BloomTokenizer
classkeras_nlp.tokenizers.BloomTokenizer(vocabulary=None, merges=None, **kwargs)
A BLOOM 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 BLOOM
models and provides a from_preset()
method to automatically download
a matching vocabulary for a BLOOM 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.BloomTokenizer.from_preset("bloom_560m_multi")
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 = {"<s>": 0, "</s>": 1, "<pad>": 2, "a": 3, "Ġquick": 4, "Ġfox": 5}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_hub.models.BloomTokenizer(vocabulary=vocab, merges=merges)
tokenizer("a quick fox.")
from_preset
methodBloomTokenizer.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 |
---|---|---|
bloom_560m_multi | 559.21M | 24-layer Bloom model with hidden dimension of 1024. trained on 45 natural languages and 12 programming languages. |
bloom_1.1b_multi | 1.07B | 24-layer Bloom model with hidden dimension of 1536. trained on 45 natural languages and 12 programming languages. |
bloom_1.7b_multi | 1.72B | 24-layer Bloom model with hidden dimension of 2048. trained on 45 natural languages and 12 programming languages. |
bloom_3b_multi | 3.00B | 30-layer Bloom model with hidden dimension of 2560. trained on 45 natural languages and 12 programming languages. |
bloomz_560m_multi | 559.21M | 24-layer Bloom model with hidden dimension of 1024. finetuned on crosslingual task mixture (xP3) dataset. |
bloomz_1.1b_multi | 1.07B | 24-layer Bloom model with hidden dimension of 1536. finetuned on crosslingual task mixture (xP3) dataset. |
bloomz_1.7b_multi | 1.72B | 24-layer Bloom model with hidden dimension of 2048. finetuned on crosslingual task mixture (xP3) dataset. |
bloomz_3b_multi | 3.00B | 30-layer Bloom model with hidden dimension of 2560. finetuned on crosslingual task mixture (xP3) dataset. |