def transformer(model: str = 'xlnet', size: str = 'base', **kwargs):
"""
Load Transformer emotion model.
Parameters
----------
model : str, optional (default='bert')
Model architecture supported. Allowed values:
* ``'bert'`` - BERT architecture from google.
* ``'xlnet'`` - XLNET architecture from google.
* ``'albert'`` - ALBERT architecture from google.
size : str, optional (default='base')
Model size supported. Allowed values:
* ``'base'`` - BASE size.
* ``'small'`` - SMALL size.
Returns
-------
BERT : malaya._models._bert_model.BINARY_BERT class
"""
model = model.lower()
size = size.lower()
if model not in _availability:
raise Exception(
'model not supported, please check supported models from malaya.sentiment.available_transformer_model()'
)
if size not in _availability[model]:
raise Exception(
'size not supported, please check supported models from malaya.sentiment.available_transformer_model()'
)
check_file(PATH_TOXIC[model][size], S3_PATH_TOXIC[model][size], **kwargs)
g = load_graph(PATH_TOXIC[model][size]['model'])
if model in ['albert', 'bert']:
if model == 'bert':
from ._transformer._bert import _extract_attention_weights_import
if model == 'albert':
from ._transformer._albert import _extract_attention_weights_import
tokenizer, cls, sep = sentencepiece_tokenizer_bert(
PATH_TOXIC[model][size]['tokenizer'],
PATH_TOXIC[model][size]['vocab'],
)
return SIGMOID_BERT(
X=g.get_tensor_by_name('import/Placeholder:0'),
segment_ids=None,
input_masks=None,
logits=g.get_tensor_by_name('import/logits:0'),
logits_seq=g.get_tensor_by_name('import/logits_seq:0'),
sess=generate_session(graph=g),
tokenizer=tokenizer,
label=_label_toxic,
cls=cls,
sep=sep,
attns=_extract_attention_weights_import(bert_num_layers[size], g),
class_name='toxic',
)
if model in ['xlnet']:
from ._transformer._xlnet import _extract_attention_weights_import
tokenizer = sentencepiece_tokenizer_xlnet(
PATH_TOXIC[model][size]['tokenizer'])
return SIGMOID_XLNET(
X=g.get_tensor_by_name('import/Placeholder:0'),
segment_ids=g.get_tensor_by_name('import/Placeholder_1:0'),
input_masks=g.get_tensor_by_name('import/Placeholder_2:0'),
logits=g.get_tensor_by_name('import/logits:0'),
logits_seq=g.get_tensor_by_name('import/logits_seq:0'),
sess=generate_session(graph=g),
tokenizer=tokenizer,
label=_label_toxic,
attns=_extract_attention_weights_import(g),
class_name='toxic',
)
def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):
"""
Load Transformer toxicity 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.model.bert.SIGMOID_BERT class
"""
model = model.lower()
if model not in _transformer_availability:
raise Exception(
'model not supported, please check supported models from `malaya.toxicity.available_transformer()`.'
)
check_file(
PATH_TOXIC[model], S3_PATH_TOXIC[model], quantized = quantized, **kwargs
)
if quantized:
model_path = 'quantized'
else:
model_path = 'model'
g = load_graph(PATH_TOXIC[model][model_path], **kwargs)
path = PATH_TOXIC
if model in ['albert', 'bert', 'tiny-albert', 'tiny-bert']:
if model in ['bert', 'tiny-bert']:
from malaya.transformers.bert import (
_extract_attention_weights_import,
)
from malaya.transformers.bert import bert_num_layers
tokenizer = sentencepiece_tokenizer_bert(
path[model]['tokenizer'], path[model]['vocab']
)
if model in ['albert', 'tiny-albert']:
from malaya.transformers.albert import (
_extract_attention_weights_import,
)
from malaya.transformers.albert import bert_num_layers
from albert import tokenization
tokenizer = tokenization.FullTokenizer(
vocab_file = path[model]['vocab'],
do_lower_case = False,
spm_model_file = path[model]['tokenizer'],
)
return SIGMOID_BERT(
X = g.get_tensor_by_name('import/Placeholder:0'),
segment_ids = None,
input_masks = g.get_tensor_by_name('import/Placeholder_1:0'),
logits = g.get_tensor_by_name('import/logits:0'),
logits_seq = g.get_tensor_by_name('import/logits_seq:0'),
vectorizer = g.get_tensor_by_name('import/dense/BiasAdd:0'),
sess = generate_session(graph = g, **kwargs),
tokenizer = tokenizer,
label = label,
attns = _extract_attention_weights_import(
bert_num_layers[model], g
),
class_name = 'toxic',
)
if model in ['xlnet', 'alxlnet']:
if model in ['xlnet']:
from malaya.transformers.xlnet import (
_extract_attention_weights_import,
)
if model in ['alxlnet']:
from malaya.transformers.alxlnet import (
_extract_attention_weights_import,
)
tokenizer = sentencepiece_tokenizer_xlnet(path[model]['tokenizer'])
return SIGMOID_XLNET(
X = g.get_tensor_by_name('import/Placeholder:0'),
segment_ids = g.get_tensor_by_name('import/Placeholder_1:0'),
input_masks = g.get_tensor_by_name('import/Placeholder_2:0'),
logits = g.get_tensor_by_name('import/logits:0'),
logits_seq = g.get_tensor_by_name('import/logits_seq:0'),
vectorizer = g.get_tensor_by_name('import/transpose_3:0'),
sess = generate_session(graph = g, **kwargs),
tokenizer = tokenizer,
label = label,
attns = _extract_attention_weights_import(g),
class_name = 'toxic',
)
def transformer(model: str = 'xlnet', **kwargs):
"""
Load Transformer toxicity 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 : malaya.model.bert.SIGMOID_BERT class
"""
model = model.lower()
if model not in _availability:
raise Exception(
'model not supported, please check supported models from malaya.sentiment.available_transformer()'
)
check_file(PATH_TOXIC[model], S3_PATH_TOXIC[model], **kwargs)
g = load_graph(PATH_TOXIC[model]['model'])
path = PATH_TOXIC
if model in ['albert', 'bert', 'tiny-albert', 'tiny-bert']:
if model in ['bert', 'tiny-bert']:
from malaya.transformers.bert import (
_extract_attention_weights_import, )
from malaya.transformers.bert import bert_num_layers
tokenizer = sentencepiece_tokenizer_bert(path[model]['tokenizer'],
path[model]['vocab'])
if model in ['albert', 'tiny-albert']:
from malaya.transformers.albert import (
_extract_attention_weights_import, )
from malaya.transformers.albert import bert_num_layers
from albert import tokenization
tokenizer = tokenization.FullTokenizer(
vocab_file=path[model]['vocab'],
do_lower_case=False,
spm_model_file=path[model]['tokenizer'],
)
return SIGMOID_BERT(
X=g.get_tensor_by_name('import/Placeholder:0'),
segment_ids=None,
input_masks=g.get_tensor_by_name('import/Placeholder_1:0'),
logits=g.get_tensor_by_name('import/logits:0'),
logits_seq=g.get_tensor_by_name('import/logits_seq:0'),
sess=generate_session(graph=g),
tokenizer=tokenizer,
label=label,
attns=_extract_attention_weights_import(bert_num_layers[model], g),
class_name='toxic',
)
if model in ['xlnet', 'alxlnet']:
if model in ['xlnet']:
from malaya.transformers.xlnet import (
_extract_attention_weights_import, )
if model in ['alxlnet']:
from malaya.transformers.alxlnet import (
_extract_attention_weights_import, )
tokenizer = sentencepiece_tokenizer_xlnet(path[model]['tokenizer'])
return SIGMOID_XLNET(
X=g.get_tensor_by_name('import/Placeholder:0'),
segment_ids=g.get_tensor_by_name('import/Placeholder_1:0'),
input_masks=g.get_tensor_by_name('import/Placeholder_2:0'),
logits=g.get_tensor_by_name('import/logits:0'),
logits_seq=g.get_tensor_by_name('import/logits_seq:0'),
sess=generate_session(graph=g),
tokenizer=tokenizer,
label=label,
attns=_extract_attention_weights_import(g),
class_name='toxic',
)