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--- |
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language: su |
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tags: |
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- sundanese-bert-base-emotion-classifier |
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license: mit |
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widget: |
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- text: "Punten ini akurat ga ya sieun ihh daerah aku masuk zona merah" |
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--- |
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## Sundanese BERT Base Emotion Classifier |
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Sundanese BERT Base Emotion Classifier is an emotion-text-classification model based on the [BERT](https://arxiv.org/abs/1810.04805) model. The model was originally the pre-trained [Sundanese BERT Base Uncased](https://hf.co/luche/bert-base-sundanese-uncased) model trained by [`@luche`](https://hf.co/luche), which is then fine-tuned on the [Sundanese Twitter dataset](https://github.com/virgantara/sundanese-twitter-dataset), consisting of Sundanese tweets. |
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10% of the dataset is kept for evaluation purposes. After training, the model achieved an evaluation accuracy of 96.82% and F1-macro of 96.75%. |
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Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co./transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless. |
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## Model |
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| Model | #params | Arch. | Training/Validation data (text) | |
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| ---------------------------------------- | ------- | --------- | ------------------------------- | |
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| `sundanese-bert-base-emotion-classifier` | 110M | BERT Base | Sundanese Twitter dataset | |
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## Evaluation Results |
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The model was trained for 10 epochs and the best model was loaded at the end. |
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| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |
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| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- | |
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| 1 | 0.759800 | 0.263913 | 0.924603 | 0.925042 | 0.928426 | 0.926130 | |
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| 2 | 0.213100 | 0.456022 | 0.908730 | 0.906732 | 0.924141 | 0.907846 | |
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| 3 | 0.091900 | 0.204323 | 0.956349 | 0.955896 | 0.956226 | 0.956248 | |
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| 4 | 0.043800 | 0.219143 | 0.956349 | 0.955705 | 0.955848 | 0.956392 | |
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| 5 | 0.013700 | 0.247289 | 0.960317 | 0.959734 | 0.959477 | 0.960782 | |
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| 6 | 0.004800 | 0.286636 | 0.956349 | 0.955540 | 0.956519 | 0.956615 | |
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| 7 | 0.000200 | 0.243408 | 0.960317 | 0.959085 | 0.959145 | 0.959310 | |
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| 8 | 0.001500 | 0.232138 | 0.960317 | 0.959451 | 0.959427 | 0.959997 | |
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| 9 | 0.000100 | 0.215523 | 0.968254 | 0.967556 | 0.967192 | 0.968330 | |
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| 10 | 0.000100 | 0.216533 | 0.968254 | 0.967556 | 0.967192 | 0.968330 | |
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## How to Use |
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### As Text Classifier |
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```python |
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from transformers import pipeline |
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pretrained_name = "sundanese-bert-base-emotion-classifier" |
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nlp = pipeline( |
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"sentiment-analysis", |
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model=pretrained_name, |
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tokenizer=pretrained_name |
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) |
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nlp("Punten ini akurat ga ya sieun ihh daerah aku masuk zona merah") |
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``` |
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## Disclaimer |
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Do consider the biases which come from both the pre-trained BERT model and the Sundanese Twitter dataset that may be carried over into the results of this model. |
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## Author |
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Sundanese BERT Base Emotion Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. |
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## Citation Information |
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```bib |
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@article{rs-907893, |
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author = {Wongso, Wilson |
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and Lucky, Henry |
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and Suhartono, Derwin}, |
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journal = {Journal of Big Data}, |
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year = {2022}, |
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month = {Feb}, |
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day = {26}, |
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abstract = {The Sundanese language has over 32 million speakers worldwide, but the language has reaped little to no benefits from the recent advances in natural language understanding. Like other low-resource languages, the only alternative is to fine-tune existing multilingual models. In this paper, we pre-trained three monolingual Transformer-based language models on Sundanese data. When evaluated on a downstream text classification task, we found that most of our monolingual models outperformed larger multilingual models despite the smaller overall pre-training data. In the subsequent analyses, our models benefited strongly from the Sundanese pre-training corpus size and do not exhibit socially biased behavior. We released our models for other researchers and practitioners to use.}, |
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issn = {2693-5015}, |
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doi = {10.21203/rs.3.rs-907893/v1}, |
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url = {https://doi.org/10.21203/rs.3.rs-907893/v1} |
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} |
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``` |
