Instructions to use dweb/deberta-base-CoLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dweb/deberta-base-CoLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dweb/deberta-base-CoLA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dweb/deberta-base-CoLA") model = AutoModelForSequenceClassification.from_pretrained("dweb/deberta-base-CoLA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32d8c0e1b2ae7943f7ac4ca9fd91c9686bd4cc60dfcfd9e6e2f77fd4d9c80a57
- Size of remote file:
- 557 MB
- SHA256:
- 51315330027d48f3b6eb008abce5eb40c24dd9cadb6498d4345d5d3ce9d037dc
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