Overwriting the Trainer can be done as follows (this is also explained in the docs 31):
from torch import nn
from transformers import Trainer

class CustomTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False):
        labels = inputs.get("labels")
        # forward pass
        outputs = model(**inputs)
        logits = outputs.get('logits')
        # compute custom loss
        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([0.2, 0.3]))
        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
        return (loss, outputs) if return_outputs else loss