src/transformers/convert_slow_tokenizer.py/RobertaConverter/converted
class RobertaConverter:    def converted(self) -> Tokenizer:
        ot = self.original_tokenizer
        vocab = ot.encoder
        merges = list(ot.bpe_ranks.keys())

        tokenizer = Tokenizer(
            BPE(
                vocab=vocab,
                merges=merges,
                dropout=None,
                continuing_subword_prefix="",
                end_of_word_suffix="",
                fuse_unk=False,
            )
        )

        tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space)
        tokenizer.decoder = decoders.ByteLevel()
        tokenizer.post_processor = processors.RobertaProcessing(
            sep=(ot.sep_token, ot.sep_token_id),
            cls=(ot.cls_token, ot.cls_token_id),
            add_prefix_space=ot.add_prefix_space,
            trim_offsets=True,  # True by default on Roberta (historical)
        )

        return tokenizer