This is a fine-tuned BERT model trained using as a base model BETO, which is a BERT base-sized trained specifically for Spanish. The dataset for training this model is a gold standard for protest events for toxicity and incivility in Spanish.

The dataset comprises ~5M data points from three Latin American protest events: (a) protests against the coronavirus and judicial reform measures in Argentina during August 2020; (b) protests against education budget cuts in Brazil in May 2019; and (c) the social outburst in Chile stemming from protests against the underground fare hike in October 2019. We are focusing on interactions in Spanish to elaborate a gold standard for digital interactions in this language, therefore, we prioritise Argentinian and Chilean data.

Labels

NONTOXIC
TOXIC

Example Usage

python

## Pipeline as a high-level helper
from transformers import pipeline
toxic_classifier = pipeline("text-classification", model="bgonzalezbustamante/bert-spanish-toxicity")

## Non-toxic example
non_toxic = toxic_classifier("Que tengas un excelente día :)")

## Toxic example
toxic = toxic_classifier("Eres un maldito infeliz")

## Print examples
print(non_toxic)
print(toxic)

Output:

[{'label': 'NONTOXIC', 'score': 0.8723140358924866}]
[{'label': 'TOXIC', 'score': 0.9470418691635132}]

Validation Metrics

Accuracy: 0.835
Precision: 0.816
Recall: 0.886
F1-score: 0.849

Environmental Impact

9.15g CO₂ eq emissions

How to Cite

González-Bustamante, B. (2024). bert-spanish-toxicity (Revision e1d937c). Hugging Face. https://doi.org/10.57967/hf/6866.