from malaya.supervised.huggingface import load
from malaya.torch_model.huggingface import Transformer
available_huggingface = {
'mesolitica/roberta-base-bahasa-cased': {
'Size (MB)': 443,
},
'mesolitica/roberta-tiny-bahasa-cased': {
'Size (MB)': 66.1,
},
'mesolitica/bert-base-standard-bahasa-cased': {
'Size (MB)': 443,
},
'mesolitica/bert-tiny-standard-bahasa-cased': {
'Size (MB)': 66.1,
},
'mesolitica/roberta-base-standard-bahasa-cased': {
'Size (MB)': 443,
},
'mesolitica/roberta-tiny-standard-bahasa-cased': {
'Size (MB)': 66.1,
},
'mesolitica/electra-base-generator-bahasa-cased': {
'Size (MB)': 140,
},
'mesolitica/electra-small-generator-bahasa-cased': {
'Size (MB)': 19.3,
},
'mesolitica/malaysian-debertav2-base': {
'Size (MB)': 228,
},
}
[docs]def huggingface(
model: str = 'mesolitica/electra-base-generator-bahasa-cased',
**kwargs,
):
"""
Load transformer model.
Parameters
----------
model: str, optional (default='mesolitica/electra-base-generator-bahasa-cased')
Check available models at `malaya.transformer.available_huggingface`.
force_check: bool, optional (default=True)
Force check model one of malaya model.
Set to False if you have your own huggingface model.
"""
if model not in available_huggingface and force_check:
raise ValueError(
'model not supported, please check supported models from `malaya.transformer.available_huggingface`.'
)
return load(
model=model,
class_model=Transformer,
available_huggingface=available_huggingface,
path=__name__,
**kwargs,
)
