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* pytorch examples
* pytorch mim no trainer
* cookiecutter
* flax examples
* missed line in pytorch run_glue
* tensorflow examples
* tensorflow run_clip
* tensorflow run_mlm
* tensorflow run_ner
* tensorflow run_clm
* pytorch example from_configs
* pytorch no trainer examples
* Revert "tensorflow run_clip"
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Multiple-choice training (e.g. SWAG)
This folder contains the run_swag.py script, showing an examples of multiple-choice answering with the
🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification,
although we have also included comments in the code to indicate areas that you may need to adapt to your own projects.
Multi-GPU and TPU usage
By default, the script uses a MirroredStrategy and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the --tpu argument.
Memory usage and data loading
One thing to note is that all data is loaded into memory in this script. Most multiple-choice datasets are small enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and README, but for more information you can see the 'Input Datasets' section of this document.
Example command
python run_swag.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--do_eval \
--do_train