Instructions to use avinasht/FIRE-Deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avinasht/FIRE-Deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avinasht/FIRE-Deberta-v3-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avinasht/FIRE-Deberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("avinasht/FIRE-Deberta-v3-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16d8329855a16d20783a55b192ca73897dffad559b04f0a497c5cdcfe435a3b4
- Size of remote file:
- 5.65 kB
- SHA256:
- f483c1c175824f712a9031341ceaeddd1a96c22cd88eee81091c4975373f13f7
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