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 model. It was trained on four datasets: OSCAR's unshuffled_deduplicated_su subset, the Sundanese mC4 subset, the Sundanese CC100 subset, and Sundanese Wikipedia.
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 tab, as well as the Training metrics 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
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
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.