File size: 2,426 Bytes
1ac4a63 7d983c7 1ac4a63 7d983c7 396e92c 7d983c7 396e92c 7d983c7 2fd04d1 7d983c7 1ac4a63 7d983c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
tags:
- generated_from_trainer
model-index:
- name: code_mixed_ijebert
results: []
language:
- id
- jv
- en
pipeline_tag: fill-mask
---
<!-- 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. -->
# 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 |