# TypeError: forward() got an unexpected keyword argument 'token\_type\_ids' **URL:** \ **Category:** Beginners\ **Created:** [December 13, 2021, 2:14pm UTC](https://discuss.huggingface.co/t/typeerror-forward-got-an-unexpected-keyword-argument-token-type-ids/12766 "2021-12-13T14:14:31Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![emmakelo](https://avatars.discourse-cdn.com/v4/letter/e/ea666f/32.png) [@emmakelo](https://discuss.huggingface.co/u/emmakelo)\ **Post date:** [December 13, 2021, 2:14pm UTC](https://discuss.huggingface.co/t/typeerror-forward-got-an-unexpected-keyword-argument-token-type-ids/12766/1 "2021-12-13T14:14:32Z")
I get the following error when finetuning Bert for classification. My forwad() function does not take any token inputs ? ```auto PRE_TRAINED_MODEL_NAME = '/Bert/sm' tokenizer = FlaubertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME) PRE_TRAINED_MODEL = FlaubertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) class FlauBertForSequenceClassification(FlaubertModel): """ FlauBert Model for Classification Tasks. """ def __init__ (self, config, num_labels, freeze_encoder=False): """ @param FlauBert: a FlauBertModel object @param classifier: a torch.nn.Module classifier @param freeze_encoder (bool): Set `False` to fine-tune the FlauBERT model """ # instantiate the parent class FlaubertModel super(). __init__ (config) # Specify hidden size of FB hidden size of our classifier, and number of labels # instantiate num. of classes self.num_labels = num_labels # instantiate and load a pretrained FlaubertModel self.encoder = FlaubertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) # freeze the encoder parameters if required (Q1) if freeze_encoder: for param in self.encoder.parameters(): param.requires_grad = False # the classifier: a feed-forward layer attached to the encoder's head self.classifier = torch.nn.Sequential( torch.nn.Linear(in_features=config.emb_dim, out_features=512), torch.nn.Tanh(), # or nn.ReLU() torch.nn.Dropout(p=0.1), torch.nn.Linear(in_features=512, out_features=self.num_labels, bias=True), ) # instantiate a dropout function for the classifier's input self.dropout = torch.nn.Dropout(p=0.1) def forward( self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, ): # encode a batch of sequences encoder_output = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) # extract the hidden representations from the encoder output hidden_state = encoder_output[0] # (bs, seq_len, dim) pooled_output = hidden_state[:, 0] # (bs, dim) # apply dropout pooled_output = self.dropout(pooled_output) # (bs, dim) # feed into the classifier logits = self.classifier(pooled_output) # (bs, dim) outputs = (logits,) + encoder_output[1:] if labels is not None: #multilabel #loss_fct = BCEWithLogitsLoss() #loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels)) #binaryclassification #loss_fct = torch.nn.CrossEntropyLoss() #crossEntropyLoss #loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) # aggregate outputs #outputs = (loss,) + outputs #multiclassification loss_fct = torch.nn.CrossEntropyLoss() #crossEntropyLoss loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) # aggregate outputs outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions) # instantiate model model = FlauBertForSequenceClassification( config=PRE_TRAINED_MODEL.config, num_labels=3, freeze_encoder = False ) # Create torch dataset class Dataset(torch.utils.data.Dataset): def __init__ (self, encodings, labels=None): self.encodings = encodings self.labels = labels def __getitem__ (self, idx): item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} if self.labels: item["labels"] = torch.tensor(self.labels[idx]) return item def __len__ (self): return len(self.encodings["input_ids"]) var, l = input_file(path1) X = list(var) y = list(l) X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) X_train_tokenized = tokenizer(X_train, padding="max_length", truncation=True, max_length=512) print(X_train_tokenized) X_val_tokenized = tokenizer(X_val, padding="max_length", truncation=True, max_length=512) train_dataset = Dataset(X_train_tokenized, y_train) val_dataset = Dataset(X_val_tokenized, y_val) training_args = TrainingArguments( output_dir='/ghf/sm', logging_dir='/ogs/sm', do_train=True, do_eval=False, evaluation_strategy="steps", logging_first_step=True, logging_steps=10, num_train_epochs=2.0, per_device_train_batch_size=8, per_device_eval_batch_size=8, learning_rate=5e-5, weight_decay=0.01 ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset, compute_metrics=compute_metrics ) print("Train") trainer.train() ``` STACKTRACE ```auto *****Running training***** Num examples = 536 Num Epochs = 2 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient Accumulation steps = 1 Total optimization steps = 34 0%| | 0/34 [00:00 trainer.train() File "/ython3.9/site-packages/transformers/trainer.py", line 1269, in train tr_loss += self.training_step(model, inputs) File "/lw/.conda/envs/bert/lib/python3.9/site-packages/transformers/trainer.py", line 1754, in training_step loss = self.compute_loss(model, inputs) File "/luw/.conda/envs/bert/lib/python3.9/site-packages/transformers/trainer.py", line 1786, in compute_loss outputs = model(**inputs) File "uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py", line 168, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py", line 178, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply output.reraise() File "uw/.conda/envs/bert/lib/python3.9/site-packages/torch/_utils.py", line 425, in reraise raise self.exc_type(msg) TypeError: Caught TypeError in replica 0 on device 0. Original Traceback (most recent call last): File "/uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker output = module(*input, **kwargs) File "/uw/.conda/envs/bert/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) TypeError: forward() got an unexpected keyword argument 'token_type_ids' ``` ---
**Author:** ![anjandash](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/anjandash/32/3690_2.png) [@anjandash](https://discuss.huggingface.co/u/anjandash)\ **Post date:** [March 4, 2022, 10:10am UTC](https://discuss.huggingface.co/t/typeerror-forward-got-an-unexpected-keyword-argument-token-type-ids/12766/2 "2022-03-04T10:10:41Z")
Did you figure out a solution for this? Was it resolved? ---
**Author:** ![emmakelo](https://avatars.discourse-cdn.com/v4/letter/e/ea666f/32.png) [@emmakelo](https://discuss.huggingface.co/u/emmakelo)\ **Post date:** [March 10, 2022, 10:43am UTC](https://discuss.huggingface.co/t/typeerror-forward-got-an-unexpected-keyword-argument-token-type-ids/12766/3 "2022-03-10T10:43:08Z")
Yes I find it. ---
**Author:** ![TempUser](https://avatars.discourse-cdn.com/v4/letter/t/9dc877/32.png) [@TempUser](https://discuss.huggingface.co/u/TempUser)\ **Post date:** [June 10, 2022, 10:27am UTC](https://discuss.huggingface.co/t/typeerror-forward-got-an-unexpected-keyword-argument-token-type-ids/12766/4 "2022-06-10T10:27:40Z")
Can you please provide the solution