indojave-codemixed-roberta-base

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Indojave: RoBERTa-base

This is a pre-trained masked language model for code-mixed Indonesian-Javanese-English tweets data. This model is trained based on RoBERTa model utilizing Hugging Face's 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

Evaluation Results

We train the data with 3 epochs and total steps of 296K for 16 days. The following are the results obtained from the training:

How to use

Load model and tokenizer

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("fathan/indojave-codemixed-roberta-base")
model = AutoModel.from_pretrained("fathan/indojave-codemixed-roberta-base")

Masked language model

from transformers import pipeline

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

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

Training procedure

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