Attention based Bi-LSTM model & bert

SemEval 2010 Task 8

python==3.6
pytorch==1.5.0
transformers==2.1.0
tqdm
nltk
numpy
pandas

place pretrained glove embedding in folder "code/vector_cache/" for att_bilstm model. example: "relation_classification/code/vector_cache/glove.6B.200d.txt"

python main.py --model att_bilstm --nums_train_epochs 50 --train_batch_size 64 --learning_rate 0.005 --embedding_dim 200 --hidden_dim 400 --save_model ./model/ --output_dir ./out/

python main.py --model bert --num_train_epochs 10 --learning_rate 0.00005 --train_batch_size 64 --save_model ./model/ --output_dir ./out/

Micro-averaged result (excluding Other): P = 1822/2281 = 79.88% R = 1822/2263 = 80.51% F1 = 80.19%

MACRO-averaged result (excluding Other): P = 79.43% R = 79.25% F1 = 79.10%

Micro-averaged result (excluding Other): P = 2014/2339 = 86.11% R = 2014/2263 = 89.00% F1 = 87.53%

MACRO-averaged result (excluding Other): P = 85.45% R = 88.93% F1 = 87.02%