Update README.md

README.md

@@ -6,7 +6,19 @@ library_name: transformers
 pipeline_tag: text-classification
 ---
 
-# 📊 Model Performance
+# Akbank Hackathon: DisasterTech - Our Contribution
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62bdd8065f304e8ea762287f/raHCZDUuHPckwwrKDRz-A.png)
+
+## 🎯 Introduction
+
+**Akbank LAB** and **imece** teamed up to launch the **Akbank Hackathon: DisasterTech**, a beacon for innovators passionate about harnessing technology to revolutionize disaster management and relief. The event, which began on the 14th of October online and culminated at the Sabancı Center on the 22nd, saw a plethora of teams brainstorming and developing visionary solutions aimed at disaster alerts, preparedness, and post-calamity assistance.
+
+In response to this call-to-action, our team stepped up, and this repository stands testament to the innovation we brought to the table during this monumental event.
+
+For an in-depth look at the hackathon, feel free to visit [Akbank Hackathon: DisasterTech](https://www.akbanklab.com/tr/akbank-hackathon-disastertech#section-4).
+
+## 📊 Model Performance & Usage
 
 In this document, you can find detailed insights regarding our classification model's performance.
 
@@ -31,3 +43,47 @@ In this document, you can find detailed insights regarding our classification mo
 | Yemek İhtiyacı              | 0.90      | 0.88   | 0.89     | 226     |
 | **Total/Avg**      | **0.89**  | **0.89**| **0.89** | **1485**|
 
+
+## 🖥️ How to use the model
+
+Here is a Python example demonstrating how to use the model for predicting class of a given text:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+from torch.nn.functional import softmax
+import torch
+
+model_name = "tarikkaankoc7/zeltech-akbank-hackathon"
+model = BertForSequenceClassification.from_pretrained(model_name)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model.eval()
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probs = softmax(outputs.logits, dim=-1)
+    predicted_class_id = torch.argmax(probs, dim=-1).item()
+    predicted_class_name = model.config.id2label[predicted_class_id]
+
+    return predicted_class_name
+
+text = "Hatay/Antakya odabaşı atatürk bulvarı ahmet gürses apartmanı arkadasım ilayda kürkçü enkaz altında paylaşır mısınız"
+predicted_class_name = predict(text)
+print(f"Predicted Class: {predicted_class_name}")
+```
+
+## Expected Output:
+
+```bash
+Predicted Class: Debris Removal Notification
+```
+
+## 🖋️ Authors
+
+- **Şeyma SARIGIL** - [📧 Email](mailto:seymasargil@gmail.com)
+- **Tarık Kaan KOÇ** - [📧 Email](mailto:tarikkaan1koc@gmail.com)
+- **Alaaddin Erdinç DAL** - [📧 Email](mailto:aerdincdal@icloud.com)
+- **Anıl YAĞIZ** - [📧 Email](mailto:anill.yagiz@gmail.com)