Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-large-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-large-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-large-lite")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-large-lite") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-large-lite") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f04efd3e6f15478aa04eeb521486a15e2dc75f75f76bfb52518dbcc7e6277de
- Size of remote file:
- 2.55 GB
- SHA256:
- 41e838fbf2ebc1ece85fb8bdbc6156d2d3075db5660d7eb8c4f497d5753fee4a
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