Instructions to use taskydata/deberta-v3-base_10xp3_10xc4_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taskydata/deberta-v3-base_10xp3_10xc4_128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_10xp3_10xc4_128")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_128") model = AutoModelForSequenceClassification.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e4b489095d9950a0fc56f954a042f91937dab3c794ba56fbb4e60253fda0cfa
- Size of remote file:
- 738 MB
- SHA256:
- 7a8ccda6fc02ee0d624bf0f4e1d9b4d0fb478eed89123e7f69090a0b84d1689f
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