Instructions to use binwang/RSE-BERT-base-Transfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/RSE-BERT-base-Transfer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForRSE tokenizer = AutoTokenizer.from_pretrained("binwang/RSE-BERT-base-Transfer") model = BertForRSE.from_pretrained("binwang/RSE-BERT-base-Transfer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from binwang/RSE-BERT-base-Transfer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/binwang/RSE-BERT-base-Transfer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://binwang/RSE-BERT-base-Transfer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/binwang/RSE-BERT-base-Transfer/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 875021faea7407a61dd62e140f8263bec2a7b75fe0f433eda01dd2bf78e7583c
- Size of remote file:
- 438 MB
- SHA256:
- 53e5296478a5055b50b38a145a796631d7364c0d853bf48681f7b2cc920d255b
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