Instructions to use datasciencemmw/old-beta1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasciencemmw/old-beta1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="datasciencemmw/old-beta1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datasciencemmw/old-beta1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08b6467b4451c91c5e5546aa8657fb66c3c25196545ded9defd42fe1529f395d
- Size of remote file:
- 712 kB
- SHA256:
- 41b9d12a4c559e4098f30173bc0f76d092c43d7b1f873e7027da45a256f30f87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.