# ======Generate data variables========

task=re

input_dir=data/

ade_dir=ade_corpus/

target_dir=biobert_re/dataset/

max_seq_len=128

dev_split=0.1

tokenizer=biobert-base

file_ext=tsv

sep=tab

# ========BioBERT NER training variables========

ner_biobert_save_dir=./output

ner_biobert_data_dir=./dataset

ner_biobert_model_name=dmis-lab/biobert-large-cased-v1.1

ner_biobert_max_len=128

ner_biobert_batch_size=8

ner_biobert_epochs=1

ner_biobert_save_steps=4000

ner_biobert_seed=0

# ========BioBERT RE training variables========

re_biobert_save_dir=./output

re_biobert_data_dir=./dataset

re_biobert_model_name=dmis-lab/biobert-base-cased-v1.1

re_biobert_config_name=bert-base-cased

re_biobert_max_len=128

re_biobert_batch_size=8

re_biobert_epochs=3

re_biobert_save_steps=6264

re_biobert_seed=1

re_biobert_lr=5e-5

# ========FastAPI========

fast_api_fname=fast_api

# Generates data

generate-data:

    python generate_data.py \

    --task ${task} \

    --input_dir ${input_dir} \

    --ade_dir ${ade_dir} \

    --target_dir ${target_dir} \

    --max_seq_len ${max_seq_len} \

    --dev_split ${dev_split} \

    --tokenizer ${tokenizer} \

    --ext ${file_ext} \

    --sep ${sep}

# Trains BioBERT NER model

train-biobert-ner:

    cd biobert_ner/ && \

    python run_ner.py \

    --data_dir ${ner_biobert_data_dir}/ \

    --labels ${ner_biobert_data_dir}/labels.txt \

    --model_name_or_path ${ner_biobert_model_name} \

    --output_dir ${ner_biobert_save_dir}/ \

    --max_seq_length ${ner_biobert_max_len} \

    --num_train_epochs ${ner_biobert_epochs} \

    --per_device_train_batch_size ${ner_biobert_batch_size} \

    --save_steps ${ner_biobert_save_steps} \

    --seed ${ner_biobert_seed} \

    --do_train \

    --do_eval \

    --do_predict \

    --overwrite_output_dir

# Trains the BiLSTM NER model

train-bilstm:

    cd bilstm_crf_ner && \

    python train.py

# Trains BioBERT RE model

train-biobert-re:

    cd biobert_re/ && \

    python run_re.py \

    --task_name ehr-re \

    --config_name ${re_biobert_config_name} \

    --data_dir ${re_biobert_data_dir} \

    --model_name_or_path ${re_biobert_model_name} \

    --max_seq_length ${re_biobert_max_len} \

    --num_train_epochs ${re_biobert_epochs} \

    --per_device_train_batch_size ${re_biobert_batch_size} \

    --save_steps ${re_biobert_save_steps} \

    --seed ${re_biobert_seed} \

    --do_train \

    --do_eval \

    --do_predict \

    --learning_rate ${re_biobert_lr} \

    --output_dir ${re_biobert_save_dir} \

    --overwrite_output_dir

# Starts the FastAPI server in debug mode

start-api-local:

    uvicorn ${fast_api_fname}:app --reload

# Starts api on GCP

start-api-gcp:

    gunicorn -b 0.0.0.0:8000 -w 4 -k uvicorn.workers.UvicornWorker fast_api:app --timeout 300 --daemon