How Malaya gathered corpus?¶
Note
This tutorial is available as an IPython notebook here.
We use a translator to translate from a validated English dataset to a Bahasa dataset.
Everyone agree that Google Translate is the best online translator in this world, but the problem here, to subscribe the API from Google Cloud is really insane expensive.
Good thing about https://translate.google.com/, it open for public internet! So we just code a headless browser using Selenium with PhantomJS as the backbone, that’s all!
You can check the source code here, translator/
from translate_selenium import Translate, Translate_Concurrent
Translate a sentence¶
with open('sample-joy') as fopen:
dataset = list(filter(None, fopen.read().split('\n')))
len(dataset)
18
translator = Translate(from_lang = 'en', to_lang = 'ms')
You can get list of supported language in here, https://cloud.google.com/translate/docs/languages
%%time
translator.translate(dataset[0])
CPU times: user 4 ms, sys: 0 ns, total: 4 ms
Wall time: 1.23 s
'seorang lelaki yang saya mengagumi begitu banyak meminta saya untuk pergi bersamanya'
1.23 seconds, it took a very long time to translate a single sentence. What if you have 100k of sentences? It will cost you around 123000 seconds! insane to wait!
So, we provide multihreading translator, concurrently translate multi sentences.
Translate batch of strings¶
translators = Translate_Concurrent(batch_size = 3, from_lang = 'en', to_lang = 'ms')
%%time
translators.translate_batch(dataset[:3])
100%|███████████████████████████████████| 1/1 [00:01<00:00, 1.44s/it]
CPU times: user 8 ms, sys: 12 ms, total: 20 ms
Wall time: 1.44 s
['kawan yang sudah berkahwin rapat hanya mempunyai anak pertamanya',
'pengenalan rapat menangis untuk saya saya merasa gembira kerana ada yang peduli',
'seorang lelaki yang saya mengagumi begitu banyak meminta saya untuk pergi bersamanya']
See, we predicted 3 sentences at almost wall time. You can increase the
batch_size to any size you want, limit is your spec now, this method
will never make Google blocked your IP. Malaya already tested it more
than 300k of sentences.
Remember, 1 translator took a quite toll, here I spawned 10 translators,
look from my top,
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
14628 husein 20 0 3175700 398980 43036 S 33.6 2.4 5:38.05 phantomjs
14652 husein 20 0 3188824 408880 43084 S 29.9 2.5 5:34.62 phantomjs
14489 husein 20 0 3204708 411520 43064 S 28.6 2.5 5:35.29 phantomjs
14466 husein 20 0 3171668 400304 43008 S 24.6 2.5 5:26.74 phantomjs
14443 husein 20 0 3181056 403228 42916 S 21.9 2.5 5:26.24 phantomjs
14512 husein 20 0 3187592 416036 42956 S 20.3 2.6 5:30.03 phantomjs
14558 husein 20 0 3206104 419800 43640 S 19.9 2.6 5:30.76 phantomjs
14535 husein 20 0 3179416 405508 43196 S 18.3 2.5 5:27.54 phantomjs
14420 husein 20 0 3202472 422448 43064 S 17.6 2.6 5:26.78 phantomjs
14581 husein 20 0 3181132 401892 43056 S 16.3 2.5 5:33.48 phantomjs
1 translator cost me around,
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
14628 husein 20 0 3175700 398980 43036 S 33.6 2.4 5:38.05 phantomjs
My machine specifications,
H/W path Device Class Description
======================================================
system G1.Sniper H6 (To be filled by O.E.M.)
/0 bus G1.Sniper H6
/0/3d processor Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz
/0/42 memory 16GiB System Memory
/0/42/0 memory DIMM [empty]
/0/42/1 memory 8GiB DIMM DDR3 Synchronous 1600 MHz (0.6 ns)
/0/42/2 memory DIMM [empty]
/0/42/3 memory 8GiB DIMM DDR3 Synchronous 1600 MHz (0.6 ns)
/0/100 bridge 4th Gen Core Processor DRAM Controller
/0/100/1 bridge Xeon E3-1200 v3/4th Gen Core Processor PCI Express x16 Controller
/0/100/1/0 display GM206 [GeForce GTX 960]
/0/100/1/0.1 multimedia NVIDIA Corporation
So, beware of your machine!