Source code for malaya.paraphrase

from malaya.supervised import t5 as t5_load
from malaya.supervised import transformer as transformer_load
from malaya.model.t5 import Paraphrase as T5_Paraphrase
from import Paraphrase as TF_Paraphrase
from herpetologist import check_type

_transformer_availability = {
    't5': {
        'Size (MB)': 1250,
        'Quantized Size (MB)': 481,
        'BLEU': 0.60890377
    'small-t5': {
        'Size (MB)': 355.6,
        'Quantized Size (MB)': 195,
        'BLEU': 0.6174561,
    'tiny-t5': {
        'Size (MB)': 208,
        'Quantized Size (MB)': 103,
        'BLEU': 0.46032128,

[docs]def available_transformer(): """ List available transformer models. """ from malaya.function import describe_availability return describe_availability( _transformer_availability, text='tested on ParaSCI test set.' )
[docs]@ check_type def transformer(model: str = 'small-t5', quantized: bool = False, **kwargs): """ Load Malaya transformer encoder-decoder model to generate a paraphrase given a string. Parameters ---------- model : str, optional (default='small-t5') Model architecture supported. Allowed values: * ``'t5'`` - T5 BASE parameters. * ``'small-t5'`` - T5 SMALL parameters. * ``'tiny-t5'`` - T5 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 `t5` in model, will return `malaya.model.t5.Paraphrase`. """ model = model.lower() if model not in _transformer_availability: raise ValueError( 'model not supported, please check supported models from `malaya.paraphrase.available_transformer()`.' ) return t5_load.load( module='paraphrase-v2', model=model, model_class=T5_Paraphrase, quantized=quantized, **kwargs, )