Source code for malaya.emotion

from malaya.supervised import classification
from malaya.path import PATH_EMOTION, S3_PATH_EMOTION
from herpetologist import check_type

label = ['anger', 'fear', 'happy', 'love', 'sadness', 'surprise']
label_goemotions = [
    'admiration',
    'amusement',
    'anger',
    'annoyance',
    'approval',
    'caring',
    'confusion',
    'curiosity',
    'desire',
    'disappointment',
    'disapproval',
    'disgust',
    'embarrassment',
    'excitement',
    'fear',
    'gratitude',
    'grief',
    'joy',
    'love',
    'nervousness',
    'optimism',
    'pride',
    'realization',
    'relief',
    'remorse',
    'sadness',
    'surprise',
    'neutral'
]

_transformer_availability = {
    'bert': {
        'Size (MB)': 425.6,
        'Quantized Size (MB)': 111,
        'macro precision': 0.99786,
        'macro recall': 0.99773,
        'macro f1-score': 0.99779,
    },
    'tiny-bert': {
        'Size (MB)': 57.4,
        'Quantized Size (MB)': 15.4,
        'macro precision': 0.99692,
        'macro recall': 0.99696,
        'macro f1-score': 0.99694,
    },
    'albert': {
        'Size (MB)': 48.6,
        'Quantized Size (MB)': 12.8,
        'macro precision': 0.99740,
        'macro recall': 0.99773,
        'macro f1-score': 0.99757,
    },
    'tiny-albert': {
        'Size (MB)': 22.4,
        'Quantized Size (MB)': 5.98,
        'macro precision': 0.99325,
        'macro recall': 0.99378,
        'macro f1-score': 0.99351,
    },
    'xlnet': {
        'Size (MB)': 446.5,
        'Quantized Size (MB)': 118,
        'macro precision': 0.99773,
        'macro recall': 0.99775,
        'macro f1-score': 0.99774,
    },
    'alxlnet': {
        'Size (MB)': 46.8,
        'Quantized Size (MB)': 13.3,
        'macro precision': 0.99663,
        'macro recall': 0.99697,
        'macro f1-score': 0.99680,
    },
    'fastformer': {
        'Size (MB)': 446,
        'Quantized Size (MB)': 113,
        'macro precision': 0.99197,
        'macro recall': 0.99194,
        'macro f1-score': 0.99195,
    },
    'tiny-fastformer': {
        'Size (MB)': 77.2,
        'Quantized Size (MB)': 19.6,
        'macro precision': 0.98926,
        'macro recall': 0.98783,
        'macro f1-score': 0.98853,
    }
}


[docs]def available_transformer(): """ List available transformer emotion analysis models. """ from malaya.function import describe_availability return describe_availability( _transformer_availability, text='tested on 20% test set.' )
[docs]def multinomial(**kwargs): """ Load multinomial emotion model. Returns ------- result : malaya.model.ml.MulticlassBayes class """ return classification.multinomial( path=PATH_EMOTION, s3_path=S3_PATH_EMOTION, module='emotion', label=label, **kwargs )
[docs]@check_type def transformer(model: str = 'xlnet', quantized: bool = False, **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. * ``'fastformer'`` - FastFormer BASE parameters. * ``'tiny-fastformer'`` - FastFormer TINY 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 `fastformer` in model, will return `malaya.model.fastformer.MulticlassFastFormer`. """ model = model.lower() if model not in _transformer_availability: raise ValueError( 'model not supported, please check supported models from `malaya.emotion.available_transformer()`.' ) return classification.transformer( module='emotion', label=label, model=model, quantized=quantized, **kwargs )