Instructions to use jinho8345/bros-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinho8345/bros-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinho8345/bros-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("jinho8345/bros-base-uncased") model = AutoModel.from_pretrained("jinho8345/bros-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jinho8345/bros-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jinho8345/bros-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jinho8345/bros-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jinho8345/bros-base-uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d5ffc6a596b1ecc50a9f7730a5c9dc1b5e27e1875fdc3cb3b86b17c34d4989b7
- Size of remote file:
- 438 MB
- SHA256:
- 7c31c742a5fe9719ee2125224f8ab369f8d29e2a82badc9d0663afc50551202c
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