BIG Reorganize examples (#4213)

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* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

examples/adversarial/README.md

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+## Adversarial evaluation of model performances
+
+Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
+
+The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
+
+This is an example of using test_hans.py:
+
+```bash
+export HANS_DIR=path-to-hans
+export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
+export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
+
+python examples/hans/test_hans.py \
+        --task_name hans \
+        --model_type $MODEL_TYPE \
+        --do_eval \
+        --data_dir $HANS_DIR \
+        --model_name_or_path $MODEL_PATH \
+        --max_seq_length 128 \
+        --output_dir $MODEL_PATH \
+```
+
+This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
+
+The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
+
+```bash
+Heuristic entailed results:
+lexical_overlap: 0.9702
+subsequence: 0.9942
+constituent: 0.9962
+
+Heuristic non-entailed results:
+lexical_overlap: 0.199
+subsequence: 0.0396
+constituent: 0.118
+```