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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb8715d265e99905aae8fbceb5a6137e0f781e24eba072aae3c28537601287b4
- Size of remote file:
- 3.64 kB
- SHA256:
- 1d4aa70d9de9bd50e5d64638fcaa2c3fcfa85f0904fa615c96ba24e49731fa6e
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