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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: code_mixed_ijebert |
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results: [] |
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language: |
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- id |
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- jv |
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- en |
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pipeline_tag: fill-mask |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Code-mixed IJEBERT |
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## About |
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Code-mixed IJEBERT is a pre-trained maksed language model for code-mixed Indonesian-Javanese-English tweets data. |
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This model is trained based on [BERT](https://huggingface.co/bert-base-multilingual-cased) model utilizing |
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Hugging Face's [Transformers]((https://huggingface.co/transformers)) library. |
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## Pre-training Data |
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The Twitter data is collected from January 2022 until January 2023. The tweets are collected using 8698 random keyword phrases. |
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To make sure the retrieved data are code-mixed, we use keyword phrases that contain code-mixed Indonesian, Javanese, or English words. |
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The following are few examples of the keyword phrases: |
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- travelling terus |
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- proud koncoku |
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- great kalian semua |
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- chattingane ilang |
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- baru aja launching |
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We acquire 40,788,384 raw tweets. We apply first stage pre-processing tasks such as: |
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- remove duplicate tweets, |
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- remove tweets with token length less than 5, |
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- remove multiple space, |
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- convert emoticon, |
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- convert all tweets to lower case. |
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After the first stage pre-processing, we obtain 17,385,773 tweets. |
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In the second stage pre-processing, we do the following pre-processing tasks: |
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- split the tweets into sentences, |
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- remove sentences with token length less than 4, |
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- convert ‘@username’ to ‘@USER’, |
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- convert URL to HTTPURL. |
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Finally, we have 28,121,693 sentences for our pre-training task. |
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## Model |
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| Model name | #params | Arch. | Size of training data | Size of validation data | |
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|----------------------|---------|----------|----------------------------|-------------------------| |
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| `code-mixed-ijebert` | | BERT | 2.24 GB of text | 249 MB of text | |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.12.0+cu102 |
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- Datasets 2.9.0 |
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- Tokenizers 0.12.1 |
