HUSEIN ZOLKEPLI
216e167ce6
* add bert bahasa readme * update readme * update readme * added xlnet * added tiny-bert and fix xlnet readme * added albert base
93 lines
3.7 KiB
Markdown
93 lines
3.7 KiB
Markdown
---
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language: malay
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---
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# Bahasa Tiny-BERT Model
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General Distilled Tiny BERT language model for Malay and Indonesian.
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## Pretraining Corpus
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`tiny-bert-bahasa-cased` model was distilled on ~1.8 Billion words. We distilled on both standard and social media language structures, and below is list of data we distilled on,
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1. [dumping wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
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2. [local instagram](https://github.com/huseinzol05/Malaya-Dataset#instagram).
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3. [local twitter](https://github.com/huseinzol05/Malaya-Dataset#twitter-1).
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4. [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
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5. [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
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6. [local singlish/manglish text](https://github.com/huseinzol05/Malaya-Dataset#singlish-text).
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7. [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
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8. [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
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9. [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
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Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/preprocess).
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## Distilling details
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- This model was distilled using huawei-noah Tiny-BERT's github [repository](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT) on 3 Titan V100 32GB VRAM.
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- All steps can reproduce from here, [Malaya/pretrained-model/tiny-bert](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/tiny-bert).
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## Load Distilled Model
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You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
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```python
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from transformers import AlbertTokenizer, BertModel
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model = BertModel.from_pretrained('huseinzol05/tiny-bert-bahasa-cased')
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tokenizer = AlbertTokenizer.from_pretrained(
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'huseinzol05/tiny-bert-bahasa-cased',
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unk_token = '[UNK]',
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pad_token = '[PAD]',
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do_lower_case = False,
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)
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```
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We use [google/sentencepiece](https://github.com/google/sentencepiece) to train the tokenizer, so to use it, need to load from `AlbertTokenizer`.
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## Example using AutoModelWithLMHead
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```python
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from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
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model = AutoModelWithLMHead.from_pretrained('huseinzol05/tiny-bert-bahasa-cased')
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tokenizer = AlbertTokenizer.from_pretrained(
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'huseinzol05/tiny-bert-bahasa-cased',
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unk_token = '[UNK]',
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pad_token = '[PAD]',
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do_lower_case = False,
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)
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fill_mask = pipeline('fill-mask', model = model, tokenizer = tokenizer)
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print(fill_mask('makan ayam dengan [MASK]'))
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```
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Output is,
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```text
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[{'sequence': '[CLS] makan ayam dengan berbual[SEP]',
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'score': 0.00015769545279908925,
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'token': 17859},
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{'sequence': '[CLS] makan ayam dengan kembar[SEP]',
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'score': 0.0001448775001335889,
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'token': 8289},
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{'sequence': '[CLS] makan ayam dengan memaklumkan[SEP]',
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'score': 0.00013484008377417922,
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'token': 6881},
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{'sequence': '[CLS] makan ayam dengan Senarai[SEP]',
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'score': 0.00013061291247140616,
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'token': 11698},
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{'sequence': '[CLS] makan ayam dengan Tiga[SEP]',
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'score': 0.00012453157978598028,
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'token': 4232}]
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```
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## Results
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For further details on the model performance, simply checkout accuracy page from Malaya, https://malaya.readthedocs.io/en/latest/Accuracy.html, we compared with traditional models.
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## Acknowledgement
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Thanks to [Im Big](https://www.facebook.com/imbigofficial/), [LigBlou](https://www.facebook.com/ligblou), [Mesolitica](https://mesolitica.com/) and [KeyReply](https://www.keyreply.com/) for sponsoring AWS, Google and GPU clouds to train BERT for Bahasa.
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