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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44ab789f784cd0010fc2dd1288b29c9cbcbfde1fc50bab7ebd10b6346f5366e6
- Size of remote file:
- 433 MB
- SHA256:
- c445965e02bce9e912cb2c5271568a25c536a9a21a6f874e90353e9c9dde438c
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