u/jjjardev

A month ago, I released HiliSenti v1, the first public sentiment analysis dataset for Hiligaynon, 23,337 real‑world sentences labeled as negative, neutral, or positive. Today, I'm releasing the fine‑tuned model itself: an XLM‑RoBERTa‑large (355M parameters) that achieves 93.5% test accuracy and 93.4% macro F1, with per‑class F1 scores of 0.95 (Negative), 0.91 (Neutral), and 0.94 (Positive). The model handles code‑switching (Tagalog/English) and performs well.

Everything was built on zero budget, free Google Colab T4 GPU, free 15GB Google Drive. The model weights are now publicly available on Hugging Face under CC BY‑NC‑SA 4.0 (same as the dataset), and the training code is open‑source on GitHub under MIT. I also secured a DOI for the model (10.57967/hf/9302) so it's permanently citable even without an arXiv paper yet.

If you're into NLP, low‑resource languages, or just want to see a Filipino regional language get some ML love, go check it out. The model is ready for inference via transformers pipeline, just load it and run. I'd love to hear your feedback, especially if you're working on similar projects for other Philippine languages.

Links:

• Model: https://huggingface.co/jjjardev/hilisenti-v1-model
• Dataset: https://huggingface.co/datasets/jjjardev/hilisenti-v1
• Code: https://github.com/jjjardev/hilisenti

You can try the model interactively using the Colab notebook available in this repository:

hilisenti_test.ipynb

Simply open the notebook in Google Colab and run all cells to test the model on your own Hiligaynon sentences.

I want to showcase HiliSenti v1, the first public sentiment analysis dataset for Hiligaynon. It’s a multi‑domain collection of 23,337 real‑world Hiligaynon sentences, many with natural Tagalog/English code‑switching labeled as negative, neutral, or positive. I trained an XLM‑RoBERTa‑large model on it and got 93.5% accuracy (macro F1 of 93.4%), which is far above the 80% target I originally set. This means the model can reliably understand sentiment in actual Hiligaynon text, not just in English or Tagalog, which is a first for our language.

Everything was done on a free Google Colab GPU and the free 15GB Google Drive, without any paid API or cloud credits, just a lot of manual dataset curation and some creative checkpoint pruning when Drive storage kept filling up. The code is open‑source on GitHub, the dataset is on Hugging Face, and I’m working on a paper (aiming to submit to ACL). If you’re into NLP, low‑resource languages, or just want to see a Filipino regional language get some ML love, go take a look.

Dataset: https://huggingface.co/datasets/jjjardev/hilisenti-v1

Code: https://github.com/jjjardev/hilisenti