---
tags:
- generated_from_trainer
model-index:
- name: code_mixed_ijebert
  results: []
language:
- id
- jv
- en
pipeline_tag: fill-mask
---

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# Code-mixed IJEBERT

## About
Code-mixed IJEBERT is a pre-trained maksed language model for code-mixed Indonesian-Javanese-English tweets data.
This model is trained based on [BERT](https://huggingface.co/bert-base-multilingual-cased) 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 our pre-training task.

## Model
| Model name           | #params | Arch.    | Size of training data      | Size of validation data |
|----------------------|---------|----------|----------------------------|-------------------------|
| `code-mixed-ijebert` |         | BERT     | 2.24 GB of text            | 249 MB of text          |  

### 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

### Training results



### Framework versions

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