Instructions to use KevinChoi/dpr-context_encoder-klue-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevinChoi/dpr-context_encoder-klue-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KevinChoi/dpr-context_encoder-klue-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KevinChoi/dpr-context_encoder-klue-roberta-base") model = AutoModelForMaskedLM.from_pretrained("KevinChoi/dpr-context_encoder-klue-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KevinChoi/dpr-context_encoder-klue-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/KevinChoi/dpr-context_encoder-klue-roberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KevinChoi/dpr-context_encoder-klue-roberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KevinChoi/dpr-context_encoder-klue-roberta-base/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 626f546b503103116e52682793411e73546fbc630dac51edd004dc456d492d57
- Size of remote file:
- 443 MB
- SHA256:
- 8dfd393569165fe67615c815bbca89c23e10c584298c94275bff7faa4a2144e9
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