Seq2SeqLMPreprocessor
classkeras_hub.models.Seq2SeqLMPreprocessor(
tokenizer, encoder_sequence_length=1024, decoder_sequence_length=1024, **kwargs
)
Base class for seq2seq language modeling preprocessing layers.
Seq2SeqLMPreprocessor
tasks wrap a keras_hub.tokenizer.Tokenizer
to
create a preprocessing layer for seq2seq language modeling tasks. It is
intended to be paired with a keras.models.Seq2SeqLM
task.
All Seq2SeqLMPreprocessor
layers take inputs a dictionary input with keys
"encoder_text"
and "decoder_text"
.
This layer will always output a (x, y, sample_weight)
tuple, where x
is a dictionary with the tokenized inputs, y
contains the tokens from x
offset by 1, and sample_weight
marks where y
contains padded
values. The exact contents of x
will vary depending on the model being
used.
a Seq2SeqLMPreprocessor
contains two extra methods, generate_preprocess
and generate_postprocess
for use with generation. See examples below.
All Seq2SeqLMPreprocessor
tasks include a from_preset()
constructor
which can be used to load a pre-trained config and vocabularies. You can
call the from_preset()
constructor directly on this base class, in which
case the correct class for you model will be automatically instantiated.
Examples.
preprocessor = keras_hub.models.Seq2SeqLMPreprocessor.from_preset(
"bart_base_en",
encoder_sequence_length=256,
decoder_sequence_length=256,
)
# Tokenize, mask and pack a single sentence.
x = {
"encoder_text": "The fox was sleeping.",
"decoder_text": "The fox was awake.",
}
x, y, sample_weight = preprocessor(x)
# Tokenize and pad/truncate a batch of labeled sentences.
x = {
"encoder_text": ["The fox was sleeping."],
"decoder_text": ["The fox was awake."],
x, y, sample_weight = preprocessor(x)
# With a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
ds = tf.data.Dataset.from_tensor_slices(x)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
# Generate preprocess and postprocess.
x = preprocessor.generate_preprocess(x) # Tokenized numeric inputs.
x = preprocessor.generate_postprocess(x) # Detokenized string outputs.
from_preset
methodSeq2SeqLMPreprocessor.from_preset(preset, config_file="preprocessor.json", **kwargs)
Instantiate a keras_hub.models.Preprocessor
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 Preprocessor
subclass, you can run cls.presets.keys()
to
list all built-in presets available on the class.
As there are usually multiple preprocessing classes for a given model,
this method should be called on a specific subclass like
keras_hub.models.BertTextClassifierPreprocessor.from_preset()
.
Arguments
Examples
# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
"gemma_2b_en",
)
# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
"bert_base_en",
)
Preset name | Parameters | Description |
---|---|---|
bart_base_en | 139.42M | 6-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl. |
bart_large_en | 406.29M | 12-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl. |
bart_large_en_cnn | 406.29M | The bart_large_en backbone model fine-tuned on the CNN+DM summarization dataset. |
save_to_preset
methodSeq2SeqLMPreprocessor.save_to_preset(preset_dir)
Save preprocessor to a preset directory.
Arguments
tokenizer
propertykeras_hub.models.Seq2SeqLMPreprocessor.tokenizer
The tokenizer used to tokenize strings.