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To train an NER model with BioBERT-v1.1 (base), run the command below.
Before training, please run ./preprocess.sh
to preprocess the datasets downloaded in biobert-pytorch
(see here).
pip install seqeval
)export SAVE_DIR=./output
export DATA_DIR=../datasets/NER
export MAX_LENGTH=192
export BATCH_SIZE=32
export NUM_EPOCHS=30
export SAVE_STEPS=1000
export ENTITY=NCBI-disease
export SEED=1
python run_ner.py \
--data_dir ${DATA_DIR}/${ENTITY}/ \
--labels ${DATA_DIR}/${ENTITY}/labels.txt \
--model_name_or_path dmis-lab/biobert-base-cased-v1.1 \
--output_dir ${SAVE_DIR}/${ENTITY} \
--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
Test Precision (%) | Test Recall (%) | Test F1 (%) | |
---|---|---|---|
NCBI-disease | 86.96 | 89.68 | 88.30 |
BC5CDR-disease | 84.84 | 87.95 | 86.37 |
BC5CDR-chem | 92.66 | 93.42 | 93.04 |
BC4CHEMD | 91.74 | 90.57 | 91.15 |
JNLPBA | 70.36 | 82.58 | 75.98 |
BC2GM | 82.62 | 84.22 | 83.41 |
LINNAEUS | 92.14 | 84.36 | 88.08 |
S800 | 69.20 | 75.88 | 72.38 |
Test Precision (%) | Test Recall (%) | Test F1 (%) | |
---|---|---|---|
NCBI-disease | 83.44 | 87.18 | 85.27 |
BC5CDR-disease | 82.40 | 84.17 | 83.28 |
BC5CDR-chem | 91.71 | 91.86 | 91.78 |
BC4CHEMD | 90.42 | 88.62 | 89.51 |
JNLPBA | 69.78 | 81.70 | 75.27 |
BC2GM | 80.66 | 81.20 | 80.93 |
LINNAEUS | 89.26 | 78.36 | 83.46 |
S800 | 68.15 | 73.66 | 70.80 |
Test Precision (%) | Test Recall (%) | Test F1 (%) | |
---|---|---|---|
NCBI-disease | 84.65 | 85.62 | 85.13 |
BC5CDR-disease | 79.17 | 81.89 | 80.51 |
BC5CDR-chem | 90.36 | 88.43 | 89.38 |
BC4CHEMD | 89.51 | 86.25 | 87.85 |
JNLPBA | 71.03 | 80.44 | 75.44 |
BC2GM | 80.18 | 80.25 | 80.22 |
LINNAEUS | 89.49 | 80.25 | 84.62 |
S800 | 68.81 | 73.92 | 71.27 |
For help or issues using BioBERT-PyTorch, please create an issue and tag @minstar.
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