Instructions to use sami1616/deberta-v3-base-custom-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sami1616/deberta-v3-base-custom-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sami1616/deberta-v3-base-custom-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sami1616/deberta-v3-base-custom-v2") model = AutoModelForSequenceClassification.from_pretrained("sami1616/deberta-v3-base-custom-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44e97bb39d4b9b8f772fbbdcfaa0f34cfb5989b2e5e1c9d07cd54e87be2c7e73
- Size of remote file:
- 738 MB
- SHA256:
- 8c5d3bc11b8317800440284890b3bae817357a000631b730674f4208e642b308
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