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* push to show * small improvement * small improvement * Update src/transformers/feature_extraction_utils.py * Update src/transformers/feature_extraction_utils.py * implement base * add common tests * make all tests pass for wav2vec2 * make padding work & add more tests * finalize feature extractor utils * add call method to feature extraction * finalize feature processor * finish tokenizer * finish general processor design * finish tests * typo * remove bogus file * finish docstring * add docs * finish docs * small fix * correct docs * save intermediate * load changes * apply changes * apply changes to doc * change tests * apply surajs recommend * final changes * Apply suggestions from code review * fix typo * fix import * correct docstring |
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|---|---|---|
| .. | ||
| README.md | ||
| requirements.txt | ||
| run_swag.py | ||
| run_tf_multiple_choice.py | ||
| utils_multiple_choice.py | ||
Multiple Choice
Based on the script run_swag.py.
Fine-tuning on SWAG
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
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
