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# Multiple-choice training (e.g. SWAG) |
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This folder contains the `run_swag.py` script, showing an examples of *multiple-choice answering* with the |
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🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, |
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although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. |
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### Multi-GPU and TPU usage |
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By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs |
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can also be used by passing the name of the TPU resource with the `--tpu` argument. |
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### Memory usage and data loading |
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One thing to note is that all data is loaded into memory in this script. Most multiple-choice datasets are small |
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enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle |
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data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data |
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required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and |
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README, but for more information you can see the 'Input Datasets' section of |
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[this document](https://www.tensorflow.org/guide/tpu). |
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### Example command |
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```bash |
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python run_swag.py \ |
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--model_name_or_path distilbert-base-cased \ |
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--output_dir output \ |
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--do_eval \ |
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--do_train |
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``` |
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