day 100 of reporting, still getting this error 
---------------------------------------------------------------------------

IndexError                                Traceback (most recent call last)

<ipython-input-38-dda642f3d8b6> in <module>()
     47     )
     48 
---> 49 train_results = trainer.train()

11 frames

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, **kwargs)
   1118                         tr_loss += self.training_step(model, inputs)
   1119                 else:
-> 1120                     tr_loss += self.training_step(model, inputs)
   1121                 self._total_flos += float(self.floating_point_ops(inputs))
   1122 

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in training_step(self, model, inputs)
   1522                 loss = self.compute_loss(model, inputs)
   1523         else:
-> 1524             loss = self.compute_loss(model, inputs)
   1525 
   1526         if self.args.n_gpu > 1:

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in compute_loss(self, model, inputs, return_outputs)
   1554         else:
   1555             labels = None
-> 1556         outputs = model(**inputs)
   1557         # Save past state if it exists
   1558         # TODO: this needs to be fixed and made cleaner later.

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, attention_mask, global_attention_mask, head_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
   1855             output_attentions=output_attentions,
   1856             output_hidden_states=output_hidden_states,
-> 1857             return_dict=return_dict,
   1858         )
   1859         sequence_output = outputs[0]

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, attention_mask, global_attention_mask, head_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)
   1662 
   1663         embedding_output = self.embeddings(
-> 1664             input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
   1665         )
   1666 

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
    491         if inputs_embeds is None:
    492             inputs_embeds = self.word_embeddings(input_ids)
--> 493         position_embeddings = self.position_embeddings(position_ids)
    494         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    495 

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/sparse.py in forward(self, input)
    156         return F.embedding(
    157             input, self.weight, self.padding_idx, self.max_norm,
--> 158             self.norm_type, self.scale_grad_by_freq, self.sparse)
    159 
    160     def extra_repr(self) -> str:

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1914         # remove once script supports set_grad_enabled
   1915         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1916     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   1917 
   1918 

IndexError: index out of range in self


Still trying to get out of this…