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
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
unk_token = self.original_tokenizer.unk_token
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="</w>",
fuse_unk=False,
unk_token=str(unk_token),
))
tokenizer.normalizer = normalizers.Sequence([
normalizers.NFC(),
normalizers.Replace(Regex(r"\s+"), " "),
normalizers.Lowercase()
])
tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
pre_tokenizers.Split(
Regex(
r"""'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""
),
behavior="removed",
invert=True,
),
pre_tokenizers.ByteLevel(add_prefix_space=False),
])
tokenizer.decoder = decoders.ByteLevel()
# Hack to have a ByteLevel and TemplaceProcessor
tokenizer.post_processor = processors.RobertaProcessing(
sep=(self.original_tokenizer.eos_token,
self.original_tokenizer.eos_token_id),
cls=(self.original_tokenizer.bos_token,
self.original_tokenizer.bos_token_id),
add_prefix_space=False,
trim_offsets=False,
)
return tokenizer
