def __init__(self, vocab_path, bpe_merges_path):
tokenizer = Tokenizer(models.BPE.from_files(vocab_path, bpe_merges_path))
# Use the byte level
add_prefix_spaces = False # Whether to automatically prefix the sequences with a space if none found
tokenizer.with_pre_tokenizer(pre_tokenizers.ByteLevel.new(add_prefix_spaces))
tokenizer.with_decoder(decoders.ByteLevel.new())
# Setup truncation if needed
truncate = False
max_length = 1024
if truncate:
stride = 0
strategy = 'longest_first' # Can also be `only_first` or `only_second`
tokenizer.with_truncation(max_length, stride, strategy)
# Setup padding if needed
padding = False
# Whether to always pad to max_length. If this is false, we will pad to the
# longest sequence in the batch.
pad_to_max_length = False
padding_side = "right" # Can also be "left"
pad_token_id = 0
pad_token_type_id = 0
pad_token = "[PAD]"
if padding:
tokenizer.with_padding(
max_length if pad_to_max_length else None,
padding_side,
pad_token_id,
pad_token_type_id,
pad_token
)
self.tokenizer = tokenizer
parser.add_argument('out_npz',
metavar='OUT.npz',
type=str,
default='',
nargs='?',
help='Output file path')
args = parser.parse_args()
# Initialize a tokenizer based on BPE
vocab = "./models/%s/encoder.json" % args.model_name
merges = "./models/%s/vocab.bpe" % args.model_name
tokenizer = Tokenizer(models.BPE.from_files(vocab, merges))
# Use the byte level
add_prefix_spaces = False # Whether to automatically prefix the sequences with a space if none found
tokenizer.with_pre_tokenizer(pre_tokenizers.ByteLevel.new(add_prefix_spaces))
tokenizer.with_decoder(decoders.ByteLevel.new())
# Setup truncation if needed
truncate = False
max_length = 1024
if truncate:
stride = 0
strategy = 'longest_first' # Can also be `only_first` or `only_second`
tokenizer.with_truncation(max_length, stride, strategy)
# Setup padding if needed
padding = False
# Whether to always pad to max_length. If this is false, we will pad to the
# longest sequence in the batch.