Text Classification
Transformers
Safetensors
English
deberta-v2
multilabel-classification
deberta-v3
opp115
text-embeddings-inference
Instructions to use Hacktrix-121/deberta-v3-base-opp115-multilabel-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hacktrix-121/deberta-v3-base-opp115-multilabel-v2 with Transformers:
# 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")# 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") - Notebooks
- Google Colab
- Kaggle
DeBERTaV3 Base β OPP115 Multilabel (v2)
Fine-tuned DeBERTaV3 model for multi-label classification on the OPP115 dataset.
π Evaluation Metrics
| Metric | Score |
|---|---|
| Macro F1 | 0.8092 |
| Micro F1 | 0.8565 |
| Weighted F1 | 0.8531 |
| Macro Precision | 0.8657 |
| Macro Recall | 0.7697 |
π§ͺ 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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