readme: ---
  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}
  }
  ```