Instructions to use patrickvonplaten/data2vec-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickvonplaten/data2vec-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="patrickvonplaten/data2vec-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/data2vec-base") model = AutoModel.from_pretrained("patrickvonplaten/data2vec-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc1c19537a2a4f75760d6e3e377648c787713d75c3a8fe3152fdbf2eafe202c3
- Size of remote file:
- 373 MB
- SHA256:
- 999df36ad12943c3848d8e2323266cf875d906ec5119d3f398bbff45f45a11d2
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