Instructions to use sofom/Style-Embedding-turingbench-aa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofom/Style-Embedding-turingbench-aa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sofom/Style-Embedding-turingbench-aa")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sofom/Style-Embedding-turingbench-aa") model = AutoModel.from_pretrained("sofom/Style-Embedding-turingbench-aa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ce05c5b03f5eb89b76553fa3b2b3f33ecbcd75ef34ef9f2a242cc008f8ab9fb
- Size of remote file:
- 249 MB
- SHA256:
- 1474944a9e83a979837c2828fcea31afe983131a67123e6c534d6221a8878bb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.