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|
| # Question answering example |
|
|
| This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library. |
| For straightforward use-cases you may be able to use this script without modification, although we have also |
| included comments in the code to indicate areas that you may need to adapt to your own projects. |
|
|
| ### Usage notes |
|
|
| Note that when contexts are long they may be split into multiple training cases, not all of which may contain |
| the answer span. |
|
|
| As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user |
| inputs as well. |
|
|
| ### 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. There are some issues surrounding |
| these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're |
| actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick |
| workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it. |
|
|
| ### Memory usage and data loading |
|
|
| One thing to note is that all data is loaded into memory in this script. Most question answering 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](https://www.tensorflow.org/guide/tpu). |
|
|
| ### Example command |
|
|
| ```bash |
| python run_qa.py \ |
| --model_name_or_path distilbert/distilbert-base-cased \ |
| --output_dir output \ |
| --dataset_name squad \ |
| --do_train \ |
| --do_eval |
| ``` |
|
|
