|
--- |
|
language: "id" |
|
license: "mit" |
|
datasets: |
|
- wikipedia |
|
- id_newspapers_2018 |
|
widget: |
|
- text: "ayahku sedang bekerja di sawah untuk [MASK] padi." |
|
--- |
|
|
|
# Indonesian DistilBERT base model (uncased) |
|
|
|
## Model description |
|
This model is a distilled version of the [Indonesian BERT base model](https://huggingface.co/cahya/bert-base-indonesian-1.5G). |
|
This model is uncased. |
|
|
|
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about |
|
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers) |
|
|
|
## Intended uses & limitations |
|
|
|
### How to use |
|
You can use this model directly with a pipeline for masked language modeling: |
|
```python |
|
>>> from transformers import pipeline |
|
>>> unmasker = pipeline('fill-mask', model='cahya/distilbert-base-indonesian') |
|
>>> unmasker("Ayahku sedang bekerja di sawah untuk [MASK] padi") |
|
|
|
[ |
|
{ |
|
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk menanam padi [SEP]", |
|
"score": 0.6853187084197998, |
|
"token": 12712, |
|
"token_str": "menanam" |
|
}, |
|
{ |
|
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk bertani padi [SEP]", |
|
"score": 0.03739545866847038, |
|
"token": 15484, |
|
"token_str": "bertani" |
|
}, |
|
{ |
|
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk memetik padi [SEP]", |
|
"score": 0.02742469497025013, |
|
"token": 30338, |
|
"token_str": "memetik" |
|
}, |
|
{ |
|
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk penggilingan padi [SEP]", |
|
"score": 0.02214187942445278, |
|
"token": 28252, |
|
"token_str": "penggilingan" |
|
}, |
|
{ |
|
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk tanam padi [SEP]", |
|
"score": 0.0185895636677742, |
|
"token": 11308, |
|
"token_str": "tanam" |
|
} |
|
] |
|
|
|
``` |
|
Here is how to use this model to get the features of a given text in PyTorch: |
|
```python |
|
from transformers import DistilBertTokenizer, DistilBertModel |
|
|
|
model_name='cahya/distilbert-base-indonesian' |
|
tokenizer = DistilBertTokenizer.from_pretrained(model_name) |
|
model = DistilBertModel.from_pretrained(model_name) |
|
text = "Silakan diganti dengan text apa saja." |
|
encoded_input = tokenizer(text, return_tensors='pt') |
|
output = model(**encoded_input) |
|
``` |
|
and in Tensorflow: |
|
```python |
|
from transformers import DistilBertTokenizer, TFDistilBertModel |
|
|
|
model_name='cahya/distilbert-base-indonesian' |
|
tokenizer = DistilBertTokenizer.from_pretrained(model_name) |
|
model = TFDistilBertModel.from_pretrained(model_name) |
|
text = "Silakan diganti dengan text apa saja." |
|
encoded_input = tokenizer(text, return_tensors='tf') |
|
output = model(encoded_input) |
|
``` |
|
|
|
## Training data |
|
|
|
This model was distiled with 522MB of indonesian Wikipedia and 1GB of |
|
[indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018). |
|
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are |
|
then of the form: |
|
|
|
```[CLS] Sentence A [SEP] Sentence B [SEP]``` |
|
|
