Instructions to use Data255FinalProj/deberta-tuned-gd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data255FinalProj/deberta-tuned-gd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Data255FinalProj/deberta-tuned-gd")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Data255FinalProj/deberta-tuned-gd") model = AutoModelForQuestionAnswering.from_pretrained("Data255FinalProj/deberta-tuned-gd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98be7a557f3e294471c0ea76c5f9bfee07e6f0b739a3ed0f4919068a981bd0fb
- Size of remote file:
- 554 MB
- SHA256:
- 912bffa1f5b47cd251a80594aa24dc95353c0e2e4d799f29987e7497c1946cfe
路
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