JAV to MS HuggingFace

This tutorial is available as an IPython notebook at Malaya/example/jav-ms-translation-huggingface.

This module trained on standard language and augmented local language structures, proceed with caution.

import os

os.environ['CUDA_VISIBLE_DEVICES'] = ''

%%time

import malaya
import logging

logging.basicConfig(level=logging.INFO)

CPU times: user 4.1 s, sys: 3.32 s, total: 7.42 s
Wall time: 3.35 s

List available HuggingFace models

malaya.translation.jav_ms.available_huggingface()

INFO:malaya.translation.jav_ms:tested on FLORES200 JAV-MS (jav_Latn-zsm_Latn) pair `dev` set, https://github.com/facebookresearch/flores/tree/main/flores200

	Size (MB)	BLEU	SacreBLEU Verbose	SacreBLEU-chrF++-FLORES200	Suggested length
mesolitica/finetune-translation-austronesian-t5-tiny-standard-bahasa-cased	139	23.797628	58.2/31.1/18.1/10.8 (BP = 0.977 ratio = 0.977 ...	50.65	512
mesolitica/finetune-translation-austronesian-t5-small-standard-bahasa-cased	242	25.244377	57.7/31.9/19.0/11.6 (BP = 1.000 ratio = 1.022 ...	52.58	512
mesolitica/finetune-translation-austronesian-t5-base-standard-bahasa-cased	892	25.772897	58.9/32.6/19.6/12.1 (BP = 0.992 ratio = 0.992 ...	52.21	512

Load Transformer models

def huggingface(
    model: str = 'mesolitica/finetune-translation-austronesian-t5-small-standard-bahasa-cased',
    force_check: bool = True,
    **kwargs,
):
    """
    Load HuggingFace model to translate JAV-to-MS.

    Parameters
    ----------
    model: str, optional (default='mesolitica/finetune-translation-t5-small-standard-bahasa-cased')
        Check available models at `malaya.translation.jav_ms.available_huggingface()`.

    Returns
    -------
    result: malaya.torch_model.huggingface.Generator
    """

transformer_huggingface = malaya.translation.jav_ms.huggingface()

Translate

def generate(self, strings: List[str], **kwargs):
    """
    Generate texts from the input.

    Parameters
    ----------
    strings : List[str]
    **kwargs: vector arguments pass to huggingface `generate` method.
        Read more at https://huggingface.co/docs/transformers/main_classes/text_generation

    Returns
    -------
    result: List[str]
    """

For better results, always split by end of sentences.

from pprint import pprint

text = 'Aku ora seneng lele lan pitik goreng'

%%time

pprint(transformer_huggingface.generate([text],
                                       max_length = 1000))

You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.

['Saya tidak suka lele dan ayam goreng']
CPU times: user 1.08 s, sys: 0 ns, total: 1.08 s
Wall time: 95.1 ms