Instructions to use up201806461/bert-java-bfp_combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use up201806461/bert-java-bfp_combined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="up201806461/bert-java-bfp_combined")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("up201806461/bert-java-bfp_combined") model = AutoModelForMaskedLM.from_pretrained("up201806461/bert-java-bfp_combined") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5223194fb02c22f5f5c0711f5486bf451104321b07a1600a94b7dfc141fcbcf
- Size of remote file:
- 433 MB
- SHA256:
- abc93df776083d920a39f4806aa0dcde0d0bc7704c220e186f337ec299d48091
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