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added electra model

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(cherry picked from commit b5f2dc5d627d44b8cbb0ccf8ad2b46bea211a236)
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husein zolkepli
2020-04-19 16:47:09 +08:00
committed by Julien Chaumond
parent 03121deba3
commit 7f23af1684
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model_cards/huseinzol05/electra-base-discriminator-bahasa-cased/README.md Normal file
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---
language: malay
---
# Bahasa ELECTRA Model
Pretrained ELECTRA base language model for Malay and Indonesian.
## Pretraining Corpus
`electra-base-discriminator-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 ELECTRA's github [repository](https://github.com/google-research/electra) on a single TESLA V100 32GB VRAM.
- All steps can reproduce from here, [Malaya/pretrained-model/electra](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/electra).
## 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 ElectraTokenizer, ElectraModel
model = ElectraModel.from_pretrained('huseinzol05/electra-base-discriminator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-base-discriminator-bahasa-cased',
do_lower_case = False,
)
```
## Example using ElectraForPreTraining
```python
from transformers import ElectraTokenizer, AutoModelWithLMHead, pipeline
model = ElectraForPreTraining.from_pretrained('huseinzol05/electra-base-discriminator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-base-discriminator-bahasa-cased',
do_lower_case = False
)
sentence = 'kerajaan sangat prihatin terhadap rakyat'
fake_tokens = tokenizer.tokenize(sentence)
fake_inputs = tokenizer.encode(sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
list(zip(fake_tokens, predictions.tolist()))
```
Output is,
```text
[('kerajaan', 0.0),
('sangat', 0.0),
('prihatin', 0.0),
('terhadap', 0.0),
('rakyat', 0.0)]
```
## 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 ELECTRA for Bahasa.

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model_cards/huseinzol05/electra-base-generator-bahasa-cased/README.md Normal file
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---
language: malay
---
# Bahasa ELECTRA Model
Pretrained ELECTRA base language model for Malay and Indonesian.
## Pretraining Corpus
`electra-base-generator-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 ELECTRA's github [repository](https://github.com/google-research/electra) on v3-8 TPU.
- All steps can reproduce from here, [Malaya/pretrained-model/electra](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/electra).
## 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 ElectraTokenizer, ElectraModel
model = ElectraModel.from_pretrained('huseinzol05/electra-base-generator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-base-generator-bahasa-cased',
do_lower_case = False,
)
```
## Example using AutoModelWithLMHead
```python
from transformers import ElectraTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained('huseinzol05/electra-base-generator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-base-generator-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': 0.08424834907054901,
'token': 3255},
{'sequence': '[CLS] makan ayam dengan rendang [SEP]',
'score': 0.064150370657444,
'token': 6288},
{'sequence': '[CLS] makan ayam dengan nasi [SEP]',
'score': 0.033446669578552246,
'token': 2533},
{'sequence': '[CLS] makan ayam dengan kucing [SEP]',
'score': 0.02803465723991394,
'token': 3577},
{'sequence': '[CLS] makan ayam dengan telur [SEP]',
'score': 0.026627106592059135,
'token': 6350}]
```
## 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 ELECTRA for Bahasa.

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---
language: malay
---
# Bahasa ELECTRA Model
Pretrained ELECTRA small language model for Malay and Indonesian.
## Pretraining Corpus
`electra-small-discriminator-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 ELECTRA's github [repository](https://github.com/google-research/electra) on a single TESLA V100 32GB VRAM.
- All steps can reproduce from here, [Malaya/pretrained-model/electra](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/electra).
## 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 ElectraTokenizer, ElectraModel
model = ElectraModel.from_pretrained('huseinzol05/electra-small-discriminator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-small-discriminator-bahasa-cased',
do_lower_case = False,
)
```
## Example using ElectraForPreTraining
```python
from transformers import ElectraTokenizer, AutoModelWithLMHead, pipeline
model = ElectraForPreTraining.from_pretrained('huseinzol05/electra-small-discriminator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-small-discriminator-bahasa-cased',
do_lower_case = False
)
sentence = 'kerajaan sangat prihatin terhadap rakyat'
fake_tokens = tokenizer.tokenize(sentence)
fake_inputs = tokenizer.encode(sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
list(zip(fake_tokens, predictions.tolist()))
```
Output is,
```text
[('kerajaan', 0.0),
('sangat', 0.0),
('prihatin', 0.0),
('terhadap', 0.0),
('rakyat', 0.0)]
```
## 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 ELECTRA for Bahasa.

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model_cards/huseinzol05/electra-small-generator-bahasa-cased/README.md Normal file
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---
language: malay
---
# Bahasa ELECTRA Model
Pretrained ELECTRA small language model for Malay and Indonesian.
## Pretraining Corpus
`electra-small-generator-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 ELECTRA's github [repository](https://github.com/google-research/electra) on a single TESLA V100 32GB VRAM.
- All steps can reproduce from here, [Malaya/pretrained-model/electra](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/electra).
## 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 ElectraTokenizer, ElectraModel
model = ElectraModel.from_pretrained('huseinzol05/electra-small-generator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-small-generator-bahasa-cased',
do_lower_case = False,
)
```
## Example using AutoModelWithLMHead
```python
from transformers import ElectraTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained('huseinzol05/electra-small-generator-bahasa-cased')
tokenizer = ElectraTokenizer.from_pretrained(
'huseinzol05/electra-small-generator-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': 0.08424834907054901,
'token': 3255},
{'sequence': '[CLS] makan ayam dengan rendang [SEP]',
'score': 0.064150370657444,
'token': 6288},
{'sequence': '[CLS] makan ayam dengan nasi [SEP]',
'score': 0.033446669578552246,
'token': 2533},
{'sequence': '[CLS] makan ayam dengan kucing [SEP]',
'score': 0.02803465723991394,
'token': 3577},
{'sequence': '[CLS] makan ayam dengan telur [SEP]',
'score': 0.026627106592059135,
'token': 6350}]
```
## 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 ELECTRA for Bahasa.