Instructions to use GleghornLab/AT_RED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/AT_RED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GleghornLab/AT_RED")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GleghornLab/AT_RED") model = AutoModelForMaskedLM.from_pretrained("GleghornLab/AT_RED") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa052d627aa0f0c1ed7689188b9a28c223456a41b9636218cb9a67d572c53071
- Size of remote file:
- 69.3 MB
- SHA256:
- e2d60f16685ea1bc8b6e30367aa6cc0a82fb4760f82159a5034682e4903c4ef0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.