---
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)