Instructions to use harish/SemEval2022Task2SubTask2ABaseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/SemEval2022Task2SubTask2ABaseline with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harish/SemEval2022Task2SubTask2ABaseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 0_Transformer/pytorch_model.bin from harish/SemEval2022Task2SubTask2ABaseline: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/harish/SemEval2022Task2SubTask2ABaseline/resolve/main/0_Transformer/pytorch_model.bin
- Command line
-
hf download hf://harish/SemEval2022Task2SubTask2ABaseline/0_Transformer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harish/SemEval2022Task2SubTask2ABaseline/resolve/main/0_Transformer/pytorch_model.bin
712 MB
- Xet hash:
- 6ae3d054f0e5d247d8631b1a6b8a4264f345562eba4e694ee5643de3ee6bf415
- Size of remote file:
- 712 MB
- SHA256:
- 688f92a1aeb2dc73d7afcde0c1115b5daeb3f6b40015cea249fb3091b8722d00
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