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Update model card huseinzol05/bert-base-bahasa-cased (#3425)

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* add bert bahasa readme

* update readme

* update readme

* added xlnet
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HUSEIN ZOLKEPLI
2020-03-26 19:50:27 +08:00
committed by GitHub
parent 9c683ef01e
commit 3c5c567507
2 changed files with 108 additions and 3 deletions
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model_cards/huseinzol05/bert-base-bahasa-cased/README.md
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@@ -32,13 +32,54 @@ Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess
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 XLNetTokenizer, BertModel
from transformers import AlbertTokenizer, BertModel
model = BertModel.from_pretrained('huseinzol05/bert-base-bahasa-cased')
tokenizer = XLNetTokenizer.from_pretrained('huseinzol05/bert-base-bahasa-cased')
tokenizer = AlbertTokenizer.from_pretrained(
'huseinzol05/bert-base-bahasa-cased',
unk_token = '[UNK]',
pad_token = '[PAD]',
do_lower_case = False,
)
```
We use [google/sentencepiece](https://github.com/google/sentencepiece) to train the tokenizer, so to use it, need to load from `XLNetTokenizer`.
We use [google/sentencepiece](https://github.com/google/sentencepiece) to train the tokenizer, so to use it, need to load from `AlbertTokenizer`.
## Example using AutoModelWithLMHead
```python
from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained('huseinzol05/bert-base-bahasa-cased')
tokenizer = AlbertTokenizer.from_pretrained(
'huseinzol05/bert-base-bahasa-cased',
unk_token = '[UNK]',
pad_token = '[PAD]',
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 rendang[SEP]',
'score': 0.10812027007341385,
'token': 2446},
{'sequence': '[CLS] makan ayam dengan kicap[SEP]',
'score': 0.07653367519378662,
'token': 12928},
{'sequence': '[CLS] makan ayam dengan nasi[SEP]',
'score': 0.06839974224567413,
'token': 450},
{'sequence': '[CLS] makan ayam dengan ayam[SEP]',
'score': 0.059544261544942856,
'token': 638},
{'sequence': '[CLS] makan ayam dengan sayur[SEP]',
'score': 0.05294966697692871,
'token': 1639}]
```
## Results

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model_cards/huseinzol05/xlnet-base-bahasa-cased/README.md Normal file
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@@ -0,0 +1,64 @@
---
language: malay
---
# Bahasa XLNet Model
Pretrained XLNet base language model for Malay and Indonesian.
## Pretraining Corpus
`XLNET-base-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 zihangdai XLNet's github [repository](https://github.com/zihangdai/xlnet) on 3 Titan V100 32GB VRAM.
- All steps can reproduce from here, [Malaya/pretrained-model/xlnet](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/xlnet).
## 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 XLNetTokenizer, XLNetModel
model = XLNetModel.from_pretrained('huseinzol05/xlnet-base-bahasa-cased')
tokenizer = XLNetTokenizer.from_pretrained(
'huseinzol05/xlnet-base-bahasa-cased', do_lower_case = False
)
```
## Example using AutoModelWithLMHead
```python
from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained('huseinzol05/xlnet-base-bahasa-cased')
tokenizer = XLNetTokenizer.from_pretrained(
'huseinzol05/xlnet-base-bahasa-cased', do_lower_case = False
)
fill_mask = pipeline('fill-mask', model = model, tokenizer = tokenizer)
print(fill_mask('makan ayam dengan [MASK]'))
```
## 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 XLNet for Bahasa.