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+# BioBERT for NER
+To train an NER model with BioBERT-v1.1 (base), run the command below.
+<br>
+Before running this, make sure you have generated the pre-processed dataset using the generate_data.py file with the command mentioned in the parent directory. 
+
+## Additional Requirements
+- seqeval: Used for NER evaluation (```pip install seqeval```)
+
+## Training
+```
+export SAVE_DIR=./output
+export DATA_DIR=./dataset
+
+export MAX_LENGTH=128
+export BATCH_SIZE=16
+export NUM_EPOCHS=5
+export SAVE_STEPS=1000
+export SEED=0
+
+python run_ner.py \
+    --data_dir ${DATA_DIR}/ \
+    --labels ${DATA_DIR}/labels.txt \
+    --model_name_or_path dmis-lab/biobert-large-cased-v1.1 \
+    --output_dir ${SAVE_DIR}/ \
+    --max_seq_length ${MAX_LENGTH} \
+    --num_train_epochs ${NUM_EPOCHS} \
+    --per_device_train_batch_size ${BATCH_SIZE} \
+    --save_steps ${SAVE_STEPS} \
+    --seed ${SEED} \
+    --do_train \
+    --do_eval \
+    --do_predict \
+    --overwrite_output_dir
+```
+
+## Results
+|             | precision |   recall | f1-score |
+|:---:|:---:|:---:|:---:|
+|         ADE |      0.6351 |     0.5680|      0.5997|
+|       Dosage |      0.9254  |    0.9485  |    0.9368   |
+|        Drug |      0.9580  |    0.9542  |    0.9561   |
+|         Duration |      0.8119  |    0.9021  |    0.8546   |
+|         Form |      0.9546  |    0.9456  |    0.9501   |
+|         Frequency |      0.9707  |    0.9668  |    0.9688   |
+|         Reason |      0.7203  |    0.7348  |    0.7275   |
+|         Route |      0.9530  |    0.9525  |    0.9527   |
+|         Strength |      0.9807  |    0.9846  |    0.9827   |
+|   micro avg |      0.9327  |    0.9330  |    0.9328   |
+|   macro avg |      0.9253  |    0.9225  |    0.9230   |
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