Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language: sw
+tags:
+  - hate-speech
+  - swahili
+  - text-classification
+  - bert
+  - offensive-language
+  - political-hate-speech
+datasets:
+  - custom
+pipeline_tag: text-classification
+---
+
+# Swahili Hate Speech Classification Model
+
+This is a fine-tuned BERT model for **multi-class text classification** in Swahili. It predicts whether a given text is:
+
+- **Non-hate speech**
+- **Political hate speech**
+- **Offensive language**
+
+## 🧠 Model Details
+
+- **Architecture**: BERT (base)
+- **Languages**: Swahili
+- **Classes**: 3
+- **Model size**: 178M parameters
+- **Framework**: PyTorch
+- **Training data**: A custom labeled dataset of Swahili social media or online comments (non-public)
+
+## 🏷️ Labels
+
+| Label ID | Class Name              |
+|----------|--------------------------|
+| `LABEL_0` | Non-hate speech         |
+| `LABEL_1` | Political hate speech   |
+| `LABEL_2` | Offensive language      |
+
+## 🚀 Usage
+
+You can load and test the model using the `transformers` library:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="sandbox338/hatespeech")
+
+result = classifier("Hii ni ujumbe wa kawaida bila matusi.")
+print(result)  # [{'label': 'LABEL_0', 'score': 0.98}]