mesolitica
/

nanot5-base-malaysian-translation-v2

@@ -3,197 +3,79 @@ library_name: transformers
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 tags: []
 ---
+# NanoT5 Base Malaysian Translation
+Finetuned https://huggingface.co/mesolitica/nanot5-base-malaysian-cased using 2048 context length on 7B tokens of translation dataset.
+- This model able to translate from localize text into standard text.
+- This model able to reverse translate from standard to localize text, suitable for text augmentation.
+- This model able to translate code.
+- This model natively code switching.
+- This model maintain `\n`, `\t`, `\r` as it is.
+**Still in training session**, Wandb at https://wandb.ai/huseinzol05/nanot5-base-malaysian-cased-translation-v4?nw=nwuserhuseinzol05
+## Supported prefix
+1. `'terjemah ke Mandarin: '`
+3. `'terjemah ke Tamil: '`
+4. `'terjemah ke Jawa: '`
+5. `'terjemah ke Melayu: '`
+6. `'terjemah ke Inggeris: '`
+7. `'terjemah ke johor: '`
+8. `'terjemah ke kedah: '`
+9. `'terjemah ke kelantan: '`
+10. `'terjemah ke pasar Melayu: '`
+11. `'terjemah ke melaka: '`
+12. `'terjemah ke negeri sembilan: '`
+13. `'terjemah ke pahang: '`
+14. `'terjemah ke perak: '`
+15. `'terjemah ke sabah: '`
+16. `'terjemah ke sarawak: '`
+17. `'terjemah ke terengganu: '`
+18. `'terjemah ke Jawi: '`
+19. `'terjemah ke Manglish: '`
+20. `'terjemah ke Banjar: '`
+21. `'terjemah ke pasar Mandarin: '`
+## how to
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/nanot5-base-malaysian-translation-v2')
+model = T5ForConditionalGeneration.from_pretrained('mesolitica/nanot5-base-malaysian-translation-v2')
+strings = [
+    'ak tak paham la',
+    'Hi guys! I noticed semalam & harini dah ramai yang dapat cookies ni kan. So harini i nak share some post mortem of our first batch:',
+    "Memanglah. Ini tak payah expert, aku pun tau. It's a gesture, bodoh.",
+    'jam 8 di pasar KK memang org ramai 😂, pandai dia pilih tmpt.',
+    'Jadi haram jadah😀😃🤭',
+    'nak gi mana tuu',
+    'Macam nak ambil half day',
+    "Bayangkan PH dan menang pru-14. Pastu macam-macam pintu belakang ada. Last-last Ismail Sabri naik. That's why I don't give a fk about politics anymore. Sumpah dah fk up dah.",
+]
+all_special_ids = [0, 1, 2]
+prefix = 'terjemah ke Melayu: '
+input_ids = [{'input_ids': tokenizer.encode(f'{prefix}{s}{tokenizer.eos_token}', return_tensors='pt')[
+    0]} for s in strings]
+padded = tokenizer.pad(input_ids, padding='longest')
+outputs = model.generate(**padded, max_length = 100)
+tokenizer.batch_decode([[i for i in o if i not in all_special_ids] for o in outputs])
+```
+Output,
+```
+[' Saya tidak faham',
+ ' Hi guys! Saya perasan semalam dan hari ini ramai yang menerima cookies. Jadi hari ini saya ingin berkongsi beberapa post mortem batch pertama kami:',
+ ' Memanglah. Tak perlu pakar, saya juga tahu. Ini adalah satu isyarat, bodoh.',
+ ' Orang ramai di pasar KK pada jam 8 pagi, mereka sangat pandai memilih tempat.',
+ ' Jadi haram jadah 😀😃🤭',
+ ' Di mana kamu pergi?',
+ ' Saya ingin mengambil separuh hari',
+ ' Bayangkan PH dan menang PRU-14. Terdapat pelbagai pintu belakang. Akhirnya, Ismail Sabri naik. Itulah sebabnya saya tidak lagi bercakap tentang politik. Saya bersumpah sudah berputus asa.']
+```
+Input text can be any languages that speak in Malaysia, as long you use proper prefix, it should be able to translate to target language.