Note, although an F1 score is calculated in the python code, two additional files are also written out at the checkpoint intervals {global_step}_semeval_results.tsv that may be used with the official Semeval evaluation script (supplied in the semeval data archive). The dataset is available under Creative Commons Atrribution 3.0 Unported Licence (http://creativecommons.org/licenses/by/3.0/) and is available here.

for example, using BERT:

./semeval2010_task8_scorer-v1.2.pl $SEMEVAL_DIR/{global_step}_semeval_results.tsv $SEMEVAL_DIR/TEST_FILE_SEMEVAL_SCRIPT_FORMAT.tsv 

However, the RoBERTa model can also be used with this head:

python ./examples/run_semeval.py \
--data_dir $SEMEVAL_DIR \
--output_dir $RESULTS_DIR \
--model_name_or_path roberta-large \
--model_type roberta \
--do_train \
--do_eval \
--overwrite_output_dir \
--num_train_epochs 8.0 \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 16 \
--learning_rate 2e-5 \
--max_seq_length 128 \
--task_name semeval2010_task8 \
--train_on_other_labels \
--eval_on_other_labels \
--include_directionality