Instructions to use datasciencemmw/old-beta2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasciencemmw/old-beta2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="datasciencemmw/old-beta2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datasciencemmw/old-beta2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 684b625eb624214ed31fac6797d903320c69eaf457e25bdebacab38e85d359b6
- Size of remote file:
- 1.14 kB
- SHA256:
- 6af63cdeb4aaef2c06583f97f97c0f115279be043e39bc8a97a1a673094c75f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.