I built three Filipino sentiment models and ran them against the public Filipino sentiment checkpoints I could load. The run covers 13 models, 6 scoring sets, and 1 contamination probe, and it produced 91 scored rows. Six sets produce leaderboard scores. The seventh is the training data of one public model, used as an overfitting check.
The run has two tracks because the models do not share a label space. The three-class track scores macro F1 over negative, neutral, and positive on every row. Nine models compete there. The neutral-free track removes neutral rows and scores negative vs positive. Two binary models compete there. The two tracks are not comparable, and no model has both scores, so there is no single leaderboard. Every number below says which track it comes from.
On the three-class track, macro F1 gives each class equal weight, so a model cannot win by guessing one label. My FiliSenti leads the six-set mean at 0.7572. HiliSenti follows at 0.7024 and TagaSenti at 0.6873. The best model outside the family, immortalizing-legacies, reaches 0.6206. My third model still clears the best outsider by 0.0667, but no single checkpoint wins everywhere. My three models win four of the six columns between them. immortalizing-legacies wins the other two.
On the neutral-free track, The-Thesis-Gods/lazada-xlm-roberta-sentiment averages 0.8364 and scaredmeow/xlnet-tagalog-sentiment-shopee scores 0.9480 on Shopee binary, the highest single test score in the run. I cannot tell you whether Thesis beats FiliSenti. The run never scored both models on the same task, so that comparison is missing by construction.
On the human-annotated Taglish set, TagaSenti leads the three-class track at 0.6338 and finds 0.5895 of the neutral rows, the best neutral recall on that set. The set has only 190 neutral rows, so that number has wide error bars. On the Lazada set, FiliSenti leads the three-class track at 0.6344.
The comparison I want to highlight is DOST-ASTI's RoBERTa model. DOST-ASTI is a publicly funded research institute with more than 8 million training instances, a V100-32GB, the COARE facility, and a published paper. It trails all three of my models on all six scoring sets. On the human-annotated Taglish set, my TagaSenti scores 0.6338 against 0.4816. On Lazada, FiliSenti scores 0.6344 against 0.5165. The gap on my TagaSenti training set is bigger at 0.8475 against 0.4926, but DOST trained on a different corpus, so that one is out-of-domain for them. The honest claim is "DOST trails my three models on every set in this run, including the two human or externally sourced sets none of us list as training data." Not "DOST is a worse model in general."
The run also includes a contamination probe. The model that trained on it scores 0.9609 on its own rows and 0.6922 on held-out Shopee binary. That 0.2687 gap is why the tables mark home turf.
The caveats matter. FiliSenti's TagaSenti score is 0.9327, but it is trained on the full 35,686-row TagaSenti CSV and re-split 80/10/10. The benchmark uses a different split of the same CSV, so a large share of those test rows were likely in its training pool. Drop TagaSenti and the FiliSenti lead over HiliSenti falls from 0.0548 to 0.0083. The top three stay mine, but the "FiliSenti is clearly best" claim does not survive. The same home-turf warning covers all three of my models on TagaSenti and Hiligaynon, because both sets come from my own training corpora. The cleanest columns for the family are Taglish, Lazada, MTEB Shopee, and Shopee binary.
The run also has no confidence intervals, the sets are capped between 1,000 and 2,000 rows, four models run on probe-inferred label orders, and raphgonda/FilipinoShopping still failed to load despite a documented fix attempt. Gaps under roughly two points should be read as noise.
The full CSV, chart, and notebook are here:
https://github.com/jjjardev/filisenti-benchmark
The models are on Hugging Face:
https://huggingface.co/jjjardev/filisenti
https://huggingface.co/jjjardev/hilisenti-v1-model
https://huggingface.co/jjjardev/tagasenti_model
If you spot a flaw in the setup or a better baseline I should add, tell me. The repo has a Corrections section and the CSV is the source of truth.
TL;DR: My three self-built Filipino sentiment models took the top three three-class means (0.7572/0.7024/0.6873) among 13 models on 6 scoring sets, and each beats DOST-ASTI's 8M-instance baseline on all six. Two binary models run in a separate neutral-free track, so there is no single leaderboard. One honest asterisk: FiliSenti trained on my own TagaSenti CSV, so that column is in-domain for it.