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---
language: su
tags:
- sundanese-gpt2-base-emotion-classifier
license: mit
widget:
- text: "Wah, éta gélo, keren pisan!"
---
## Sundanese GPT-2 Base Emotion Classifier
Sundanese GPT-2 Base Emotion Classifier is an emotion-text-classification model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. The model was originally the pre-trained [Sundanese GPT-2 Base](https://hf.co/w11wo/sundanese-gpt2-base) model, which is then fine-tuned on the [Sundanese Twitter dataset](https://github.com/virgantara/sundanese-twitter-dataset), consisting of Sundanese tweets.
10% of the dataset is kept for evaluation purposes. After training, the model achieved an evaluation accuracy of 94.84% and F1-macro of 94.75%.
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.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ---------------------------------------- | ------- | ---------- | ------------------------------- |
| `sundanese-gpt2-base-emotion-classifier` | 124M | GPT-2 Base | Sundanese Twitter dataset |
## Evaluation Results
The model was trained for 10 epochs and the best model was loaded at the end.
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
| 1 | 0.819200 | 0.331463 | 0.880952 | 0.878694 | 0.883126 | 0.879304 |
| 2 | 0.140300 | 0.309764 | 0.900794 | 0.899025 | 0.906819 | 0.898632 |
| 3 | 0.018600 | 0.324491 | 0.948413 | 0.947525 | 0.948037 | 0.948153 |
| 4 | 0.004500 | 0.335100 | 0.932540 | 0.931648 | 0.934629 | 0.931617 |
| 5 | 0.000200 | 0.392145 | 0.932540 | 0.932281 | 0.935075 | 0.932527 |
| 6 | 0.000000 | 0.371689 | 0.932540 | 0.931760 | 0.934925 | 0.931840 |
| 7 | 0.000000 | 0.368086 | 0.944444 | 0.943652 | 0.945875 | 0.943843 |
| 8 | 0.000000 | 0.367550 | 0.944444 | 0.943652 | 0.945875 | 0.943843 |
| 9 | 0.000000 | 0.368033 | 0.944444 | 0.943652 | 0.945875 | 0.943843 |
| 10 | 0.000000 | 0.368391 | 0.944444 | 0.943652 | 0.945875 | 0.943843 |
## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "sundanese-gpt2-base-emotion-classifier"
nlp = pipeline(
"sentiment-analysis",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Wah, éta gélo, keren pisan!")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and the Sundanese Twitter dataset that may be carried over into the results of this model.
## Author
Sundanese GPT-2 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.
## Citation Information
```bib
@article{rs-907893,
author = {Wongso, Wilson
and Lucky, Henry
and Suhartono, Derwin},
journal = {Journal of Big Data},
year = {2022},
month = {Feb},
day = {26},
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.},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-907893/v1},
url = {https://doi.org/10.21203/rs.3.rs-907893/v1}
}
``` |