Add TF multiple choice example

examples/tensorflow/multiple-choice/README.md

@@ -1,5 +1,5 @@
 <!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
+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.
@@ -13,26 +13,31 @@ 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.
 -->
+# Multiple-choice training (e.g. SWAG)
 
-# Multiple Choice
+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.
 
-## Fine-tuning on SWAG
+### 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](https://www.tensorflow.org/guide/tpu).
+
+### Example command
 ```bash
-export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/multiple-choice/run_tf_multiple_choice.py \
---task_name swag \
---model_name_or_path bert-base-cased \
---do_train \
---do_eval \
---data_dir $SWAG_DIR \
---learning_rate 5e-5 \
---num_train_epochs 3 \
---max_seq_length 80 \
---output_dir models_bert/swag_base \
---per_gpu_eval_batch_size=16 \
---per_device_train_batch_size=16 \
---logging-dir logs \
---gradient_accumulation_steps 2 \
---overwrite_output
+python run_swag.py \
+ --model_name_or_path distilbert-base-cased \
+ --output_dir output \
+ --do_eval \
+ --do_train
 ```