def transformer(model: str = 'xlnet', **kwargs):
"""
Load Transformer emotion model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - BERT architecture from google.
* ``'tiny-bert'`` - BERT architecture from google with smaller parameters.
* ``'albert'`` - ALBERT architecture from google.
* ``'tiny-albert'`` - ALBERT architecture from google with smaller parameters.
* ``'xlnet'`` - XLNET architecture from google.
* ``'alxlnet'`` - XLNET architecture from google + Malaya.
Returns
-------
MODEL : Transformer class
"""
model = model.lower()
if model not in _availability:
raise Exception(
'model not supported, please check supported models from malaya.relevancy.available_transformer_model()'
)
return softmax.transformer(PATH_RELEVANCY,
S3_PATH_RELEVANCY,
'relevancy',
label,
model=model,
**kwargs)
def transformer(model: str = 'xlnet', **kwargs):
"""
Load Transformer emotion model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - Google BERT BASE parameters.
* ``'tiny-bert'`` - Google BERT TINY parameters.
* ``'albert'`` - Google ALBERT BASE parameters.
* ``'tiny-albert'`` - Google ALBERT TINY parameters.
* ``'xlnet'`` - Google XLNET BASE parameters.
* ``'alxlnet'`` - Malaya ALXLNET BASE parameters.
Returns
-------
result : malaya.supervised.softmax.transformer function
"""
model = model.lower()
if model not in _transformer_availability:
raise ValueError(
'model not supported, please check supported models from malaya.emotion.available_transformer()'
)
return softmax.transformer(PATH_EMOTION,
S3_PATH_EMOTION,
'emotion',
label,
model=model,
**kwargs)
def transformer(model: str = 'xlnet', **kwargs):
"""
Load Transformer subjectivity model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - BERT architecture from google.
* ``'tiny-bert'`` - BERT architecture from google with smaller parameters.
* ``'albert'`` - ALBERT architecture from google.
* ``'tiny-albert'`` - ALBERT architecture from google with smaller parameters.
* ``'xlnet'`` - XLNET architecture from google.
* ``'alxlnet'`` - XLNET architecture from google + Malaya.
Returns
-------
result : Transformer class
"""
model = model.lower()
if model not in _availability:
raise Exception(
'model not supported, please check supported models from malaya.subjective.available_transformer()'
)
return softmax.transformer(PATH_SUBJECTIVE,
S3_PATH_SUBJECTIVE,
'subjective',
label,
model=model,
**kwargs)
def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):
"""
Load Transformer relevancy model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - Google BERT BASE parameters.
* ``'tiny-bert'`` - Google BERT TINY parameters.
* ``'albert'`` - Google ALBERT BASE parameters.
* ``'tiny-albert'`` - Google ALBERT TINY parameters.
* ``'xlnet'`` - Google XLNET BASE parameters.
* ``'alxlnet'`` - Malaya ALXLNET BASE parameters.
* ``'bigbird'`` - Google BigBird BASE parameters.
* ``'tiny-bigbird'`` - Malaya BigBird BASE parameters.
quantized : bool, optional (default=False)
if True, will load 8-bit quantized model.
Quantized model not necessary faster, totally depends on the machine.
Returns
-------
result: model
List of model classes:
* if `bert` in model, will return `malaya.model.bert.MulticlassBERT`.
* if `xlnet` in model, will return `malaya.model.xlnet.MulticlassXLNET`.
* if `bigbird` in model, will return `malaya.model.xlnet.MulticlassBigBird`.
"""
model = model.lower()
if model not in _transformer_availability:
raise ValueError(
'model not supported, please check supported models from `malaya.relevancy.available_transformer()`.'
)
return softmax.transformer(
class_name = 'relevancy',
label = label,
model = model,
quantized = quantized,
**kwargs
)
def transformer(model: str = 'bert', quantized: bool = False, **kwargs):
"""
Load Transformer subjectivity model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - Google BERT BASE parameters.
* ``'tiny-bert'`` - Google BERT TINY parameters.
* ``'albert'`` - Google ALBERT BASE parameters.
* ``'tiny-albert'`` - Google ALBERT TINY parameters.
* ``'xlnet'`` - Google XLNET BASE parameters.
* ``'alxlnet'`` - Malaya ALXLNET BASE parameters.
quantized : bool, optional (default=False)
if True, will load 8-bit quantized model.
Quantized model not necessary faster, totally depends on the machine.
Returns
-------
result : malaya.supervised.softmax.transformer function
"""
model = model.lower()
if model not in _transformer_availability:
raise Exception(
'model not supported, please check supported models from `malaya.subjective.available_transformer()`.'
)
return softmax.transformer(PATH_SUBJECTIVE,
S3_PATH_SUBJECTIVE,
'subjective',
label,
model=model,
quantized=quantized,
**kwargs)
