src/transformers/models/camembert/modeling_camembert.py/CamembertForMaskedLM/__init__
class CamembertForMaskedLM:    def __init__(self, config):
        super().__init__(config)

        if config.is_decoder:
            logger.warning(
                "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.roberta = CamembertModel(config, add_pooling_layer=False)
        self.lm_head = CamembertLMHead(config)

        # The LM head weights require special treatment only when they are tied with the word embeddings
        self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

        # Initialize weights and apply final processing
        self.post_init()