# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# 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
```
python run_qa.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name squad \
--do_train \
--do_eval \
```

<!---
Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# 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
```
python run_qa.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name squad \
--do_train \
--do_eval \
```
