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transformers/examples/multiple-choice
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Julien Chaumond 7b75aa9fa5
[TPU] Doc, fix xla_spawn.py, only preprocess dataset once (#4223) 
* [TPU] Doc, fix xla_spawn.py, only preprocess dataset once

* Update examples/README.md

* [xla_spawn] Add `_mp_fn` to other Trainer scripts

* [TPU] Fix: eval dataloader was None
2020-05-08 14:10:05 -04:00
..
README.md [doc] Fix broken links + remove crazy big notebook 2020-05-07 18:44:18 -04:00
run_multiple_choice.py [TPU] Doc, fix xla_spawn.py, only preprocess dataset once (#4223) 2020-05-08 14:10:05 -04:00
utils_multiple_choice.py BIG Reorganize examples (#4213) 2020-05-07 13:48:44 -04:00

README.md

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_gpu_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