---
language: su
tags:
  - sundanese-gpt2-base
license: mit
datasets:
  - mc4
  - cc100
  - oscar
  - wikipedia
widget:
  - text: "Nami abdi Budi, ti Indonésia"
---

## Sundanese GPT-2 Base

Sundanese GPT-2 Base is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. It was trained on four datasets: [OSCAR](https://hf.co/datasets/oscar)'s `unshuffled_deduplicated_su` subset, the Sundanese [mC4](https://hf.co/datasets/mc4) subset, the Sundanese [CC100](https://hf.co/datasets/cc100) subset, and Sundanese [Wikipedia](https://su.wikipedia.org/).

10% of the dataset is kept for evaluation purposes. The model was trained from scratch and achieved an evaluation loss of 3.61 and an evaluation perplexity of 36.97.

This model was trained using HuggingFace's Flax framework. All necessary scripts used for training could be found in the [Files and versions](https://hf.co/w11wo/sundanese-gpt2-base/tree/main) tab, as well as the [Training metrics](https://hf.co/w11wo/sundanese-gpt2-base/tensorboard) logged via Tensorboard.

## Model

| Model                 | #params | Arch. | Training/Validation data (text)       |
| --------------------- | ------- | ----- | ------------------------------------- |
| `sundanese-gpt2-base` | 124M    | GPT-2 | OSCAR, mC4, CC100, Wikipedia (758 MB) |

## Evaluation Results

The model was trained for 50 epochs and the following is the final result once the training ended.

| train loss | valid loss | valid PPL | total time |
| ---------- | ---------- | --------- | ---------- |
| 2.436      | 3.61       | 36.97     | 7:1:54     |

## How to Use

### As Causal Language Model

```python
from transformers import pipeline

pretrained_name = "w11wo/sundanese-gpt2-base"

nlp = pipeline(
    "text-generation",
    model=pretrained_name,
    tokenizer=pretrained_name
)

nlp("Nami abdi Budi, ti Indonésia")
```

### Feature Extraction in PyTorch

```python
from transformers import GPT2Model, GPT2TokenizerFast

pretrained_name = "w11wo/sundanese-gpt2-base"
model = GPT2Model.from_pretrained(pretrained_name)
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)

prompt = "Nami abdi Budi, ti Indonésia"
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```

## Disclaimer

Do consider the biases which came from all four datasets that may be carried over into the results of this model.

## Author

Sundanese GPT-2 Base was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/).