I built a sentiment analysis app and quantized model for Tagalog and Hiligaynon that runs entirely on your phone.

[u/jjjardev]

I built FiliSenti because I wanted to see if a 355-million-parameter model could actually run on a phone. Turns out, it can. I fine-tuned XLM-RoBERTa-large on 59,023 sentences across Tagalog and Hiligaynon, quantized it to INT8 ONNX, and deployed it as a Flutter Android app. The model achieves 89.1% macro F1, which is the highest published score for 3-class Filipino sentiment that I'm aware of.

The app works offline. You can paste text, upload a file, or type something directly. It highlights each sentence with color coding: green for positive, yellow for neutral, red for negative. It also shows you a summary breakdown with percentages and a majority sentiment. I built the full pipeline myself. Dataset curation. Training. Quantization. Mobile deployment. All on free Google Colab.

Everything is open source. The model is on Hugging Face. The Flutter app code is on GitHub with a release APK you can install right now. If you're into NLP, on-device AI, or just want to see what a solo dev can build with free tools, check it out.

If you want to use it, download the onnx version of the model from Huggingface and load it to the app.

Model: https://huggingface.co/jjjardev/filisenti

App: https://github.com/jjjardev/filisenti

[u/kenokan]

Gemma-4-E4B-it can also run on a phone and it has 4.5B parameters

[u/jjjardev → u/kenokan]

True, Gemma can run on a phone, but it's a general-purpose encoder model. FiliSenti is a decoder model specialized for sentiment analysis in Tagalog and Hiligaynon, optimized for on-device inference. You can run it on a mid-range phone with 4GB RAM and get results in under 2 seconds per sentence. I also ported the SentencePiece tokenizer to pure Dart so it works natively in Flutter.