Instructions to use binwang/RSE-BERT-base-USEB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/RSE-BERT-base-USEB with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForRSE tokenizer = AutoTokenizer.from_pretrained("binwang/RSE-BERT-base-USEB") model = BertForRSE.from_pretrained("binwang/RSE-BERT-base-USEB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from binwang/RSE-BERT-base-USEB: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/binwang/RSE-BERT-base-USEB/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://binwang/RSE-BERT-base-USEB/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/binwang/RSE-BERT-base-USEB/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0477ea6f957ce81b483907194c20a6b5bbdd95945a896e2bb9c26b1b36359484
- Size of remote file:
- 438 MB
- SHA256:
- 11764abe855c118a1d403ca3eb393295bbb1577e4fbe2e7a5991f52022509491
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