text: Title: RoBERTa on SuperGLUE's 'Reading Comprehension with Commonsense Reasoning' task
  Body: ReCoRD is one of the tasks of the [SuperGLUE](https://super.gluebenchmark.com) benchmark. The task is to re-trace the steps of Facebook's RoBERTa paper (https://arxiv.org/pdf/1907.11692.pdf) and build an AllenNLP config that reads the ReCoRD data and fine-tunes a model on it. We expect scores in the range of their entry on the [SuperGLUE leaderboard](https://super.gluebenchmark.com/leaderboard).
  
  This is a span prediction task. You can use the existing [`TransformerQA` model](https://github.com/allenai/allennlp-models/blob/main/allennlp_models/rc/models/transformer_qa.py) and [dataset reader](https://github.com/allenai/allennlp-models/blob/main/allennlp_models/rc/dataset_readers/transformer_squad.py), or write your own in the style of [`TransformerClassificationTT`](https://github.com/allenai/allennlp-models/blob/Imdb/allennlp_models/classification/models/transformer_classification_tt.py). Or you write an AllenNLP model that's a thin wrapper around the Huggingface SQuAD model. All of these are valid approaches. To tie your components together, we recommend [the IMDB experiment config](https://github.com/allenai/allennlp-models/blob/Imdb/training_config/tango/imdb.jsonnet) as a starting point.