update class name

)

class ChemBERTa(BertPreTrainedModel):

class ChemBERTaforSequenceClassification(BertPreTrainedModel):

    r"""

        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for computing the sequence classification/regression loss.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = RobertaTokenizer.from_pretrained('roberta-base')

        model = ChemBERTa.from_pretrained('roberta-base')

        model = ChemBERTaforSequenceClassification.from_pretrained('roberta-base')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1

        outputs = model(input_ids, labels=labels)