from malaya.supervised import classification
from malaya.supervised.huggingface import load
from malaya.torch_model.huggingface import Classification
from malaya.path import PATH_SENTIMENT, S3_PATH_SENTIMENT
label = ['negative', 'neutral', 'positive']
available_huggingface = {
'mesolitica/sentiment-analysis-nanot5-tiny-malaysian-cased': {
'Size (MB)': 93,
'macro precision': 0.67768,
'macro recall': 0.68266,
'macro f1-score': 0.67997,
},
'mesolitica/sentiment-analysis-nanot5-small-malaysian-cased': {
'Size (MB)': 167,
'macro precision': 0.67602,
'macro recall': 0.67120,
'macro f1-score': 0.67339,
}
}
info = """
Trained on https://huggingface.co/datasets/mesolitica/chatgpt-explain-sentiment
Split 90% to train, 10% to test.
""".strip()
[docs]def multinomial(**kwargs):
"""
Load multinomial sentiment model.
Returns
-------
result : malaya.model.ml.Bayes class
"""
return classification.multinomial(
path=PATH_SENTIMENT,
s3_path=S3_PATH_SENTIMENT,
module='sentiment',
label=label,
**kwargs
)
[docs]def huggingface(
model: str = 'mesolitica/sentiment-analysis-nanot5-small-malaysian-cased',
force_check: bool = True,
**kwargs,
):
"""
Load HuggingFace model to classify sentiment.
Parameters
----------
model: str, optional (default='mesolitica/sentiment-analysis-nanot5-small-malaysian-cased')
Check available models at `malaya.sentiment.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.Classification
"""
return load(
model=model,
class_model=Classification,
available_huggingface=available_huggingface,
force_check=force_check,
path=__name__,
**kwargs,
)
