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* per_device instead of per_gpu/error thrown when argument unknown * [docs] Restore examples.md symlink * Correct absolute links so that symlink to the doc works correctly * Update src/transformers/hf_argparser.py Co-authored-by: Julien Chaumond <chaumond@gmail.com> * Warning + reorder * Docs * Style * not for squad Co-authored-by: Julien Chaumond <chaumond@gmail.com>
1.5 KiB
1.5 KiB
Multiple Choice
Based on the script run_multiple_choice.py.
Fine-tuning on SWAG
Download swag data
#training on 4 tesla V100(16GB) GPUS
export SWAG_DIR=/path/to/swag_data_dir
python ./examples/multiple-choice/run_multiple_choice.py \
--task_name swag \
--model_name_or_path roberta-base \
--do_train \
--do_eval \
--data_dir $SWAG_DIR \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--max_seq_length 80 \
--output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \
--per_device_train_batch_size=16 \
--gradient_accumulation_steps 2 \
--overwrite_output
Training with the defined hyper-parameters yields the following results:
***** Eval results *****
eval_acc = 0.8338998300509847
eval_loss = 0.44457291918821606
Tensorflow
export SWAG_DIR=/path/to/swag_data_dir
python ./examples/multiple-choice/run_tf_multiple_choice.py \
--task_name swag \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--data_dir $SWAG_DIR \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--max_seq_length 80 \
--output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \
--per_device_train_batch_size=16 \
--logging-dir logs \
--gradient_accumulation_steps 2 \
--overwrite_output
Run it in colab
