CausalLMPreprocessor
classkeras_nlp.models.CausalLMPreprocessor(
tokenizer, sequence_length=1024, add_start_token=True, add_end_token=True, **kwargs
)
Base class for causal language modeling preprocessing layers.
CausalLMPreprocessor
tasks wrap a keras_hub.tokenizer.Tokenizer
to
create a preprocessing layer for causal language modeling tasks. It is
intended to be paired with a keras.models.CausalLM
task.
All CausalLMPreprocessor
take inputs a single input. This can be a single
string or a batch of strings. See examples below. These inputs
will be tokenized and padded/truncated to a fixed sequence length.
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 CausalLMPreprocessor
contains two extra methods, generate_preprocess
and generate_postprocess
for use with generation. See examples below.
All CausalLMPreprocessor
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.CausalLMPreprocessor.from_preset(
"bert_base_en_uncased",
sequence_length=256, # Optional.
)
# Tokenize, mask and pack a single sentence.
x = "The quick brown fox jumped."
x, y, sample_weight = preprocessor(x)
# Tokenize and pad/truncate a batch of labeled sentences.
x = ["The quick brown fox jumped.", "Call me Ishmael."]
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
methodCausalLMPreprocessor.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 |
---|---|---|
pali_gemma_3b_mix_224 | 2.92B | image size 224, mix fine tuned, text sequence length is 256 |
pali_gemma_3b_mix_448 | 2.92B | image size 448, mix fine tuned, text sequence length is 512 |
pali_gemma_3b_224 | 2.92B | image size 224, pre trained, text sequence length is 128 |
pali_gemma_3b_448 | 2.92B | image size 448, pre trained, text sequence length is 512 |
pali_gemma_3b_896 | 2.93B | image size 896, pre trained, text sequence length is 512 |
gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, base Gemma model. |
gemma_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_1.1_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. The 1.1 update improves model quality. |
code_gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, base Gemma model. |
gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
code_gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. |
code_gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. The 1.1 update improves model quality. |
gemma2_2b_en | 2.61B | 2 billion parameter, 26-layer, base Gemma model. |
gemma2_instruct_2b_en | 2.61B | 2 billion parameter, 26-layer, instruction tuned Gemma model. |
gemma2_9b_en | 9.24B | 9 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_9b_en | 9.24B | 9 billion parameter, 42-layer, instruction tuned Gemma model. |
gemma2_27b_en | 27.23B | 27 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_27b_en | 27.23B | 27 billion parameter, 42-layer, instruction tuned Gemma model. |
shieldgemma_2b_en | 2.61B | 2 billion parameter, 26-layer, ShieldGemma model. |
shieldgemma_9b_en | 9.24B | 9 billion parameter, 42-layer, ShieldGemma model. |
shieldgemma_27b_en | 27.23B | 27 billion parameter, 42-layer, ShieldGemma model. |
phi3_mini_4k_instruct_en | 3.82B | 3.8 billion parameters, 32 layers, 4k context length, Phi-3 model. The model was trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. |
phi3_mini_128k_instruct_en | 3.82B | 3.8 billion parameters, 32 layers, 128k context length, Phi-3 model. The model was trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. |
llama2_7b_en | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model. |
llama2_7b_en_int8 | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model with activation and weights quantized to int8. |
llama2_instruct_7b_en | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model. |
llama2_instruct_7b_en_int8 | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model with activation and weights quantized to int8. |
vicuna_1.5_7b_en | 6.74B | 7 billion parameter, 32-layer, instruction tuned Vicuna v1.5 model. |
llama3_8b_en | 8.03B | 8 billion parameter, 32-layer, base LLaMA 3 model. |
llama3_8b_en_int8 | 8.03B | 8 billion parameter, 32-layer, base LLaMA 3 model with activation and weights quantized to int8. |
llama3_instruct_8b_en | 8.03B | 8 billion parameter, 32-layer, instruction tuned LLaMA 3 model. |
llama3_instruct_8b_en_int8 | 8.03B | 8 billion parameter, 32-layer, instruction tuned LLaMA 3 model with activation and weights quantized to int8. |
mistral_7b_en | 7.24B | Mistral 7B base model |
mistral_instruct_7b_en | 7.24B | Mistral 7B instruct model |
mistral_0.2_instruct_7b_en | 7.24B | Mistral 7B instruct Version 0.2 model |
falcon_refinedweb_1b_en | 1.31B | 24-layer Falcon model (Falcon with 1B parameters), trained on 350B tokens of RefinedWeb dataset. |
opt_125m_en | 125.24M | 12-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_1.3b_en | 1.32B | 24-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_2.7b_en | 2.70B | 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_6.7b_en | 6.70B | 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
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. |
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. |
save_to_preset
methodCausalLMPreprocessor.save_to_preset(preset_dir)
Save preprocessor to a preset directory.
Arguments
tokenizer
propertykeras_nlp.models.CausalLMPreprocessor.tokenizer
The tokenizer used to tokenize strings.