readme: - You'll need to instantiate a special RoBERTa class. Though technically a "Longformer", the elongated RoBERTa model will still need to be pulled in as such.
  - To do so, use the following classes:
  
  ```python
  class RobertaLongSelfAttention(LongformerSelfAttention):
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          output_attentions=False,
      ):
          return super().forward(hidden_states, attention_mask=attention_mask, output_attentions=output_attentions)
  
  class RobertaLongForMaskedLM(RobertaForMaskedLM):
      def __init__(self, config):
          super().__init__(config)
          for i, layer in enumerate(self.roberta.encoder.layer):
              # replace the `modeling_bert.BertSelfAttention` object with `LongformerSelfAttention`
              layer.attention.self = RobertaLongSelfAttention(config, layer_id=i)
  ```
  - Then, pull the model as ```RobertaLongForMaskedLM.from_pretrained('simonlevine/bioclinical-roberta-long')```
  - Now, it can be used as usual. Note you may get untrained weights warnings.
  - Note that you can replace ```RobertaForMaskedLM``` with a different task-specific RoBERTa from Huggingface, such as RobertaForSequenceClassification.
  