XLMRobertaTokenizer
classkeras_hub.tokenizers.XLMRobertaTokenizer(proto, **kwargs)
An XLM-RoBERTa tokenizer using SentencePiece subword segmentation.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.SentencePieceTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by
XLM-RoBERTa models and provides a from_preset()
method to automatically
download a matching vocabulary for an XLM-RoBERTa preset.
Note: If you are providing your own custom SentencePiece model, the original fairseq implementation of XLM-RoBERTa re-maps some token indices from the underlying sentencepiece output. To preserve compatibility, we do the same re-mapping here.
If input is a batch of strings (rank > 0), the layer will output a
tf.RaggedTensor
where the last dimension of the output is ragged.
If input is a scalar string (rank == 0), the layer will output a dense
tf.Tensor
with static shape [None]
.
Arguments
string
path to a SentencePiece proto file or a
bytes
object with a serialized SentencePiece proto. See the
SentencePiece repository
for more details on the format.Examples
tokenizer = keras_hub.models.XLMRobertaTokenizer.from_preset(
"xlm_roberta_base_multi",
)
# Unbatched inputs.
tokenizer("the quick brown fox")
# Batched inputs.
tokenizer(["the quick brown fox", "الأرض كروية"])
# Detokenization.
tokenizer.detokenize(tokenizer("the quick brown fox"))
# Custom vocabulary
def train_sentencepiece(ds, vocab_size):
bytes_io = io.BytesIO()
sentencepiece.SentencePieceTrainer.train(
sentence_iterator=ds.as_numpy_iterator(),
model_writer=bytes_io,
vocab_size=vocab_size,
model_type="WORD",
unk_id=0,
bos_id=1,
eos_id=2,
)
return bytes_io.getvalue()
ds = tf.data.Dataset.from_tensor_slices(
["the quick brown fox", "the earth is round"]
)
proto = train_sentencepiece(ds, vocab_size=10)
tokenizer = keras_hub.models.XLMRobertaTokenizer(proto=proto)
from_preset
methodXLMRobertaTokenizer.from_preset(preset, config_file="tokenizer.json", **kwargs)
Instantiate a keras_hub.models.Tokenizer
from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset
can be passed as
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./bert_base_en'
For any Tokenizer
subclass, you can run cls.presets.keys()
to list
all built-in presets available on the class.
This constructor can be called in one of two ways. Either from the base
class like keras_hub.models.Tokenizer.from_preset()
, or from
a model class like keras_hub.models.GemmaTokenizer.from_preset()
.
If calling from the base class, the subclass of the returning object
will be inferred from the config in the preset directory.
Arguments
True
, the weights will be loaded into the
model architecture. If False
, the weights will be randomly
initialized.Examples
# Load a preset tokenizer.
tokenizer = keras_hub.tokenizer.Tokenizer.from_preset("bert_base_en")
# Tokenize some input.
tokenizer("The quick brown fox tripped.")
# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])
Preset name | Parameters | Description |
---|---|---|
xlm_roberta_base_multi | 277.45M | 12-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages. |
xlm_roberta_large_multi | 558.84M | 24-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages. |