Instructions to use ICML2022/Tranception with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICML2022/Tranception with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ICML2022/Tranception")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("ICML2022/Tranception", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9e1d0ac5ddbd02bcf8ad35c972f83802f9eac705a888183cee94c1a27ce0ed7
- Size of remote file:
- 2.87 GB
- SHA256:
- e3ee11f9fa7cca8f7859d63769a40ef788f3f32967b5a9ec32bbb2c1f35b0bea
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