Fill-Mask
Transformers
PyTorch
English
luke
named entity recognition
entity typing
relation classification
question answering
Instructions to use studio-ousia/luke-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/luke-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/luke-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/luke-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f404644bd151278f0829bbc69a1616817fb51eb90b5656cc741b6c11a7bd0318
- Size of remote file:
- 1.1 GB
- SHA256:
- da673eb0629dd709ae947555f1085c5a3285ec5ad0a676e64d3d6551463b781a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.