GPT2Tokenizer
classkeras_hub.tokenizers.GPT2Tokenizer(vocabulary=None, merges=None, **kwargs)
A GPT-2 tokenizer using Byte-Pair Encoding subword segmentation.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.BytePairTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by GPT-2
models and provides a from_preset()
method to automatically download
a matching vocabulary for a GPT-2 preset.
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
Examples
# Unbatched input.
tokenizer = keras_hub.models.GPT2Tokenizer.from_preset("gpt2_base_en")
tokenizer("The quick brown fox jumped.")
# Batched input.
tokenizer(["The quick brown fox jumped.", "The fox slept."])
# Detokenization.
tokenizer.detokenize(tokenizer("The quick brown fox jumped."))
# Custom vocabulary.
vocab = {"<|endoftext|>": 0, "a": 4, "Ġquick": 5, "Ġfox": 6}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_hub.models.GPT2Tokenizer(vocabulary=vocab, merges=merges)
tokenizer("a quick fox.")
from_preset
methodGPT2Tokenizer.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 |
---|---|---|
gpt2_base_en | 124.44M | 12-layer GPT-2 model where case is maintained. Trained on WebText. |
gpt2_medium_en | 354.82M | 24-layer GPT-2 model where case is maintained. Trained on WebText. |
gpt2_large_en | 774.03M | 36-layer GPT-2 model where case is maintained. Trained on WebText. |
gpt2_extra_large_en | 1.56B | 48-layer GPT-2 model where case is maintained. Trained on WebText. |
gpt2_base_en_cnn_dailymail | 124.44M | 12-layer GPT-2 model where case is maintained. Finetuned on the CNN/DailyMail summarization dataset. |