beto-colombiacheck

BETO — ColombiaCheck Verdict Classifier

⚠️ This is NOT a truth detector. The model predicts how ColombiaCheck would label a claim (Falso / Cuestionable / Verdadero), learned from a single organization's published fact-checks. It does not verify facts, and its minority class Verdadero scores F1 = 0.0 on the held-out test set — in practice it behaves as a Falso/Cuestionable discriminator. It is an educational / portfolio artifact; do not use it for moderation, editorial decisions, or any decision affecting people or publications.

Fine-tuned from BETO (Spanish BERT, cased) on 2,935 claims fact-checked by ColombiaCheck (2018-10-26 → 2026-07-16 snapshot). Full pipeline, EDA, and governance docs (Datasheet, Data Statement, Model Card) live in the GitHub repository.

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="polux89/beto-colombiacheck")
clf("Una persona fallecida fue jurado de votación en las elecciones de 2026")
# [{'label': 'Falso', 'score': ...}]

Input: a short claim in Spanish (the corpus averages ~10 words; max_length=64 covers 100% of it). Output: one of Falso, Cuestionable, Verdadero.

Metrics (held-out test, n = 439)

TF-IDF + Logistic Regression baseline: 0.386 test macro-F1. Confusion matrix (rows = truth Cuestionable/Falso/Verdadero): [40, 49, 0] · [61, 266, 9] · [5, 9, 0].

Training

• Data: 2,935 ColombiaCheck claims (claimReviewed from public ClaimReview markup — the neutral claim, not the verdict-revealing headline). Stratified 70/15/15 split, seed 42. Class distribution: Falso 76.5% / Cuestionable 20.3% / Verdadero 3.2%. The corpus itself is not redistributed — the claim texts belong to ColombiaCheck; the repository ships the code to rebuild it.
• Loss: cross-entropy with inverse-frequency class weights (Verdadero ≈ 10x).
• max_length=64, lr 2e-5, batch 16, ≤6 epochs, early stopping on validation macro-F1 (patience 2), seed 42. Trained on Apple M4 (MPS, fp32).

Limitations & bias

• Single-source labels: the model reproduces ColombiaCheck's editorial judgment and inherits its selection bias (what gets fact-checked).
• Structural scarcity + drift of Verdadero: only 93 examples, concentrated in the COVID era (33 in 2020 → 2 in 2026). The class is effectively unlearnable from this corpus, and the label distribution the model saw no longer exists.
• The ClaimReview-availability selection is non-random and disproportionately removes minority classes (details in the repo's Data Statement).

License & attribution

• Fine-tuned weights released under CC BY 4.0, inheriting the base model's license. BETO's authors note they cannot guarantee that all of BETO's pre-training data is compatible with commercial use — the same caveat applies here (and this model is not intended for production use anyway).
• Data source credit: ColombiaCheck (https://colombiacheck.com).
• Base model citation:

@inproceedings{CaneteCFP2020,
  title={Spanish Pre-Trained BERT Model and Evaluation Data},
  author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
  booktitle={PML4DC at ICLR 2020},
  year={2020}
}