Text Classification
Transformers
Safetensors
English
deberta-v2
multilabel-classification
deberta-v3
opp115
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hacktrix-121/deberta-v3-base-opp115-multilabel-v2")
model = AutoModelForSequenceClassification.from_pretrained("Hacktrix-121/deberta-v3-base-opp115-multilabel-v2", device_map="auto")Quick Links
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
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)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hacktrix-121/deberta-v3-base-opp115-multilabel-v2")