I’m training a german language Electra model for multi label classification. When I run
# Training model
trainer.fit(model, data_module)
I get the error
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-63-67a8decbf039> in <module>()
1 # Training model
----> 2 trainer.fit(model, data_module)
16 frames
<ipython-input-51-69561934ba6f> in forward(self, input_ids, attention_mask, labels)
12 def forward(self, input_ids, attention_mask, labels=None):
13 output = self.electra(input_ids, attention_mask=attention_mask)
---> 14 output = self.classifier(output.last_hidden_state[:, 0]) # output = self.classifier(output.pooler_output)
15 output = torch.sigmoid(output)
16 loss = 0
AttributeError: 'ElectraForPreTrainingOutput' object has no attribute 'last_hidden_state'
I define my model parameters like this (in a lightening module):
class CrowdCodedTagger(pl.LightningModule):
#AutoModelForPreTraining.from_pretrained("german-nlp-group/electra-base-german-uncased", return_dict = True)
def __init__(self, n_classes: int, n_training_steps=None, n_warmup_steps=None):
super().__init__()
#self.electra = ElectraModel.from_pretrained(ELECTRA_MODEL_NAME) # changed ElectraModel to ElectraForSequenceClassification. Removed (, return_dict=False)
self.electra = AutoModelForPreTraining.from_pretrained("german-nlp-group/electra-base-german-uncased")
self.classifier = nn.Linear(self.electra.config.hidden_size, n_classes)
self.n_training_steps = n_training_steps
self.n_warmup_steps = n_warmup_steps
self.criterion = nn.BCELoss()
def forward(self, input_ids, attention_mask, labels=None):
output = self.electra(input_ids, attention_mask=attention_mask)
output = self.classifier(output.last_hidden_state[:, 0]) # output = self.classifier(output.pooler_output)
output = torch.sigmoid(output)
loss = 0
if labels is not None:
loss = self.criterion(output, labels)
return loss, output
def training_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
labels = batch["labels"]
loss, outputs = self(input_ids, attention_mask, labels)
self.log("train_loss", loss, prog_bar=True, logger=True)
return {"loss": loss, "predictions": outputs, "labels": labels}
def validation_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
labels = batch["labels"]
loss, outputs = self(input_ids, attention_mask, labels)
self.log("val_loss", loss, prog_bar=True, logger=True)
return loss
def test_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
labels = batch["labels"]
loss, outputs = self(input_ids, attention_mask, labels)
self.log("test_loss", loss, prog_bar=True, logger=True)
return loss
def training_epoch_end(self, outputs):
labels = []
predictions = []
for output in outputs:
for out_labels in output["labels"].detach().cpu():
labels.append(out_labels)
for out_predictions in output["predictions"].detach().cpu():
predictions.append(out_predictions)
labels = torch.stack(labels).int()
predictions = torch.stack(predictions)
for i, name in enumerate(LABEL_COLUMNS):
class_roc_auc = auroc(predictions[:, i], labels[:, i])
self.logger.experiment.add_scalar(f"{name}_roc_auc/Train", class_roc_auc, self.current_epoch)
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=2e-5) #DEFINING LEARNING RATE
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=self.n_warmup_steps,
num_training_steps=self.n_training_steps
)
return dict(
optimizer=optimizer,
lr_scheduler=dict(
scheduler=scheduler,
interval='step'
)
)
This is the model I use: german-nlp-group/electra-base-german-uncased · Hugging Face
Here is a link to the colab: Google Colab
Can anyone spot the problem? Thanks in advance!