Instructions to use dbernsohn/roberta-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbernsohn/roberta-java with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbernsohn/roberta-java")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-java") model = AutoModelForMaskedLM.from_pretrained("dbernsohn/roberta-java", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from dbernsohn/roberta-java: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/dbernsohn/roberta-java/resolve/refs%2Fpr%2F2/flax_model.msgpack
- Command line
-
hf download hf://dbernsohn/roberta-java@refs/pr/2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dbernsohn/roberta-java/resolve/refs%2Fpr%2F2/flax_model.msgpack
334 MB
- Xet hash:
- 939859b3f7976bf3467d3461cdeec4831d1f6e52380414f017428a746f994b90
- Size of remote file:
- 334 MB
- SHA256:
- 332eea67fdc9af6c193a9247dd9d0fa2d9ad4c3ce75efba1841d714f4fbf84d9
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