from malaya.supervised.huggingface import load
from malaya.torch_model.huggingface import Generator
available_huggingface = {
'mesolitica/finetune-segmentation-t5-super-tiny-standard-bahasa-cased': {
'Size (MB)': 51,
'WER': 0.030962535,
'CER': 0.0041129253,
'Suggested length': 256,
},
'mesolitica/finetune-segmentation-t5-tiny-standard-bahasa-cased': {
'Size (MB)': 139,
'WER': 0.0207876127,
'CER': 0.002146691161,
'Suggested length': 256,
},
'mesolitica/finetune-segmentation-t5-small-standard-bahasa-cased': {
'Size (MB)': 242,
'WER': 0.0202468274,
'CER': 0.0024325431,
'Suggested length': 256,
},
}
info = """
tested on random generated dataset at https://f000.backblazeb2.com/file/malay-dataset/segmentation/test-set-segmentation.json
""".strip()
[docs]def huggingface(
model: str = 'mesolitica/finetune-segmentation-t5-tiny-standard-bahasa-cased',
force_check: bool = True,
**kwargs,
):
"""
Load HuggingFace model to segmentation.
Parameters
----------
model: str, optional (default='mesolitica/finetune-segmentation-t5-tiny-standard-bahasa-cased')
Check available models at `malaya.segmentation.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.
Returns
-------
result: malaya.torch_model.huggingface.Generator
"""
if model not in available_huggingface and force_check:
raise ValueError(
'model not supported, please check supported models from `malaya.segmentation.available_huggingface`.'
)
return load(
model=model,
class_model=Generator,
available_huggingface=available_huggingface,
path=__name__,
initial_text='segmentasi: ',
**kwargs,
)
