Add complete model card with metrics

README.md

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+
+---
+language: en
+library_name: transformers
+tags:
+- multilabel-classification
+- deberta-v3
+- opp115
+metrics:
+- macro_f1
+- micro_f1
+- weighted_f1
+- macro_precision
+- macro_recall
+---
+
+# DeBERTaV3 Base — OPP115 Multilabel (v2)
+
+Fine-tuned DeBERTaV3 model for multi-label classification on the OPP115 dataset.
+
+## 📊 Evaluation Metrics
+
+| Metric | Score |
+|--------|--------|
+| **Macro F1** | 0.8092 |
+| **Micro F1** | 0.8565 |
+| **Weighted F1** | 0.8531 |
+| **Macro Precision** | 0.8657 |
+| **Macro Recall** | 0.7697 |
+
+## 🧪 Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained("Hacktrix-121/deberta-v3-base-opp115-multilabel-v2")
+tokenizer = AutoTokenizer.from_pretrained("Hacktrix-121/deberta-v3-base-opp115-multilabel-v2")
+
+text = "Your input text here"
+inputs = tokenizer(text, return_tensors="pt")
+logits = model(**inputs).logits
+probs = torch.sigmoid(logits)