---
license: apache-2.0
datasets:
- gplsi/SocialTOX
language:
- es
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- BSC-LT/roberta-base-bne
pipeline_tag: text-classification
---

# 🧠 Toxicity_model_RoBERTa-base-bne– Spanish Toxicity Classifier Binary (Fine-tuned)

## 📌 Model Description

This model is a fine-tuned version** of `RoBERTa-base-bne`, specifically trained to classify the toxicity level of **Spanish-language user comments on news articles**. It distinguishes between two categories:

- **Non-toxic**
- **Toxic**

---

## 📂 Training Data

The model was fine-tuned on the **[SocialTOX dataset](https://huggingface.co/datasets/gplsi/SocialTOX)**, a collection of Spanish-language comments annotated for varying levels of toxicity. These comments come from news platforms and represent real-world scenarios of online discourse. In this case, a Binary classifier was developed, where the classes \textit{Slightly toxic} and \textit{Toxic} were merged into a single \textit{Toxic} category.  

---

## Training hyperparameters
- epochs: 10
- learning_rate: 2.45e-6
- beta1: 0.9
- beta2: 0.95
- Adam_epsilon: 1.00e-8
- weight_decay: 0
- batch_size: 16
- max_seq_length: 512