Instructions to use reaprtripr/pretrained_java_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaprtripr/pretrained_java_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="reaprtripr/pretrained_java_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("reaprtripr/pretrained_java_bert") model = AutoModelForMaskedLM.from_pretrained("reaprtripr/pretrained_java_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from reaprtripr/pretrained_java_bert: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/reaprtripr/pretrained_java_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://reaprtripr/pretrained_java_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/reaprtripr/pretrained_java_bert/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 9ceab836e76b14b5a84c3c73d47ae554a8cbaa160d8ca364f908694bf023a562
- Size of remote file:
- 334 MB
- SHA256:
- b8fac69e50739f0a81124872f610dfac690170c61e4ec21d81a655bf5d572bee
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