Instructions to use mlcourse-team2/mdeberta-asaxiy-quad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlcourse-team2/mdeberta-asaxiy-quad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mlcourse-team2/mdeberta-asaxiy-quad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mlcourse-team2/mdeberta-asaxiy-quad") model = AutoModelForQuestionAnswering.from_pretrained("mlcourse-team2/mdeberta-asaxiy-quad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9bfb530decfa847d4ed6c8a04dadbaff15c20172edb63805788b5cc26f784dd9
- Size of remote file:
- 1.11 GB
- SHA256:
- e0abcf2892af70b8ee603defa0588ef0cc312623509fa74446a57e637ac21bea
路
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