README.md

---
tags:
- generated_from_trainer
model-index:
- name: code_mixed_ijebert
  results: []
language:
- id
- jv
- en
pipeline_tag: fill-mask
widget:
- text: biasane nek arep [MASK] file bs pake software ini
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# IndoJavE: BERT-base

## About
This is a pre-trained masked language model for code-mixed Indonesian-Javanese-English tweets data.
This model is trained based on [BERT](https://arxiv.org/abs/1810.04805) model utilizing 
Hugging Face's [Transformers]((https://huggingface.co/transformers)) library.

## Pre-training Data
The Twitter data is collected from January 2022 until January 2023. The tweets are collected using 8698 random keyword phrases.
To make sure the retrieved data are code-mixed, we use keyword phrases that contain code-mixed Indonesian, Javanese, or English words. 
The following are few examples of the keyword phrases:
- travelling terus
- proud koncoku
- great kalian semua
- chattingane ilang
- baru aja launching

We acquire 40,788,384 raw tweets. We apply first stage pre-processing tasks such as:
- remove duplicate tweets,
- remove tweets with token length less than 5,
- remove multiple space,
- convert emoticon,
- convert all tweets to lower case.

After the first stage pre-processing, we obtain 17,385,773 tweets. 
In the second stage pre-processing, we do the following pre-processing tasks:
- split the tweets into sentences,
- remove sentences with token length less than 4,
- convert ‘@username’ to ‘@USER’,
- convert URL to HTTPURL.

Finally, we have 28,121,693 sentences for the training process. 
This pretraining data will not be opened to public due to Twitter policy.

## Model
| Model name                        | Architecture    | Size of training data      | Size of validation data |
|-----------------------------------|-----------------|----------------------------|-------------------------|
| `indojave-codemixed-bert-base`    | BERT            | 2.24 GB of text            | 249 MB of text          |  

## Evaluation Results
We train the data with 3 epochs and total steps of 296K for 12 days.
The following are the results obtained from the training:

| train loss | eval loss  | eval perplexity | 
|------------|------------|-----------------|
|   3.5057   |   3.0559   |  21.2398        |

## How to use
### Load model and tokenizer
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("fathan/indojave-codemixed-bert-base")
model = AutoModel.from_pretrained("fathan/indojave-codemixed-bert-base")

```
### Masked language model
```python
from transformers import pipeline

pretrained_model = "fathan/indojave-codemixed-bert-base"

fill_mask = pipeline(
    "fill-mask",
    model=pretrained_model,
    tokenizer=pretrained_model
)
```



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.12.1