Instructions to use christofid/dapbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christofid/dapbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="christofid/dapbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("christofid/dapbert") model = AutoModelForMaskedLM.from_pretrained("christofid/dapbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d1932cc4f6f7541f0684cdc6ccf71aa3acd3ce37231739fb1d493f98287e9dd
- Size of remote file:
- 2.74 kB
- SHA256:
- d3d6f0b08fe01cfbe6b31b45d691c0db40eb102dff42f36a237db678686d6fdd
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