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@ -817,7 +817,10 @@ python run_lm_finetuning.py \
### OpenAI GPT and Transformer-XL: running the examples
We provied two examples of scripts for OpenAI GPT and Transformer-XL based on (and extended from) the respective original implementations:
We provide two examples of scripts for OpenAI GPT and Transformer-XL based on (and extended from) the respective original implementations:
- fine-tuning OpenAI GPT on the ROCStories dataset
- evaluating Transformer-XL on Wikitext 103
#### Fine-tuning OpenAI GPT on the RocStories dataset
@ -829,21 +832,28 @@ Before running this example you should download the
```shell
export ROC_STORIES_DIR=/path/to/RocStories
python train_openai_gpt.py \
--task_name MRPC \
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--bert_model bert-base-uncased \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
--train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
--eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
--output_dir ../log \
--train_batch_size 16 \
```
Our test ran on a few seeds with [the original implementation hyper-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation results between 84% and 88%.
This command run in about 10 min on a single K-80 an gives an evaluation accuracy of 86.42% (the authors reports a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%).
#### Evaluating the pre-trained Transformer-XL on the WikiText 103 dataset
This example code evaluate the pre-trained Transformer-XL on the WikiText 103 dataset.
This command will download a pre-processed version of the WikiText 103 dataset in which the vocabulary has been computed.
```shell
python run_transfo_xl.py --work_dir ../log
```
This command run in about 10 min on a single K-80 an gives an evaluation accuracy of 86.42% (the authors reports a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%).
## Fine-tuning BERT-large on GPUs