Instructions to use jjzha/dajobbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjzha/dajobbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jjzha/dajobbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jjzha/dajobbert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("jjzha/dajobbert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jjzha/dajobbert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/jjzha/dajobbert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jjzha/dajobbert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jjzha/dajobbert-base-uncased/resolve/main/pytorch_model.bin
442 MB
- Xet hash:
- 406a487b4bd50be27861fab52d40baac73846b7f171f1506b3100c4e3c532c56
- Size of remote file:
- 442 MB
- SHA256:
- c7d58905883a589fec79ed1b6c339d688ba696adcfb4498032e5beea469fb6f6
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