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# This is a convenience script for evaluating ALBERT on the GLUE benchmark. | |
# | |
# By default, this script uses a pretrained ALBERT v1 BASE model, but you may | |
# use a custom checkpoint or any compatible TF-Hub checkpoint with minimal | |
# edits to environment variables (see ALBERT_HUB_MODULE_HANDLE below). | |
# | |
# This script does fine-tuning and evaluation on 8 tasks, so it may take a | |
# while to complete if you do not have a hardware accelerator. | |
set -ex | |
python3 -m venv $HOME/albertenv | |
. $HOME/albertenv/bin/activate | |
OUTPUT_DIR_BASE="$(mktemp -d)" | |
OUTPUT_DIR="${OUTPUT_DIR_BASE}/output" | |
# To start from a custom pretrained checkpoint, set ALBERT_HUB_MODULE_HANDLE | |
# below to an empty string and set INIT_CHECKPOINT to your checkpoint path. | |
ALBERT_HUB_MODULE_HANDLE="https://tfhub.dev/google/albert_base/1" | |
INIT_CHECKPOINT="" | |
pip3 install --upgrade pip | |
pip3 install numpy | |
pip3 install -r requirements.txt | |
function run_task() { | |
COMMON_ARGS="--output_dir="${OUTPUT_DIR}/$1" --data_dir="${ALBERT_ROOT}/glue" --vocab_file="${ALBERT_ROOT}/vocab.txt" --spm_model_file="${ALBERT_ROOT}/30k-clean.model" --do_lower_case --max_seq_length=512 --optimizer=adamw --task_name=$1 --warmup_step=$2 --learning_rate=$3 --train_step=$4 --save_checkpoints_steps=$5 --train_batch_size=$6" | |
python3 -m run_classifier \ | |
${COMMON_ARGS} \ | |
--do_train \ | |
--nodo_eval \ | |
--nodo_predict \ | |
--albert_hub_module_handle="${ALBERT_HUB_MODULE_HANDLE}" \ | |
--init_checkpoint="${INIT_CHECKPOINT}" | |
python3 -m run_classifier \ | |
${COMMON_ARGS} \ | |
--nodo_train \ | |
--do_eval \ | |
--do_predict \ | |
--albert_hub_module_handle="${ALBERT_HUB_MODULE_HANDLE}" | |
} | |
run_task SST-2 1256 1e-5 20935 100 32 | |
run_task MNLI 1000 3e-5 10000 100 128 | |
run_task CoLA 320 1e-5 5336 100 16 | |
run_task QNLI 1986 1e-5 33112 200 32 | |
run_task QQP 1000 5e-5 14000 100 128 | |
run_task RTE 200 3e-5 800 100 32 | |
run_task STS-B 214 2e-5 3598 100 16 | |
run_task MRPC 200 2e-5 800 100 32 | |