content-model-training-fix-typeerror-forward-got-an-unexpected-keyword-argume-f8ab3c44

THEN The base MT5EncoderModel/T5EncoderModel does not include a classification head. Create a custom model that adds a linear layer on top of the encoder output, and override forward() to accept 'labels' and return a loss including CrossEntropyLoss. Follow the pattern of BertForSequenceClassification.

```python
class MT5ForSequenceClassification(MT5PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.encoder = MT5EncoderModel(config)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.classifier = nn.Linear(config.d_model, config.num_labels)
        self.post_init()

    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        hidden_states = outputs.last_hidden_state[:, 0, :]  # use [CLS] or pool
        pooled = self.dropout(hidden_states)
        logits = self.classifier(pooled)
        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
        return transformers.modeling_outputs.SequenceClassifierOutput(
            loss=loss,
            logits=logits
        )
```
Then instantiate with `MT5ForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)`.