w11wo/javanese-gpt2-small-imdb

🤗 Hugging Face 来源text-generationmit137M 参数510 MBsafetensors✓ 4 个校验和今天更新
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Javanese GPT-2 Small IMDB

Javanese GPT-2 Small IMDB is a causal language model based on the GPT-2 model. It was trained on Javanese IMDB movie reviews.

The model was originally the pretrained Javanese GPT-2 Small model and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 60.54 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial notebook written by Sylvain Gugger.

Hugging Face's Trainer class from the Transformers library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.

Model

Model #params Arch. Training/Validation data (text)
javanese-gpt2-small-imdb 124M GPT-2 Small Javanese IMDB (47.5 MB of text)

Evaluation Results

The model was trained for 5 epochs and the following is the final result once the training ended.

train loss valid loss perplexity total time
4.135 4.103 60.54 6:22:40

How to Use (PyTorch)

As Causal Language Model

from transformers import pipeline

pretrained_name = "w11wo/javanese-gpt2-small-imdb"

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

nlp("Jenengku Budi, saka Indonesia")

Feature Extraction in PyTorch

from transformers import GPT2LMHeadModel, GPT2TokenizerFast

pretrained_name = "w11wo/javanese-gpt2-small-imdb"
model = GPT2LMHeadModel.from_pretrained(pretrained_name)
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)

prompt = "Indonesia minangka negara gedhe."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)

Disclaimer

Do consider the biases which came from the IMDB review that may be carried over into the results of this model.

Author

Javanese GPT-2 Small was trained and evaluated by Wilson Wongso. All computation and development are done on Google Colaboratory using their free GPU access.

Citation

If you use any of our models in your research, please cite:

@inproceedings{wongso2021causal,
    title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
    author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
    booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
    pages={1--7},
    year={2021},
    organization={IEEE}
}