Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-base") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 400219650217caff7d214ff94f342d5291fdba2d8a47ce01a012c684a6fd80b4
- Size of remote file:
- 2.44 GB
- SHA256:
- 56588c40f41cff82e5174f1cd790bf59151f82c434407fd103d6ede9addeee5a
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