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:
- 71834949c2faac3db50eae7c5a2abebb2b671f88f8e6d82d5f6d0f6a6707ac4a
- Size of remote file:
- 16.3 MB
- SHA256:
- edece5654237062697029364a127e7e445a194980cff769210933906a213f71d
路
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