Instructions to use Taekyoon/dpr_context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taekyoon/dpr_context with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Taekyoon/dpr_context")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Taekyoon/dpr_context") model = AutoModel.from_pretrained("Taekyoon/dpr_context", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Taekyoon/dpr_context: direct link, hf CLI and curl.
- Browser
- Download file 473 MB
-
https://huggingface.co/Taekyoon/dpr_context/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Taekyoon/dpr_context/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Taekyoon/dpr_context/resolve/main/pytorch_model.bin
473 MB
- Xet hash:
- 5105d942d0380c53ffb982b0b3580ac4062ee3052fb7ea85629a83cdfe453593
- Size of remote file:
- 473 MB
- SHA256:
- 535127911bba78341c98bd7e19d3142096f3861fd0697880f84eadf1b0b51d74
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