def gpt2(
model: str = '345M',
generate_length: int = 256,
temperature: float = 1.0,
top_k: int = 40,
**kwargs,
):
"""
Load GPT2 model to generate a string given a prefix string.
Parameters
----------
model : str, optional (default='345M')
Model architecture supported. Allowed values:
* ``'117M'`` - GPT2 117M parameters.
* ``'345M'`` - GPT2 345M parameters.
generate_length : int, optional (default=256)
length of sentence to generate.
temperature : float, optional (default=1.0)
temperature value, value should between 0 and 1.
top_k : int, optional (default=40)
top-k in nucleus sampling selection.
Returns
-------
result: malaya.transformers.gpt2.Model class
"""
model = model.upper()
if model not in _gpt2_availability:
raise ValueError(
'model not supported, please check supported models from `malaya.generator.available_gpt2()`.'
)
if generate_length < 10:
raise ValueError('generate_length must bigger than 10')
if not 0 < temperature <= 1.0:
raise ValueError('temperature must, 0 < temperature <= 1.0')
if top_k < 5:
raise ValueError('top_k must bigger than 5')
from malaya.transformers.gpt2 import load
if tf.executing_eagerly():
logging.warning(
'Load pretrained GPT2 model will disable eager execution.'
)
tf.compat.v1.disable_eager_execution()
return load(
model = model,
generate_length = generate_length,
temperature = temperature,
top_k = top_k,
**kwargs,
)
def gpt2(
model: str = '345M',
generate_length: int = 256,
temperature: float = 1.0,
top_k: int = 40,
**kwargs
):
"""
Load transformer model.
Parameters
----------
model : str, optional (default='345M')
Model architecture supported. Allowed values:
* ``'117M'`` - GPT2 117M parameters.
* ``'345M'`` - GPT2 345M parameters.
generate_length : int, optional (default=256)
length of sentence to generate.
temperature : float, optional (default=1.0)
temperature value, value should between 0 and 1.
top_k : int, optional (default=40)
top-k in nucleus sampling selection.
Returns
-------
result: malaya.transformers.gpt2.Model class
"""
model = model.upper()
if model not in ['117M', '345M']:
raise Exception(
"model not supported, for now only supported ['117M', '345M']"
)
if generate_length < 10:
raise Exception('generate_length must bigger than 10')
if not 0 < temperature <= 1.0:
raise Exception('temperature must, 0 < temperature <= 1.0')
if top_k < 5:
raise Exception('top_k must bigger than 5')
from malaya.transformers.gpt2 import load
return load(
model = model,
generate_length = generate_length,
temperature = temperature,
top_k = top_k,
**kwargs
)