File /flair/trainers/trainer.py in train(self, base_path, anneal_factor, patience, min_learning_rate, initial_extra_patience, anneal_with_restarts, learning_rate, decoder_learning_rate, mini_batch_size, eval_batch_size, mini_batch_chunk_size, max_epochs, optimizer, train_with_dev, train_with_test, reduce_transformer_vocab, main_evaluation_metric, monitor_test, monitor_train_sample, use_final_model_for_eval, gold_label_dictionary_for_eval, exclude_labels, sampler, shuffle, shuffle_first_epoch, embeddings_storage_mode, epoch, save_final_model, save_optimizer_state, save_model_each_k_epochs, create_file_logs, create_loss_file, write_weights, plugins, attach_default_scheduler, **kwargs)
    198         ]:
    199             local_variables.pop(var)
--> 200         return self.train_custom(**local_variables, **kwargs)
    201 
    202     def fine_tune(

File /flair/trainers/trainer.py in train_custom(self, base_path, learning_rate, decoder_learning_rate, mini_batch_size, eval_batch_size, mini_batch_chunk_size, max_epochs, optimizer, train_with_dev, train_with_test, max_grad_norm, reduce_transformer_vocab, main_evaluation_metric, monitor_test, monitor_train_sample, use_final_model_for_eval, gold_label_dictionary_for_eval, exclude_labels, sampler, shuffle, shuffle_first_epoch, embeddings_storage_mode, epoch, save_final_model, save_optimizer_state, save_model_each_k_epochs, create_file_logs, create_loss_file, write_weights, use_amp, plugins, **kwargs)
    596                             # forward pass
    597                             with torch.autocast(device_type=flair.device.type, enabled=use_amp):
--> 598                                 loss, datapoint_count = self.model.forward_loss(batch_step)
    599 
    600                             batch_train_samples += datapoint_count

File /flair/nn/model.py in forward_loss(self, sentences)
    745 
    746         # pass data points through network to get encoded data point tensor
--> 747         data_point_tensor = self._encode_data_points(sentences, data_points)
    748 
    749         # decode

File /flair/nn/model.py in _encode_data_points(self, sentences, data_points)
    713         # embed sentences
    714         if self.should_embed_sentence:
--> 715             self.embeddings.embed(sentences)
    716 
    717         # get a tensor of data points

File /flair/embeddings/base.py in embed(self, data_points)
     48 
     49         if not self._everything_embedded(data_points):
---> 50             self._add_embeddings_internal(data_points)
     51 
     52         return data_points

File /flair/embeddings/transformer.py in _add_embeddings_internal(self, sentences)
    703         gradient_context = torch.enable_grad() if (self.fine_tune and self.training) else torch.no_grad()
    704         with gradient_context:
--> 705             embeddings = self._forward_tensors(tensors)
    706 
    707         if self.document_embedding:

File /flair/embeddings/transformer.py in _forward_tensors(self, tensors)
   1422 
   1423     def _forward_tensors(self, tensors) -> Dict[str, torch.Tensor]:
-> 1424         return self.forward(**tensors)
   1425 
   1426     def export_onnx(

File /flair/embeddings/transformer.py in forward(self, input_ids, sub_token_lengths, token_lengths, attention_mask, overflow_to_sample_mapping, word_ids, langs, bbox, pixel_values)
   1322         if pixel_values is not None:
   1323             model_kwargs["pixel_values"] = pixel_values
-> 1324         hidden_states = self.model(input_ids, **model_kwargs)[-1]
   1325         # make the tuple a tensor; makes working with it easier.
   1326         hidden_states = torch.stack(hidden_states)

File /torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1516             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517         else:
-> 1518             return self._call_impl(*args, **kwargs)
   1519 
   1520     def _call_impl(self, *args, **kwargs):

File /torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1525                 or _global_backward_pre_hooks or _global_backward_hooks
   1526                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527             return forward_call(*args, **kwargs)
   1528 
   1529         try:

File /transformers/models/xlm_roberta/modeling_xlm_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
    837         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
    838 
--> 839         embedding_output = self.embeddings(
    840             input_ids=input_ids,
    841             position_ids=position_ids,

File /torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1516             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517         else:
-> 1518             return self._call_impl(*args, **kwargs)
   1519 
   1520     def _call_impl(self, *args, **kwargs):

File /torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1525                 or _global_backward_pre_hooks or _global_backward_hooks
   1526                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527             return forward_call(*args, **kwargs)
   1528 
   1529         try:

File /transformers/models/xlm_roberta/modeling_xlm_roberta.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
    124 
    125         if inputs_embeds is None:
--> 126             inputs_embeds = self.word_embeddings(input_ids)
    127         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    128 

File /torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1516             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517         else:
-> 1518             return self._call_impl(*args, **kwargs)
   1519 
   1520     def _call_impl(self, *args, **kwargs):

File /torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1525                 or _global_backward_pre_hooks or _global_backward_hooks
   1526                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527             return forward_call(*args, **kwargs)
   1528 
   1529         try:

File /torch/nn/modules/sparse.py in forward(self, input)
    160 
    161     def forward(self, input: Tensor) -> Tensor:
--> 162         return F.embedding(
    163             input, self.weight, self.padding_idx, self.max_norm,
    164             self.norm_type, self.scale_grad_by_freq, self.sparse)

File /torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   2231         # remove once script supports set_grad_enabled
   2232         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2233     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   2234 
   2235 

IndexError: index out of range in self