Instructions to use CAUKiel/JavaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAUKiel/JavaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAUKiel/JavaBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAUKiel/JavaBERT") model = AutoModelForMaskedLM.from_pretrained("CAUKiel/JavaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CAUKiel/JavaBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/CAUKiel/JavaBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://CAUKiel/JavaBERT@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CAUKiel/JavaBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- 143dd59e8c24250e977ae436847145ac55d1ea3296745e2a8d7b0646a5921ac9
- Size of remote file:
- 438 MB
- SHA256:
- 19c9b7b39d86c9b474a418719ee41aab152d67ced0824605dd924ef1cbcd9bc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.