Language Detection¶

This tutorial is available as an IPython notebook at Malaya/example/language-detection.

This module trained on both standard and local (included social media) language structures, so it is save to use for both.

[1]:

%%time
import malaya
import fasttext

CPU times: user 5.72 s, sys: 1.14 s, total: 6.87 s
Wall time: 8.29 s

Models accuracy¶

We use sklearn.metrics.classification_report for accuracy reporting, check at https://malaya.readthedocs.io/en/latest/models-accuracy.html#language-detection

labels supported¶

Default labels for language detection module.

[2]:

malaya.language_detection.label

[2]:

['eng', 'ind', 'malay', 'manglish', 'other', 'rojak']

[4]:

chinese_text = '今天是６月１８号，也是Muiriel的生日！'
english_text = 'i totally love it man'
indon_text = 'menjabat saleh perombakan menjabat periode komisi energi fraksi partai pengurus partai periode periode partai terpilih periode menjabat komisi perdagangan investasi persatuan periode'
malay_text = 'beliau berkata program Inisitif Peduli Rakyat (IPR) yang diperkenalkan oleh kerajaan negeri Selangor lebih besar sumbangannya'
socialmedia_malay_text = 'nti aku tengok dulu tiket dari kl pukul berapa ada nahh'
socialmedia_indon_text = 'saking kangen papanya pas vc anakku nangis'
rojak_text = 'jadi aku tadi bikin ini gengs dan dijual haha salad only k dan haha drinks only k'
manglish_text = 'power lah even shopback come to edmw riao'

Load Fast-text model¶

Make sure fast-text already installed, if not, simply,

pip install fasttext

def fasttext(quantized: bool = True, **kwargs):

    """
    Load Fasttext language detection model.
    Original size is 353MB, Quantized size 31.1MB.

    Parameters
    ----------
    quantized: bool, optional (default=True)
        if True, load quantized fasttext model. Else, load original fasttext model.

    Returns
    -------
    result : malaya.model.ml.LanguageDetection class
    """

In this example, I am going to compare with pretrained fasttext from Facebook. https://fasttext.cc/docs/en/language-identification.html

Simply download pretrained model,

wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz

[4]:

model = fasttext.load_model('lid.176.ftz')
fast_text = malaya.language_detection.fasttext()

[5]:

model.predict(['តើប្រព័ន្ធប្រតិបត្តិការណាដែលត្រូវគ្នាជាមួយកម្មវិធីធនាគារអេប៊ីអេ។'])

[5]:

([['__label__km']], array([[0.99841499]]))

[6]:

fast_text.predict(['តើប្រព័ន្ធប្រតិបត្តិការណាដែលត្រូវគ្នាជាមួយកម្មវិធីធនាគារអេប៊ីអេ។'])

[6]:

['other']

Language detection in Malaya is not trying to tackle possible languages in this world, just towards to hyperlocal language.

[7]:

model.predict(['suka makan ayam dan daging'])

[7]:

([['__label__id']], array([[0.6334154]]))

[8]:

fast_text.predict_proba(['suka makan ayam dan daging'])

[8]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.8817721009254456,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0}]

[9]:

model.predict(malay_text)

[9]:

(('__label__ms',), array([0.57101035]))

[10]:

fast_text.predict_proba([malay_text])

[10]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.9999504089355469,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0}]

[11]:

model.predict(socialmedia_malay_text)

[11]:

(('__label__id',), array([0.7870034]))

[12]:

fast_text.predict_proba([socialmedia_malay_text])

[12]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.9996305704116821,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0}]

[13]:

model.predict(socialmedia_indon_text)

[13]:

(('__label__fr',), array([0.2912012]))

[14]:

fast_text.predict_proba([socialmedia_indon_text])

[14]:

[{'eng': 0.0,
  'ind': 1.0000293254852295,
  'malay': 0.0,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0}]

[15]:

model.predict(rojak_text)

[15]:

(('__label__id',), array([0.87948251]))

[16]:

fast_text.predict_proba([rojak_text])

[16]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.0,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.9994134306907654}]

[17]:

model.predict(manglish_text)

[17]:

(('__label__en',), array([0.89707506]))

[18]:

fast_text.predict_proba([manglish_text])

[18]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.0,
  'manglish': 1.00004243850708,
  'other': 0.0,
  'rojak': 0.0}]

[19]:

model.predict(chinese_text)

[19]:

(('__label__zh',), array([0.97311586]))

[20]:

fast_text.predict_proba([chinese_text])

[20]:

[{'eng': 0.0,
  'ind': 0.0,
  'malay': 0.0,
  'manglish': 0.0,
  'other': 0.9921814203262329,
  'rojak': 0.0}]

[21]:

fast_text.predict_proba([indon_text,malay_text])

[21]:

[{'eng': 0.0,
  'ind': 1.0000287294387817,
  'malay': 0.0,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0},
 {'eng': 0.0,
  'ind': 0.0,
  'malay': 0.9999504089355469,
  'manglish': 0.0,
  'other': 0.0,
  'rojak': 0.0}]

Load Deep learning model¶

def deep_model(quantized: bool = False, **kwargs):
    """
    Load deep learning language detection model.
    Original size is 51.2MB, Quantized size 12.8MB.

    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 : malaya.model.tf.DeepLang class
    """

[5]:

deep = malaya.language_detection.deep_model()
quantized_deep = malaya.language_detection.deep_model(quantized = True)

[6]:

deep.predict_proba([indon_text])

[6]:

[{'eng': 3.6145184e-06,
  'ind': 0.9998913,
  'malay': 5.4685424e-05,
  'manglish': 5.768742e-09,
  'other': 5.8103424e-06,
  'rojak': 4.4987162e-05}]

[7]:

quantized_deep.predict_proba([indon_text])

[7]:

[{'eng': 3.6145184e-06,
  'ind': 0.9998913,
  'malay': 5.4685424e-05,
  'manglish': 5.768742e-09,
  'other': 5.8103424e-06,
  'rojak': 4.4987162e-05}]

[24]:

deep.predict_proba([malay_text])

[24]:

[{'eng': 9.500837e-11,
  'ind': 0.0004703698,
  'malay': 0.9991295,
  'manglish': 1.602048e-13,
  'other': 1.9133091e-07,
  'rojak': 0.0004000054}]

[8]:

quantized_deep.predict_proba([malay_text])

[8]:

[{'eng': 9.500829e-11,
  'ind': 0.00047036994,
  'malay': 0.99912965,
  'manglish': 1.6020499e-13,
  'other': 1.9133095e-07,
  'rojak': 0.00040000546}]

[25]:

deep.predict_proba([indon_text,malay_text])

[25]:

[{'eng': 3.6145207e-06,
  'ind': 0.9998909,
  'malay': 5.468535e-05,
  'manglish': 5.7687397e-09,
  'other': 5.8103406e-06,
  'rojak': 4.4987148e-05},
 {'eng': 9.500837e-11,
  'ind': 0.0004703698,
  'malay': 0.9991295,
  'manglish': 1.602048e-13,
  'other': 1.9133091e-07,
  'rojak': 0.0004000056}]

[9]:

quantized_deep.predict_proba([indon_text,malay_text])

[9]:

[{'eng': 3.614522e-06,
  'ind': 0.9998913,
  'malay': 5.4685373e-05,
  'manglish': 5.768742e-09,
  'other': 5.8103424e-06,
  'rojak': 4.4987162e-05},
 {'eng': 9.500829e-11,
  'ind': 0.00047036994,
  'malay': 0.99912965,
  'manglish': 1.6020499e-13,
  'other': 1.9133095e-07,
  'rojak': 0.0004000057}]

[26]:

deep.predict_proba([socialmedia_malay_text])

[26]:

[{'eng': 1.4520887e-09,
  'ind': 0.0064318455,
  'malay': 0.9824693,
  'manglish': 2.1923141e-13,
  'other': 1.06363805e-05,
  'rojak': 0.0110881105}]

[10]:

quantized_deep.predict_proba([socialmedia_malay_text])

[10]:

[{'eng': 1.4520903e-09,
  'ind': 0.006431847,
  'malay': 0.98246956,
  'manglish': 2.1923168e-13,
  'other': 1.0636383e-05,
  'rojak': 0.011088113}]

[27]:

deep.predict_proba([socialmedia_indon_text])

[27]:

[{'eng': 4.0632068e-07,
  'ind': 0.9999995,
  'malay': 6.871639e-10,
  'manglish': 7.4285925e-11,
  'other': 1.5928721e-07,
  'rojak': 4.892652e-10}]

[28]:

deep.predict_proba([rojak_text, malay_text])

[28]:

[{'eng': 0.0040922514,
  'ind': 0.02200061,
  'malay': 0.0027574676,
  'manglish': 9.336553e-06,
  'other': 0.00023811469,
  'rojak': 0.97090226},
 {'eng': 9.500837e-11,
  'ind': 0.0004703698,
  'malay': 0.9991295,
  'manglish': 1.602048e-13,
  'other': 1.9133091e-07,
  'rojak': 0.0004000056}]