## Multiple Choice Based on the script [`run_swag.py`](). #### Fine-tuning on SWAG ```bash python examples/multiple-choice/run_swag.py \ --model_name_or_path roberta-base \ --do_train \ --do_eval \ --learning_rate 5e-5 \ --num_train_epochs 3 \ --output_dir /tmp/swag_base \ --per_gpu_eval_batch_size=16 \ --per_device_train_batch_size=16 \ --overwrite_output ``` Training with the defined hyper-parameters yields the following results: ``` ***** Eval results ***** eval_acc = 0.8338998300509847 eval_loss = 0.44457291918821606 ``` ## Tensorflow ```bash 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 [](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)