<jupyter_start><jupyter_text>List available language detected<jupyter_code>malaya.language_detection.label()
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'<jupyter_output><empty_output><jupyter_text>Load multinomial model<jupyter_code>multinomial = malaya.language_detection.multinomial()
multinomial.predict(chinese_text,get_proba=True)
multinomial.predict(english_text)
multinomial.predict(indon_text)
multinomial.predict(malay_text)
multinomial.predict(malay_text,get_proba=True)
multinomial.predict_batch([english_text,malay_text])
multinomial.predict_batch([indon_text,malay_text],get_proba=True)<jupyter_output><empty_output><jupyter_text>Load SGD model<jupyter_code>sgd = malaya.language_detection.sgd()
sgd.predict(chinese_text,get_proba=True)
sgd.predict(english_text)
sgd.predict(indon_text)
sgd.predict(malay_text)
sgd.predict(malay_text,get_proba=True)
sgd.predict_batch([english_text,malay_text])
sgd.predict_batch([indon_text,malay_text],get_proba=True)<jupyter_output><empty_output><jupyter_text>Load XGB model<jupyter_code>xgb = malaya.language_detection.xgb()
xgb.predict(chinese_text)
xgb.predict(indon_text,get_proba=True)
xgb.predict_batch([indon_text,malay_text],get_proba=True)<jupyter_output><empty_output><jupyter_text>Load Deep learning model<jupyter_code>deep = malaya.language_detection.deep_model()
deep.predict(chinese_text)
deep.predict(indon_text)
deep.predict(malay_text)
deep.predict_batch([indon_text,malay_text])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09686147186147186, 'fraction_numerical': 0.0016233766233766235, 'mean_word_length': 13.792, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '135823', 'license': 'MIT', 'n_tokens_mistral': 750, 'n_tokens_neox': 712, 'n_words': 86, 'path': 'example/language-detection/load-language-detection.ipynb', 'repo_name': 'fossabot/Malaya', 'size': '1848'}