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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9223ee2cebd67830a4e7a05c46e9c826a9430df5d1e6a1df7da61b62a43aa2e
- Size of remote file:
- 738 MB
- SHA256:
- c4147f0a5d219f7be1bd2b8d11b9963919b59f173d84f208d2602fa74f91a253
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