Instructions to use CAUKiel/JavaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAUKiel/JavaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAUKiel/JavaBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAUKiel/JavaBERT") model = AutoModelForMaskedLM.from_pretrained("CAUKiel/JavaBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9ffd69d6807ea7ba853230bde6edce95a3f1c98e5d27f685acb10869efacbb4
- Size of remote file:
- 438 MB
- SHA256:
- 83e40743e57e4760015f16b237a702819f099a2464fcd0de47cc467856efb1e5
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