SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Added README huseinzol05/albert-tiny-bahasa-cased (#3746)

Browse Source 
* add bert bahasa readme

* update readme

* update readme

* added xlnet

* added tiny-bert and fix xlnet readme

* added albert base

* added albert tiny
This commit is contained in:
HUSEIN ZOLKEPLI
2020-04-11 18:42:06 +08:00
committed by GitHub
parent 700ccf6e35
commit f8c1071c51
1 changed files with 86 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

86
model_cards/huseinzol05/albert-tiny-bahasa-cased/README.md Normal file
View File

@@ -0,0 +1,86 @@
---
language: malay
---
# Bahasa Albert Model
Pretrained Albert tiny language model for Malay and Indonesian, 85% faster execution and 50% smaller than Albert base.
## Pretraining Corpus
`albert-tiny-bahasa-cased` model was pretrained on ~1.8 Billion words. We trained on both standard and social media language structures, and below is list of data we trained on,
1. [dumping wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
2. [local instagram](https://github.com/huseinzol05/Malaya-Dataset#instagram).
3. [local twitter](https://github.com/huseinzol05/Malaya-Dataset#twitter-1).
4. [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
5. [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
6. [local singlish/manglish text](https://github.com/huseinzol05/Malaya-Dataset#singlish-text).
7. [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
8. [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
9. [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/preprocess).
## Pretraining details
- This model was trained using Google Albert's github [repository](https://github.com/google-research/ALBERT) on v3-8 TPU.
- All steps can reproduce from here, [Malaya/pretrained-model/albert](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/albert).
## Load Pretrained Model
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:
```python
from transformers import AlbertTokenizer, AlbertModel
model = BertModel.from_pretrained('huseinzol05/albert-tiny-bahasa-cased')
tokenizer = AlbertTokenizer.from_pretrained(
'huseinzol05/albert-tiny-bahasa-cased',
do_lower_case = False,
)
```
## Example using AutoModelWithLMHead
```python
from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained('huseinzol05/albert-tiny-bahasa-cased')
tokenizer = AlbertTokenizer.from_pretrained(
'huseinzol05/albert-tiny-bahasa-cased',
do_lower_case = False,
)
fill_mask = pipeline('fill-mask', model = model, tokenizer = tokenizer)
print(fill_mask('makan ayam dengan [MASK]'))
```
Output is,
```text
[{'sequence': '[CLS] makan ayam dengan ayam[SEP]',
'score': 0.05121927708387375,
'token': 629},
{'sequence': '[CLS] makan ayam dengan sayur[SEP]',
'score': 0.04497420787811279,
'token': 1639},
{'sequence': '[CLS] makan ayam dengan nasi[SEP]',
'score': 0.039827536791563034,
'token': 453},
{'sequence': '[CLS] makan ayam dengan rendang[SEP]',
'score': 0.032997727394104004,
'token': 2451},
{'sequence': '[CLS] makan ayam dengan makan[SEP]',
'score': 0.031354598701000214,
'token': 129}]
```
## Results
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.
## Acknowledgement
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 Albert for Bahasa.