Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-large") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bb38076db536955be63a2a6668ba0ea7837122238c63f2655f19f4aadead105
- Size of remote file:
- 3.79 GB
- SHA256:
- c66ee0d3871b464b0df2906f5fc853799009d56564816aef4a5afb431f45d521
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