# Error when Fine-tuning pretrained Masked Language Model **URL:** **Author:** ![Neel-Gupta](https://avatars.discourse-cdn.com/v4/letter/n/f1d935/32.png) [@Neel-Gupta](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FNeel-Gupta)%5C **Post date:** [April 13, 2021, 7:56pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ferror-when-fine-tuning-pretrained-masked-language-model%2F5386%2F10 "2021-04-13T19:56:41Z") day 100 of reporting, still getting this error ☹ ```auto --------------------------------------------------------------------------- IndexError Traceback (most recent call last) in () 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… --- _[View the full topic](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ferror-when-fine-tuning-pretrained-masked-language-model%2F5386)._