Update README.md

README.md

@@ -1,3 +1,95 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- gplsi/SocialTOX
+language:
+- es
+metrics:
+- f1
+- precision
+- recall
+- confusion_matrix
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+library_name: transformers
+---
+
+# 🧠 Toxicity_model_Llama_3.1_8B_Binary – Spanish Toxicity Classifier (Instruction-Tuned)
+
+## 📌 Model Description
+
+This model is an **instruction-tuned version** of `LLaMA 3.1 8B Instruct`, specifically trained to classify the toxicity level of **Spanish-language user comments on news articles**. It distinguishes between tow categories:
+
+- **Non-toxic**
+- **Toxic**
+
+The model follows instruction-based prompts and returns a single classification label in response.
+
+---
+
+## 📂 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 develop, where the classes \textit{Slightly toxic} and \textit{Toxic} were merged into a single \textit{Toxic} category.  
+
+---
+
+## 💬 Instruction Format
+
+The model expects input in a structured conversational format like the one below:
+
+```json
+[
+  {
+    "role": "system",
+    "content": "You are an expert in detecting toxicity in comments, and your goal is to classify comments based on their level of toxicity. The comments were made on news articles. The toxicity categories are:
+    Toxic: Comments that contain derogatory or pejorative language, inappropriate jokes, fearmongering, denial of facts, threats, personal attacks, insults, degradation, or racist or sexist language. Only classify a comment as “toxic” if it contains clear attack language, direct insults, or demeaning references.
+    Non-toxic: Neutral or critical comments that do not include Toxic or Slightly toxic elements. Note that negative or critical comments (those with a serious or discontented tone) are Not toxic or Slightly toxic unless they meet the criteria of the categories above.
+    Please write the corresponding category immediately after the word 'answer.' In case of doubt between two labels, choose the one with the lowest or no toxicity level."
+  },
+  {
+    "role": "user",
+    "content": "Text: "Narco-Bolivarian Communism"
+  },
+  {
+    "role": "assistant",
+    "content": "Toxic"
+  }
+]
+```
+
+## Training hyperparameters
+- epochs: 3
+- learning_rate: 1e-5
+- beta1: 0.9
+- beta2: 0.95
+- weight_decay: 0.1
+- batch_size global: 4
+- micro_batch_size: 1
+- lr_warmup_steps: 100
+- max_seq_length: 512
+
+## 📊 Evaluation
+
+The model was evaluated on a held-out **test set of 968 manually annotated comments**. Below are the confusion matrix and classification metrics:
+
+### 🧮 Confusion Matrix (Binary Classification)
+
+|                          | Non-toxic | Toxic |
+|--------------------------|----------------------|------------------|
+| **Non-toxic**    | 534                  | 53               |
+| **Toxic**        | 136                  | 245              |
+
+---
+
+### 📈 Classification Report
+
+| Class        | Precision | Recall  | F1-score | Support |
+|--------------|-----------|---------|----------|---------|
+| **Non-toxic**| 0.8222    | 0.6430  | 0.7216   | 587     |
+| **Toxic**    | 0.7970    | 0.9097  | 0.8496   | 381     |
+|              |           |         |          |         |
+| **Accuracy** |           |         | **0.8048** | **968** |
+| **Macro avg**| 0.8096    | 0.7764  | 0.7856   | 968     |
+| **Weighted avg** | 0.8069 | 0.8048  | 0.7993   | 968     |
+
+**Macro F1-score**: `0.7856`