Instructions to use up201806461/bert-java-bfp_single with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use up201806461/bert-java-bfp_single with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="up201806461/bert-java-bfp_single")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("up201806461/bert-java-bfp_single") model = AutoModelForMaskedLM.from_pretrained("up201806461/bert-java-bfp_single") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dea6b272c1b34f5a5119b37ffa34a8fc5666f9121812124afec76801b06fc6fe
- Size of remote file:
- 3.71 kB
- SHA256:
- d906da2b782614f9ec32d9e9ff3e8979eb3adcb7d1c84a4386ed0866ff3464b5
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