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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>
57 lines
1.5 KiB
Markdown
57 lines
1.5 KiB
Markdown
## Multiple Choice
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Based on the script [`run_multiple_choice.py`]().
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#### Fine-tuning on SWAG
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Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
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```bash
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#training on 4 tesla V100(16GB) GPUS
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export SWAG_DIR=/path/to/swag_data_dir
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python ./examples/multiple-choice/run_multiple_choice.py \
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--task_name swag \
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--model_name_or_path roberta-base \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--max_seq_length 80 \
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--output_dir models_bert/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_device_train_batch_size=16 \
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--gradient_accumulation_steps 2 \
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--overwrite_output
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```
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Training with the defined hyper-parameters yields the following results:
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```
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***** Eval results *****
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eval_acc = 0.8338998300509847
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eval_loss = 0.44457291918821606
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```
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## Tensorflow
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```bash
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export SWAG_DIR=/path/to/swag_data_dir
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python ./examples/multiple-choice/run_tf_multiple_choice.py \
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--task_name swag \
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--model_name_or_path bert-base-cased \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--max_seq_length 80 \
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--output_dir models_bert/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_device_train_batch_size=16 \
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--logging-dir logs \
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--gradient_accumulation_steps 2 \
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--overwrite_output
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```
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# Run it in colab
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[](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
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