Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/sarahlintang/IndoBERT/README.md
README.md
ADDED
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
language: id
|
3 |
+
datasets:
|
4 |
+
- oscar
|
5 |
+
---
|
6 |
+
# IndoBERT (Indonesian BERT Model)
|
7 |
+
|
8 |
+
## Model description
|
9 |
+
IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language.
|
10 |
+
|
11 |
+
This model is base-uncased version which use bert-base config.
|
12 |
+
|
13 |
+
## Intended uses & limitations
|
14 |
+
|
15 |
+
#### How to use
|
16 |
+
|
17 |
+
```python
|
18 |
+
from transformers import AutoTokenizer, AutoModel
|
19 |
+
tokenizer = AutoTokenizer.from_pretrained("sarahlintang/IndoBERT")
|
20 |
+
model = AutoModel.from_pretrained("sarahlintang/IndoBERT")
|
21 |
+
tokenizer.encode("hai aku mau makan.")
|
22 |
+
[2, 8078, 1785, 2318, 1946, 18, 4]
|
23 |
+
```
|
24 |
+
|
25 |
+
|
26 |
+
## Training data
|
27 |
+
|
28 |
+
This model was pre-trained on 16 GB of raw text ~2 B words from Oscar Corpus (https://oscar-corpus.com/).
|
29 |
+
|
30 |
+
This model is equal to bert-base model which has 32,000 vocabulary size.
|
31 |
+
|
32 |
+
## Training procedure
|
33 |
+
|
34 |
+
The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
|
35 |
+
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
|
36 |
+
|
37 |
+
## Eval results
|
38 |
+
|
39 |
+
We evaluate this model on three Indonesian NLP downstream task:
|
40 |
+
- some extractive summarization model
|
41 |
+
- sentiment analysis
|
42 |
+
- Part-of-Speech Tagger
|
43 |
+
it was proven that this model outperforms multilingual BERT for all downstream tasks.
|